Member of Technical Staff - Machine Learning Training Systems

Join modal as a Member of Technical Staff specializing in Machine Learning Training Systems. In this role, you will contribute to the development and optimization of cutting-edge machine learning platforms that enhance the training and deployment of AI models. Collaborate with a talented team of engineers and data scientists to innovate and drive impactful solutions in the realm of artificial intelligence.

About UsAt Chakra Labs, we are dedicated to transforming human taste into a sophisticated intelligence framework. Our focus is on creating high-fidelity trajectories and environments that push the boundaries of AI research, collaborating with leading laboratories in the field.Why Join Us?Engage with cutting-edge technologies spanning data, AI, and infrastructure.Receive mentorship from a highly qualified and experienced team.Enjoy a competitive salary package, comprehensive benefits, and a daily meal stipend of $30, alongside an attractive equity offering.Considerations Before ApplyingThis is our passion. While we value fun, we are dedicated to hard work. If you prefer a standard 9-5 role, this may not be the right environment for you.Uncertainty is part of the journey. The landscapes of data and AI are ever-evolving. While we have a defined mission, those uncomfortable with ambiguity may find this role challenging.Your ProfileAdvanced Knowledge: You hold a Master's or PhD in machine learning, with a solid grasp of linear algebra and stochastic gradient descent, capable of deriving linear regression manually.Post-Training & Environments: You’re adept at executing SFT on distributed GPU clusters utilizing FSDP. You possess a keen interest in enhancing existing models with high-quality data and can conceptualize new environments and online reinforcement learning (GRPO, PPO).Proficient in Python: Familiarity with FastAPI and PyTorch is essential, including REST APIs and backend development, along with the ability to abstract high-level concepts in Torch using lower-level primitives.Experience: We seek approximately 3-5 years of relevant experience, though we value skills and alignment with our expectations over strict qualifications.

About UsAt Cohere, we're on a mission to harness and scale intelligence for the betterment of humanity. We specialize in training and deploying cutting-edge models for developers and enterprises, enabling them to create transformative AI experiences such as content generation, semantic search, retrieval-augmented generation (RAG), and intelligent agents. We are committed to fostering the widespread adoption of AI technologies.Our passion for excellence drives us to continuously enhance our models and the value they deliver to our clients. We thrive in a fast-paced environment where hard work and innovation are paramount to achieving the best outcomes for our users.Cohere is made up of a diverse team of researchers, engineers, designers, and more, all of whom are leaders in their fields. We believe that a variety of perspectives is essential to creating outstanding products.Join us in our quest to shape the future!Role OverviewOur rapidly expanding team of researchers and engineers is dedicated to building robust machine learning systems and enhancing the efficiency of large language model (LLM) inference. We focus on developing innovative techniques that improve production execution of models, resulting in reduced latency, increased throughput, and consistent performance across various workloads.As a member of this team, you will engage with the inference stack to enhance key performance metrics by analyzing model execution, identifying performance bottlenecks, and crafting pioneering optimizations. You will work closely with both modeling and systems teams to test, measure, and implement enhancements that significantly improve inference speed. As the team progresses, you will have the chance to deepen your knowledge in advanced performance techniques, including GPU/CUDA optimizations, kernel-level enhancements, and execution strategies for mixture of experts (MoE) and large-scale architectures.Note: Cohere has offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul, and London. We embrace a remote-friendly approach, strategically distributing teams based on interests, expertise, and time zones to enhance collaboration and flexibility. The Model Efficiency team is primarily based in the EST and PST time zones.Who You Are5+ years of experience coding high-performance, production-level softwareProficient in C++ or Python (experience in Rust/Go is also a plus)Strong understanding of machine learning concepts and frameworksExperience in optimizing ML systems for production environmentsExcellent problem-solving skills and ability to collaborate effectively in a team setting

