Post-Training Technical Staff Member at Liquid AI | San Francisco

About Liquid AIFounded as a spin-off from MIT CSAIL, Liquid AI specializes in the development of versatile artificial intelligence systems optimized for performance across various deployment environments, ranging from data center accelerators to on-device hardware. Our focus on low latency, minimal memory consumption, privacy, and reliability allows us to partner effectively with enterprises in sectors such as consumer electronics, automotive, life sciences, and financial services. As we experience rapid growth, we are eager to welcome talented individuals who can contribute to our mission.The OpportunityThis unique position places you at the forefront of advanced foundation models and their practical applications. You will oversee post-training projects from start to finish for some of the world’s leading enterprises, while also playing a vital role in the ongoing development of Liquid’s core models.In this role, you will not have to choose between impactful customer work and foundational development; instead, you will enjoy deep involvement in both. You will have significant influence over how models are adapted, assessed, and deployed, directly contributing to the enhancement of Liquid’s post-training capabilities.If you are passionate about data integrity, evaluation processes, and ensuring that models perform effectively in real-world scenarios, this is your chance to redefine the standards of applied AI at a foundation-model company.What We're Looking ForWe seek an individual who:Takes ownership: You will lead post-training initiatives from customer requirements to delivery and evaluation.Thinks end-to-end: You will connect the dots across data generation, training, alignment, and evaluation as a cohesive system.Is pragmatic: You prioritize model quality and customer satisfaction over theoretical publications.Communicates clearly: You can interpret customer needs and effectively communicate with internal technical teams, providing constructive feedback when necessary.The WorkServe as the technical lead for post-training engagements with enterprise clients.Translate client requirements into actionable post-training specifications and workflows.Design and implement data generation, filtering, and quality assessment methodologies.Conduct supervised fine-tuning, preference alignment, and reinforcement learning processes.Create task-specific evaluations, analyze outcomes, and integrate insights back into core post-training workflows.

Our MissionAt Reflection AI, our goal is to create open superintelligence and ensure its accessibility for everyone.We are pioneering open weight models for various users, including individuals, enterprises, and even nation-states. Our talented team comprises AI researchers and industry veterans from leading organizations such as DeepMind, OpenAI, Google Brain, Meta, Character.AI, and Anthropic.Role OverviewDevelop systems that convert robust pre-trained models into aligned and versatile agents.Lead research and engineering efforts to advance post-training practices, focusing on data curation and large-scale optimization.Create data generation frameworks, reward models, reinforcement learning algorithms, and techniques for inference-time scaling.Collaborate with both pre-training and post-training teams to achieve significant enhancements in model capabilities.Help refine our understanding of how large models learn to reason, follow instructions, and evolve through reinforcement learning.Your ProfileSolid grasp of machine learning principles with hands-on experience in large-scale LLM training.Proficient engineering skills, with the ability to navigate intricate ML codebases and distributed systems.Experience in enhancing model performance through data, reward modeling, or reinforcement learning techniques.Track record of leading ambitious research or engineering projects resulting in measurable improvements.Thrives in a dynamic, high-agency startup atmosphere; oriented towards action and clarity in execution.Ability to work seamlessly across research and infrastructure boundaries.Excellent communication skills and a collaborative mindset.Driven by a passion for pushing the boundaries of intelligence.What We Provide:At Reflection AI, we believe that to truly build open superintelligence, it must be rooted in a strong foundation. By joining us, you will contribute to building from the ground up within a compact, highly skilled team. Together, we will shape the future of our company and the landscape of open foundational models.We aim for you to accomplish the most impactful work of your career, with the assurance that you and your loved ones are well-supported.

Join Our TeamAt Liquid AI, we are not just creating AI models; we are revolutionizing the very fabric of intelligence. Originating from MIT, our objective is to develop efficient AI systems across all scales. Our Liquid Foundation Models (LFMs) excel in environments where others falter—on-device, at the edge, and under real-time constraints. We are not simply refining existing concepts; we are pioneering the future of AI.We recognize that exceptional talent drives remarkable technology. The Liquid team is a collective of elite engineers, researchers, and innovators dedicated to crafting the next generation of AI solutions. Whether you are designing model architectures, enhancing our development platforms, or facilitating enterprise integrations, your contributions will significantly influence the evolution of intelligent systems.While San Francisco and Boston are preferred locations, we welcome applicants from other regions within the United States.