Why Choose Charlie Health?Across the nation, countless individuals are facing mental health challenges, substance use disorders, and eating disorders. Unfortunately, many encounter obstacles to obtaining the care they need, such as limited local options and lengthy wait times. Behavioral healthcare often fails to provide the personalized support that individuals require, leaving them feeling overlooked and unsupported.At Charlie Health, our mission is to transform this landscape. We strive to connect individuals to crucial behavioral health treatment through personalized, virtual care that fosters connections between clients and clinicians, care teams, loved ones, and their supporting communities. By prioritizing those with complex needs, we are enhancing access to significant care and achieving improved outcomes—all from the comfort of home.As a rapidly expanding organization, we are reaching more communities daily, and we are assembling a team dedicated to redefining behavioral health treatment. If you are eager to leverage your skills to create meaningful change and help individuals access the care they deserve, we invite you to join us.About the RoleCharlie Health is a leader in high-acuity virtual behavioral care, having provided life-saving treatment to over 100,000 clients across the nation. Our Matching and Outcomes team is responsible for developing products that assess and enhance clinical outcomes, including systems for provider recommendations and clinical decision support tools, ensuring effective treatment. You will design predictive models and workflows to connect patients with the most suitable therapists and peers, enabling clinicians to customize care to meet each client's unique needs. The systems you develop will empower clinicians to condense and synthesize insights from our clients and providers into actionable guidance at every step. If you are passionate about utilizing data-driven insights to enhance outcomes, this team is for you.

About SciemoSciemo is revolutionizing the consumer goods industry through advanced AI technology that empowers businesses to make quicker, smarter, and more human decisions throughout the Integrated Business Planning (IBP) process. Our platform turns chaotic, fragmented data into actionable insights, aiding decision-makers in real-time by simplifying complexity.OverviewAs a pioneering startup, Sciemo specializes in AI solutions tailored for consumer brands. We harness machine learning, generative AI, agent-based systems, and graph technologies to deliver insights in seconds and tangible business outcomes in minutes.Your RoleWe are on the lookout for a Founding Member of Technical Staff, who will serve as both a Data Scientist and a Machine Learning Engineer. In this critical position, you will be instrumental in the design, development, and deployment of the intelligence that powers our AI products. You’ll engage in a wide range of applied AI efforts, from data science and machine learning to large-scale production engineering. This dual role demands not only advanced model development expertise but also the engineering acumen to implement and sustain robust, scalable systems.You will work closely with data engineers, product leads, backend engineers, and customer-facing teams to ensure our AI systems provide measurable value in practical scenarios. As one of our initial technical team members, you will help shape our AI strategy, establish technical standards, and set best practices for scalable applied AI.Key ResponsibilitiesDevelop and Deploy AI Systems:Architect, build, and deploy ML/GenAI products on cloud infrastructure (AWS or comparable).Design and implement comprehensive AI workflows: data ingestion, feature engineering, modeling, evaluation, and deployment.Establish automated pipelines for continuous learning, model promotion, and performance monitoring.System Architecture & Reliability:Lead the design of ML orchestration frameworks (such as Airflow, Kedro, ZenML, Flyte) to guarantee reproducibility and system reliability.

Join our talented team at espresso as a Staff Machine Learning Engineer, where you will train advanced models aimed at significantly optimizing our users' compute costs. Leverage your expertise to innovate and drive impactful solutions that enhance efficiency and performance.

About UsAt Cohere, our vision is to enhance intelligence to better serve humanity. We specialize in training and deploying cutting-edge models for developers and enterprises, enabling them to create extraordinary AI-driven experiences such as content generation, semantic search, retrieval-augmented generation (RAG), and intelligent agents. Our work is pivotal in fostering the broad acceptance of AI technologies.We are deeply passionate about our creations. Each team member plays a vital role in enhancing the capabilities of our models and delivering exceptional value to our clients. We prioritize hard work and agility to ensure we meet our customers' needs effectively.Cohere comprises a diverse team of researchers, engineers, designers, and other professionals who are dedicated to their craft. Our collective expertise is among the best globally, and we believe that a variety of perspectives is essential for developing outstanding products.Join us to be a part of this transformative journey!About This PositionWe are seeking a seasoned machine learning researcher or engineer to help us explore and advance the realm of agentic large language model (LLM) systems. In this role, you will spearhead the exploration and development of innovative agentic techniques, and you will have the chance to create models that drive our agentic solutions. Agentic LLM systems are increasingly being adopted by enterprises through Cohere’s North platform. Your contributions will focus on developing novel strategies for training models that enhance agent capabilities, including reasoning, tool usage, and memory management. This involves creating data generation methods for post-training enhancements (Supervised Fine-Tuning and Reinforcement Learning). The advancements made in model development will have direct implications for North and other Cohere offerings, presenting an exciting opportunity where foundational model innovations translate into significant product improvements.Please note: Our offices are located in London, Toronto, San Francisco, and New York, and we embrace a remote-friendly work culture! We prefer candidates based in Eastern Time, but remote or hybrid arrangements are welcome.Your Responsibilities as a Member of Technical Staff for Agents Modeling:Design and develop innovative agentic solutionsEnhance state-of-the-art performance on challenging agentic tasksInvestigate next-generation online learning-from-experience techniques for self-improvementCollaborate with cross-functional teams (Reasoning, Post-training, Pre-training, etc.) to boost the performance of agentic systemsCollaborate with an exceptional team of researchers and engineers...

At Chakra Labs, we are dedicated to creating innovative environments for AI agents, focusing on systems that enhance and measure their productive capabilities.Key ResponsibilitiesAgent Orchestration at Scale: Manage hundreds of agent executions simultaneously, each with its unique stateful environment. You'll oversee the dispatch layer, including SQS, concurrency management, and failure recovery.Environment and Task Design: Develop realistic environments and challenging scenarios that push agents to their limits. Your role will involve constructing new evaluations and designing meaningful tasks that assess critical performance metrics.Exploring New Frontiers: Stay on the cutting edge of agent evaluation by supporting new environment modalities and integrating external orchestration frameworks.Observability: Implement Prometheus and OpenTelemetry across services, create Grafana dashboards, and manage structured logging.QualificationsContainer Orchestration: Proficient in managing Kubernetes or similar technologies in production environments, including auto-scaling, pod lifecycle management, persistent storage, and networking.Distributed Systems: Experience in building or maintaining message-driven architectures (e.g., SQS, Kafka). You understand how to manage job flows, implement retries without duplication, and handle failures gracefully.LLM Infrastructure: Familiarity with running LLM workloads at scale, including token instrumentation, rate limit management, prompt caching, and multi-provider routing.Experience: Approximately 3-5 years of relevant experience, though we are open to candidates who possess the required skills and knowledge.Why Join Us?Unique Focus: While this role is centered on infrastructure, the workload involves AI agents—monitoring model behaviors alongside pod health, and analyzing token throughput alongside network performance.Engaging Clients: Collaborate directly with AI researchers and labs, contributing to the advancement of agent capabilities and building the foundational infrastructure they rely on.Dynamic Team Environment: Take ownership of entire systems rather than just tasks, with opportunities to impact various projects and initiatives.

At Confido, we are revolutionizing the AI infrastructure for consumer packaged goods (CPG) brands, streamlining everything from deductions to production planning. Our platform integrates cash applications, deductions, disputes, trade promotion management, forecasting, demand planning, and analytics into a cohesive system. The outcome is significant time savings, enhanced decision-making capabilities, and accelerated growth potential.We proudly serve over 200 brands, managing more than $20 billion in revenue, with clients like OLIPOP, Simple Mills, Dr. Squatch, and Tropicana.With our recent successful $15 million Series A funding round led by Footwork Ventures and Y Combinator, we are poised for rapid expansion.As a Staff AI / ML Engineer, you will play a crucial role in developing and implementing the core AI systems that power our platform, including LLM-driven document understanding and predictive models that assist brands in making informed decisions.This foundational position will involve direct collaboration with our CEO and CTO to define our long-term AI strategy and architecture.

Supported by top-tier Silicon Valley investors, Peregrine Technologies is dedicated to assisting public safety organizations, state and local governments, federal agencies, and private-sector institutions in tackling societal challenges with unmatched speed and precision. Our AI-driven platform transforms fragmented data into actionable insights—quickly presenting mission-critical information that empowers informed decisions and enhances outcomes at every level. Currently, we serve hundreds of clients across over 30 states and two countries, impacting more than 125 million individuals, and we are poised to broaden our reach into enterprise sectors and international markets.TeamAt Peregrine, we hold a strong belief that empathy is key to our solutions. Understanding user interactions with our products is crucial, and our engineers work closely with the team on-site to explore the diverse use cases that our platform addresses.We prioritize both accountability and teamwork; you will take full ownership of significant features while collaborating with fellow engineers to ensure their successful completion. We value humility and empathy as vital components in crafting effective solutions—you'll engage directly with our deployment team and users as we refine our offerings to meet their needs. Creativity and perseverance are essential in realizing our vision.RoleAs a key member of our engineering team, you will play a pivotal role in delivering unique value to our clients. Your mission will be to create powerful, intuitive user experiences powered by generative AI, allowing users to engage with our platform in innovative ways. Your contributions will help develop safe, impactful AI-driven features that enable customers to solve real-world challenges more swiftly and effortlessly.Your responsibilities will encompass a variety of complex tasks, including scaling our platform to manage terabytes of data from multiple sources, efficiently querying and notifying users in real-time, and optimizing search algorithms for rapid result delivery.Our technology stack is continuously evolving, built on a backend foundation of Python, Django, Celery, Airflow, and Kafka; a frontend developed with React, Redux, and Mapbox; data storage solutions including PostgreSQL and Elasticsearch; machine learning models hosted on Bedrock and Sagemaker; and supported by AWS, Pulumi, Terraform, and Kubernetes for our infrastructure.About YouA strong desire and initiative to take ownership of large components of our projects.Experience with machine learning frameworks and concepts.Proficient in Python and familiar with web development frameworks.Ability to work collaboratively in a team environment.A passion for developing innovative solutions to complex problems.