Our MissionAt Reflection AI, our goal is to develop open superintelligence and make it universally accessible.We are pioneering open weight models tailored for individuals, agents, enterprises, and even entire nations. Our diverse team comprises talented AI researchers and industry veterans from prestigious organizations such as DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic, and many more.Role OverviewConstruct and enhance distributed training systems that drive the pre-training of cutting-edge models.Collaborate with research teams to design and execute extensive training runs for foundational models.Create infrastructure that facilitates efficient training across thousands of GPUs leveraging contemporary distributed training frameworks.Enhance training throughput, stability, and efficiency for extensive model training tasks.Work closely with pre-training researchers to convert experimental concepts into scalable, production-ready training systems.Boost performance of distributed training tasks through optimization of communication, memory management, and GPU utilization.Develop and maintain training pipelines that accommodate large-scale datasets, checkpointing, and iterative experiments.Identify and resolve performance bottlenecks within distributed training systems, including model parallelism, GPU communication, and training runtime environments.Contribute to the creation of systems that promote swift experimentation and iteration on novel training methods.

Join our innovative team at liquid-ai as a Member of the Technical Staff specializing in audio applications. As a post-training role, you will have the opportunity to apply your knowledge in cutting-edge audio technologies, contributing to the development of advanced machine learning solutions.This position is ideal for individuals who are eager to work in a collaborative environment and are passionate about audio technology and its applications in artificial intelligence.

Join Liquid AI as a Technical Staff Member specializing in Applied Vision. In this dynamic role, you will leverage cutting-edge technology to develop innovative solutions and enhance our product offerings. This position is ideal for recent graduates with a passion for technology and a desire to make a meaningful impact in the field of artificial intelligence.

At Catalog, we are pioneering the commerce infrastructure for AI—creating the essential framework that enables digital agents to not only explore the web but also comprehend, analyze, and engage with products. Our innovations drive the future of AI-driven shopping experiences, fundamentally transforming how consumers discover and purchase items online.Role OverviewAs a Technical Staff Member, you will be instrumental in developing core systems, shaping our engineering culture, and transitioning our vision from prototype to a robust platform. This role requires full-stack expertise and a commitment to owning and resolving challenges from start to finish.Who You AreYou have experience creating beloved and trusted products from the ground up.You combine technical proficiency with a keen product sense and data-driven intuition.You are well-versed in AI technologies.You prioritize speed, write clean code, and ensure thorough instrumentation.You seek a high level of ownership within a small, talent-rich team based in San Francisco.Challenges You Will TackleDevelop and deploy agentic-search APIs that deliver structured and real-time product data in milliseconds.Build checkout systems enabling agents to conduct transactions with any merchant.Create an embeddings and retrieval layer that optimizes recall, precision, and cost efficiency.Establish a product graph and ranking pipeline that adapts based on actual user outcomes.Preferred QualificationsProven experience shipping data-centric products in a live environment.Experience with recommendation systems or information retrieval methodologies.Familiarity with API development, search indexing, and data pipeline construction.Our Work CultureWe operate with a small, high-trust, and highly motivated team, fostering an environment of in-person collaboration in North Beach, San Francisco. Our process involves debate, decision-making, and execution.If your profile aligns with our needs, we will contact you to arrange 2-3 brief technical interviews, followed by an onsite meeting in our office where you will collaborate on a small project, exchange ideas, and meet the team.