Join Deeptune’s Innovative TeamAt Deeptune, we are pioneering the development of high-fidelity training environments for AI agents, enabling them to learn real-world tasks through advanced reinforcement learning techniques. Collaborating with leading AI laboratories, we are at the forefront of training the next generation of agentic models, contributing to significant advancements in computer operations, code generation, and complex task execution.Our close-knit team of around 20 talented engineers and operators, hailing from prestigious organizations such as Anthropic, Scale AI, Palantir, Hebbia, Glean, and Retool, is based in New York City. Recently, we successfully secured $43 million in Series A funding, led by Andreessen Horowitz, with contributions from 776, Abstract Ventures, and Inspired Capital.If you are eager to tackle some of the toughest challenges at the intersection of reinforcement learning and practical AI implementation, we would love to connect with you!Learn more: Fortune Exclusive on Our Series A | a16z: Why We’re Investing in Deeptune

THE ROLE:As a pivotal member of our team, you will transform the interaction dynamics between life sciences organizations and agents. Your creativity will help craft innovative experiences that seamlessly integrate into daily workflows. You will engage with cutting-edge models and develop interfaces that empower users to harness their capabilities effectively.WHAT WE ARE LOOKING FOR:Analytical Thinkers: You possess a comprehensive approach to problem-solving, effectively balancing business and technical considerations.Technology Agnostic: You appreciate technology for its practicalities and are open to learning and adapting to new tools and ideas.Self-Motivated and Organized: You thrive in situations of ambiguity and take initiative to create meaningful impact.Product Ownership: You take responsibility for the quality and usability of the products you create, demonstrating pride in your work.Determined: You are relentless in pursuing goals, overcoming obstacles with perseverance.YOUR KEY RESPONSIBILITIES:Manage retrieval of vast document scales with diverse formats (graphs, tables, diagrams, etc.)Develop organizational interfaces to enhance collaboration with AI, integrating into everyday workflowsDesign and implement strategic planning tools involving human oversightWork directly with enterprise clients to create product experiences that automate routine tasksAssess large language models across various life science tasksEnsure government-level data security for sensitive informationConduct internet-scale data collectionDESIRABLE QUALIFICATIONS:Experience in a rapidly growing early-stage startup environment.Background in building agents or fine-tuning language models for complex tasks.Experience in developing large-scale content indexing systems.Familiarity with API development and integrations.

About Us:At Modal, we empower AI teams with robust infrastructure solutions. Our platform offers instant access to GPUs, rapid container startups, and integrated storage, enabling seamless model training, batch processing, and low-latency inference. Leading companies such as Suno, Lovable, and Substack trust Modal to transition from prototype to production effortlessly, without the complexities of infrastructure management.As a rapidly expanding organization based in New York, San Francisco, and Stockholm, we have achieved a remarkable nine-figure ARR and recently secured a Series B funding round at a valuation of $1.1 billion. Our extensive customer base, which includes industry leaders like Lovable, Scale AI, Substack, and Suno, depends on us for their production AI workloads.Joining Modal means becoming part of one of the fastest-growing AI infrastructure companies at a pivotal stage, with numerous opportunities for personal and professional growth. Our team is composed of the creators of renowned open-source projects (e.g., Seaborn, Luigi), academic scholars, international competition medalists, and seasoned engineering and product leaders with extensive experience.Your Role:We are seeking talented engineers who have a proven track record in optimizing machine learning systems for performance at scale. If you are passionate about contributing to open-source initiatives and enhancing Modal’s container runtime to improve language and diffusion models for higher throughput and reduced latency, we want to hear from you!Key Qualifications:A minimum of 5 years of experience in crafting high-quality, high-performance code.Proficient in working with PyTorch, high-level ML frameworks, and inference engines (e.g., vLLM or TensorRT).Strong understanding of Nvidia GPU architecture and CUDA programming.Hands-on experience in machine learning performance engineering, including optimizing GPU performance through techniques such as debugging SM occupancy, rewriting algorithms for compute efficiency, and minimizing host overhead.Familiarity with low-level operating system principles (Linux kernel, file systems, containers) is a plus.Willingness to work in-person at our offices in New York, San Francisco, or Stockholm.

About BasisBasis is a pioneering nonprofit organization dedicated to applied AI research, driven by two key objectives.The first objective is to comprehend and develop intelligence. This encompasses establishing the mathematical foundations of reasoning, learning, decision-making, understanding, and explanation, while also creating software that embodies these principles.The second objective is to enhance society’s capacity to tackle complex challenges. This involves broadening the scope, scale, and complexity of problems we can address today, and crucially, accelerating our capacity to solve future problems.To fulfill these missions, we are constructing an innovative technological infrastructure inspired by human reasoning, along with a collaborative organization that prioritizes human values.About the RoleAs an ML Systems Engineer at Basis, you will ensure that our training and evaluation infrastructure is fast, reliable, and scalable. You will manage the entire stack, from distributed training frameworks to cloud administration, enabling researchers to rapidly iterate on complex models while efficiently managing computational resources.We are seeking engineers who possess a profound understanding of ML systems paired with operational excellence. The ideal candidate will have experience in distributed training at scale, expertise in debugging numerical instabilities, and the ability to manage cloud infrastructure that seamlessly transitions from experimentation to production. You will be the steward of training stability, an optimizer of computational costs, and a facilitator of reproducible research.This position encompasses both traditional ML engineering and cloud/DevOps responsibilities. You will oversee GPU clusters, optimize cloud expenditures, ensure security and compliance, and build the infrastructure that allows researchers to focus on algorithms rather than operations.We are looking for individuals who are committed to developing robust ML infrastructure, maintaining a culture of documentation for issues and solutions, and prioritizing operational excellence as a core value.

Location: New York, NYStart date: ASAPLanguages: English (required)About the RoleJoin Pragmatike as we partner with a dynamic AI cybersecurity firm, recognized by top-tier investors and visionary AI leaders. This innovative company is dedicated to establishing robust security measures for the AI era, safeguarding enterprises from advanced threats, including deepfakes, smishing, and synthetic voice attacks.Following a successful Series B funding round with industry-leading AI and venture partners, the organization is poised for substantial growth. Trusted by major financial institutions, technology giants, and healthcare providers, they are rapidly scaling to meet the demands of a $200 billion market opportunity.We are seeking a Staff Machine Learning Engineer to architect and enhance the company’s machine learning capabilities from the ground up. This role is pivotal to our product vision and focuses on strategic ownership, infrastructure development, and comprehensive execution of machine learning across the organization.Currently, there is no dedicated machine learning infrastructure or team. You will lay the groundwork: identifying where machine learning can add product value, designing end-to-end production systems, and setting the technical roadmap for the evolution of machine learning within the company.This position is highly impactful and offers significant autonomy, ideal for candidates experienced in building production ML systems and ready to develop a robust ML function from inception to full-scale operation.What You'll DoDefine the company's machine learning strategy: identifying application areas across products, determining necessary infrastructure, and making informed build vs. buy decisions.Design and construct production ML systems from scratch, including data pipelines, model training workflows, evaluation frameworks, and inference serving.Establish a rigorous evaluation methodology to assess model quality, identify regressions, and support iterative improvements based on data.Oversee data strategy: define necessary data, structure labeling, establish feedback loops, and ensure models evolve continuously.Collaborate closely with product and backend engineers to integrate machine learning into customer-facing systems.Contribute production-quality code to the existing codebase and participate in architectural decision-making.In time, assist in recruiting, mentoring, and leading the ML team as the function grows.