About Liquid AIOriginating from MIT CSAIL, Liquid AI specializes in the development of general-purpose AI systems designed to operate seamlessly across various platforms, including data center accelerators and on-device hardware. Our focus is on delivering low latency, efficient memory usage, privacy, and reliability. We collaborate with organizations in diverse sectors such as consumer electronics, automotive, life sciences, and financial services. As we experience rapid growth, we seek outstanding talent to join our mission.The OpportunityThe Training Infrastructure team is at the forefront of building the distributed systems that empower our next-generation Liquid Foundation Models. As our operations expand, we aim to innovate, implement, and enhance the infrastructure crucial for large-scale training.This role is centered around high ownership of training systems, emphasizing runtime, performance, and reliability rather than a typical platform or SRE function. You will collaborate within a small, agile team, creating vital systems from the ground up instead of working with pre-existing infrastructure.While San Francisco and Boston are preferred, we are open to other locations.What We're Looking ForWe are seeking an individual who:Embraces the complexity of distributed systems: Our team is dedicated to maintaining stability during extensive training runs, troubleshooting training failures across GPU clusters, and enhancing overall performance.Is passionate about building: We value team members who take pride in developing robust, efficient, and reliable infrastructure.Excels in uncertain environments: Our systems are designed to support evolving model architectures. You will be making decisions based on incomplete information and rapidly iterating.Aligns with team goals and delivers results: The best engineers on our team align with collective priorities while providing data-driven feedback when challenges arise.The WorkDesign and develop core systems that ensure quick and reliable large training runs.Create scalable distributed training infrastructure for GPU clusters.Implement and refine parallelism and sharding strategies for evolving architectures.Optimize distributed efficiency through topology-aware collectives, communication/compute overlap, and straggler mitigation.Develop data loading systems to eliminate I/O bottlenecks for multimodal datasets.

Join our dynamic team at Adyen as a Technical Staff Member in San Francisco! We are seeking innovative minds passionate about technology and problem-solving. In this role, you will collaborate with cross-functional teams to craft solutions that enhance our services and improve customer experiences.

About TierZero TierZero helps engineering teams use AI to build and ship code more efficiently. The platform targets the bottleneck of human speed in production, giving teams tools for faster incident response, better operational visibility, and shared knowledge. TierZero is backed by $7M in funding from investors including Accel and SV Angel. Companies like Discord, Drata, and Framer trust TierZero to strengthen their infrastructure for AI-driven engineering. Role Overview: Founding Member of Technical Staff This is an on-site role based at TierZero’s San Francisco headquarters, with three days a week in the office. As a founding member, direct collaboration with the CEO, CTO, and early customers shapes the direction of both product and systems. The work spans hands-on development and close engagement with users and leadership. What You Will Do Design and build intelligent AI systems to analyze large volumes of unstructured data. Deliver full-stack features based on real user feedback. Improve the product experience so AI agents are both reliable and easy for engineers to use. Develop systems that automatically evaluate LLM outputs and advance agentic reasoning using self-play and feedback loops. Create machine learning pipelines, including data ingestion, feature generation, embedding stores, retrieval-augmented generation (RAG), vector search, and graph databases. Prototype with open-source and new LLMs, comparing their strengths and weaknesses. Build scalable infrastructure for long-running, multi-step agents, with attention to memory, state, and asynchronous workflows. What We Look For Over five years of relevant professional or open-source experience. Comfort working in environments with uncertainty and evolving challenges. Strong product focus and a drive for customer satisfaction. Interest in large language models (LLMs), Model Control Planes (MCPs), cloud infrastructure, and observability tools. Previous startup experience is a plus. Location This position is based in San Francisco. Expect to work on-site three days per week at TierZero’s HQ.

TierZero builds tools that help engineering teams deliver and manage code efficiently. The platform enables quicker incident response, clearer operational visibility, and shared knowledge among engineers. Backed by $7 million from investors like Accel and SV Angel, TierZero supports clients such as Discord, Drata, and Framer as they strengthen infrastructure for AI-driven work. This in-person role is based at TierZero's San Francisco headquarters, with a hybrid schedule requiring three days onsite each week. As a founding member of the technical staff, work directly with the CEO, CTO, and customers to influence the direction of TierZero’s core products and systems. The position calls for flexibility as priorities shift and close collaboration across the company. What you will do Design and develop AI systems that handle large volumes of unstructured data. Build full-stack product features, informed by direct feedback from users. Enhance the product so agents are intelligent, reliable, and easy for engineers to use. Create systems to automatically evaluate outputs from large language models and improve agentic reasoning through self-play and feedback. Construct machine learning pipelines, including data ingestion, feature creation, embedding stores, retrieval-augmented generation (RAG) pipelines, vector search, and graph databases. Experiment with open-source and emerging large language models to compare different approaches. Develop scalable infrastructure for long-running, multi-step agents, including memory, state management, and asynchronous workflows. Requirements Interest in working with large language models, managed cloud platforms, cloud infrastructure, and observability tools. At least 5 years of professional experience or significant open-source contributions. Comfort with shifting priorities and tackling new technical problems. Strong product focus and commitment to customer outcomes. Openness to learning from a team with a track record of delivering over $10 billion in value. Ability to work onsite in San Francisco three days per week. Bonus: Experience in a startup setting and familiarity with startup dynamics.