Our VisionAt Reflection AI, we are dedicated to the mission of creating open superintelligence and ensuring it is accessible to everyone. Our team is committed to developing open weight models that cater to individuals, agents, enterprises, and even nation-states. Our diverse group of AI researchers and innovators hail from esteemed organizations such as DeepMind, OpenAI, Google Brain, Meta, Character.AI, and Anthropic.Foundational ObjectivesVision:We aim to establish and manage a company-wide foundational platform that enhances every team’s productivity by delivering dependable, scalable developer infrastructure, site reliability engineering capabilities, and high-throughput data ingestion tools—empowering Reflection AI to progress swiftly as we expand.Team ResponsibilitiesOur team is responsible for constructing and managing essential shared services that fuel our research, training, and production environments. These systems form the backbone that supports various teams in model development, deployment, and evaluation, integrating data, compute, and workflow management while facilitating rapid experimentation and robust production systems.Design and manage shared services that multiple teams utilize across research and production workflows.Establish and maintain reliability targets through Service Level Indicators (SLIs), Service Level Objectives (SLOs), and effective on-call practices.Ensure operational readiness through comprehensive runbooks, incident playbooks, and capacity planning.Guarantee correctness and performance under load, addressing issues like consistency, tail latency, and failure modes.Create APIs, SDKs, and internal platforms that support high-velocity experimentation and iteration.Minimize operational burden through enhanced tooling, standardization, and scalable platform patterns across teams.Technologies You'll Engage WithContainer Abstractions: Containers-as-a-Service, Kubernetes abstraction layers, container orchestration, reproducible environments, multi-tenant isolation.Distributed Systems Architecture: Sharding, replication, coordination services, high-concurrency systems, concurrency control.Service Development Stack: gRPC, Protobuf, Go, Rust, C++.Reliability & Performance: Idempotency, retries, backpressure, SLI/SLO design, tail latency optimization, service reliability engineering.Your ProfileWe are looking for a talented individual with a solid background in distributed systems and a passion for building scalable solutions.

About AdaptiveAdaptive Security stands at the forefront of AI cybersecurity, recognized as NVIDIA and OpenAI’s exclusive investment in this domain. Our mission is to thwart AI-driven cyberattacks. In December 2025, we proudly announced an $81M Series B funding round led by NVIDIA and Bain Capital Ventures, joined by esteemed investors including Capital One Ventures, Citi Ventures, and ongoing support from Andreessen Horowitz (a16z), the OpenAI Startup Fund, and Abstract Ventures. This round signifies NVIDIA’s inaugural foray into AI cybersecurity investments.Founded by seasoned entrepreneurs Brian Long and Andrew Jones, who have a proven track record of building and scaling industry-defining companies, Adaptive is set to shape the security landscape for the AI era. Brian and Andrew previously co-founded Attentive, which achieved over $500M in annual revenue and a valuation exceeding $10B, and TapCommerce, acquired by Twitter. Their wealth of experience in creating high-growth, product-focused businesses is instrumental as Adaptive secures enterprises against ever-evolving threats.With a growing clientele that includes leading banks, technology firms, and healthcare organizations, Adaptive is dedicated to safeguarding teams from emerging threats like deepfakes, smishing, and AI-generated voice scams. As we experience rapid enterprise adoption and explore a market exceeding $200B, we are just scratching the surface of our potential.Role OverviewWe are on the lookout for a Staff Machine Learning Engineer to spearhead the development of Adaptive's ML capabilities. As an AI cybersecurity firm, our products leverage LLMs and ML models to identify, classify, and respond to threats in real time. The role of ML is pivotal to the future of our offerings, and we seek an individual who can define our strategy, establish the infrastructure, and execute hands-on initiatives.Currently, we lack dedicated ML infrastructure or a team; you will have the unique opportunity to build this from the ground up. You will set the technical direction for ML applications across the organization, establish the necessary infrastructure, and engage in hands-on development.Key ResponsibilitiesCraft and define Adaptive's ML strategy: identify optimal applications of ML across our product suite, determine required infrastructure, and guide build vs. buy decisions.Design and implement end-to-end production ML systems, including data pipelines, model training, evaluation frameworks, and inference serving.Establish a robust evaluation methodology to measure model quality, identify regressions, and facilitate data-driven decisions regarding model modifications.Take ownership of data strategy: ensure the acquisition of necessary data in the required formats, define labeling processes, and establish feedback loops to enhance model performance over time.Collaborate with product engineers to integrate ML capabilities seamlessly into our products.

At Garner Health, we are dedicated to revolutionizing the healthcare economy by making high-quality and affordable care accessible to all.We are fundamentally reimagining the healthcare landscape in the U.S. by collaborating with employers to redesign healthcare benefits using transparent incentives and powerful, data-driven insights. Our innovative approach directs employees toward higher-quality, lower-cost care, fostering a healthcare system that benefits everyone. This means improved health outcomes for patients, more effective spending of healthcare dollars for employers, and rewarding physicians for providing exceptional care rather than performing unnecessary procedures.Garner is recognized as one of the fastest-growing healthcare technology companies nationally. Our products are trusted by the most sophisticated employers and providers in the industry, and we are building a team of talented, mission-driven individuals who are eager to make a significant impact on healthcare at scale.About the Role:We are on the lookout for an outstanding Staff Machine Learning Engineer to join our dynamic team. At Garner, our Engineering team addresses complex technical challenges that have a meaningful impact on our mission. Our systems analyze billions of medical records to evaluate healthcare providers nationwide. We operate on AWS with Kubernetes and consistently integrate new technologies as our system load grows exponentially each year. We encourage our engineers to advocate for cutting-edge industry practices as we wholeheartedly incorporate AI into every aspect of our operations and standards. We value teammates who take pride in delivering exceptional work and nurturing a robust team culture.Where You Will Work:This position is based in our New York City office located in the Financial District. Candidates must be available to work in the office three days a week: Tuesday, Wednesday, and Thursday.What You Will Do:Lead technical strategy and execution at the team level over multi-quarter time frames.Utilize AI to build, optimize, and maintain machine learning, data, and software systems.Elevate the technical capabilities of engineers throughout the organization.Collaborate across disciplines to deepen domain understanding and iterate on projects.Ensure user privacy and security through adherence to best practices.

Vibecode is on a mission to make artificial intelligence accessible to 100 million more people. The team is dedicated to using advanced technology to inspire and educate, with a focus on the possibilities of AI-assisted coding. Role overview This New York City-based role centers on exploring how AI tools can influence the future of coding and design. The work involves building scalable design systems and paying close attention to visual details that set products apart. What you will do Use AI coding tools such as Cursor, Claude Code, and Codex to build and refine design systems. Work with fine design details, including subtle color distinctions (for example, #1f1f1f vs. #1d1d1d). Apply a strong sense of aesthetics and design judgment throughout your projects. Support the design process with Figma as needed. Who thrives here People who are deeply curious about technology and often pursue projects late into the night. Those with a strong work ethic who enjoy collaborating with a dedicated team and tackling challenging problems. Individuals who stay humble, take on any task, and value input from others.

Location: New York, NYStart date: ASAPLanguages: English (required)About the RolePragmatike is partnering with a rapidly expanding AI cybersecurity firm, supported by premier investors and leading AI experts. This innovative company is constructing the security framework for the AI age, safeguarding organizations against AI-driven threats like deepfakes, smishing, and synthetic voice attacks.After securing significant Series B funding from top-tier AI and venture capital partners, the firm is poised for an essential growth stage. With trust from major banking institutions, tech companies, and healthcare entities, they are accelerating their scaling efforts to fulfill enterprise demand in a market exceeding $200 billion.We seek a Director of Machine Learning to architect and develop the company’s machine learning capabilities from inception. This role is pivotal to our product vision, emphasizing strategic ownership, infrastructure establishment, and the execution of machine learning throughout the organization.Currently, there is no dedicated ML infrastructure or team in place. You will lay the groundwork: identifying where ML creates product value, designing comprehensive production systems, and guiding the technical evolution of ML across the company.This is a high-impact, autonomous position ideal for someone experienced in building ML systems in operational environments and ready to scale an ML function from the ground up.What You Will DoDefine the ML strategy for the company: determining application areas for ML across products, required infrastructure, and the approach for build versus buy decisions.Design and develop end-to-end production ML systems, including data pipelines, model training workflows, assessment frameworks, and inference serving.Establish a robust evaluation methodology to assess model quality, identify regressions, and facilitate data-driven improvements.Oversee the data strategy: identifying necessary data, establishing labeling processes, structuring feedback loops, and ensuring continuous model enhancement.Collaborate closely with product and backend engineering teams to seamlessly integrate ML into customer-facing systems.Produce high-quality code within the existing codebase and contribute to architectural decisions.As the function expands, support recruitment, mentorship, and leadership of the ML team.