tierzero is looking for a Founding Member of Technical Staff to help shape the direction of its technology from the ground up. This role is based at the company's San Francisco headquarters. Role overview As an early technical hire, you will work closely with engineers and product managers to build new products and features. The work centers on designing, coding, and delivering software solutions that address client needs and support tierzero's growth. Impact Contributions in this role will directly influence the company's future. The team values initiative and hands-on problem solving, giving each member a chance to make a visible difference in how the company evolves. Collaboration This position involves regular collaboration with a small, focused team. Input and ideas from every member help guide product direction and technical decisions.

tierzero seeks a Founding Member of Technical Staff to play a key role in building the company’s technology from the earliest stages. This position is based at the San Francisco headquarters and offers the chance to collaborate directly with founders and engineers. Role overview As an early team member, you will help design and develop new products and systems. The work involves close collaboration with others in the office, shaping both the technical direction and the culture of the engineering team. What you will do Develop core technology in partnership with founders and engineers Contribute ideas and code that guide the evolution of tierzero’s products Help define engineering standards and establish best practices Location This position is based onsite at the San Francisco HQ.

Overview: Due to the increasing market demand and a robust six-month product roadmap, Listen Labs is expanding its engineering team. We seek a technically adept individual (our team includes three IOI medalists) who is eager to contribute to a product that is revolutionizing corporate decision-making. If you are passionate about solving intricate problems from start to finish, we invite you to connect with us.About Listen LabsListen Labs is an innovative AI-driven research platform that empowers teams to swiftly extract insights from customer interviews in hours rather than months. Our technology enables clients to analyze conversations, identify recurring themes, and expedite informed product decisions.Company Highlights:Exceptional Team: Composed of seasoned entrepreneurs (with prior AI exits), co-founders, and experts from leading firms such as Jane Street, Twitter, Stripe, Affirm, Bain, Goldman Sachs, and more, our team is built on a foundation of excellence.Rapid Growth: We are a dynamic team of 40, supported by Sequoia, achieving a remarkable growth trajectory from $0 to $14 million run-rate in less than a year. We prioritize speed, craftsmanship, and collaboration with individuals who embrace ownership.Impressive Traction: We have seen rapid growth across various sectors, securing enterprise clients such as Google, Microsoft, Nestlé, and P&G.Outstanding Performance: Our industry-leading win rate is a direct result of our uniquely differentiated product.Market Validation: We consistently attract customers across every segment, often landing six-figure deals that lead to quick expansions.Viral Product: Our interviews are shared with tens of thousands of viewers, driving product-led growth, organic expansion, and daily inquiries from Fortune 500 companies.Technical Challenges:Research Agent Development: Unlike traditional software purchases, hiring McKinsey involves gaining insights and execution expertise. We are building Listen Labs with that mindset — an AI agent that understands our platform and best research practices, assisting users in project setup, interview execution, and response analysis.Human Database Creation: A core value proposition is our capability to connect users with specific demographics. We are developing a database of millions of individuals, continually enhancing our understanding of user needs as they engage with Listen Labs.

At Composio, we are developing advanced infrastructure that enables agents to seamlessly interact with essential work tools such as GitHub, Gmail, Notion, Salesforce, and more. Our dedicated team of engineers is committed to tackling challenges ranging from contextual understanding to search functionalities, ensuring we provide an exceptional bridge between your agents and their tools.Having secured a $25M Series A funding from Lightspeed, alongside prominent angel investors like Guillermo Rauch (CEO of Vercel), Dharmesh Shah (CTO of HubSpot), and Gokul Rajaram, we have experienced remarkable growth, tripling our ARR at the start of this year. Our clientele includes notable names from Y Combinator cohorts to Wabi, Glean, Zoom, and beyond.Your RoleEnhance the experience of teams utilizing our platform by refining our core APIs and SDK.Create intuitive interfaces for both frontend and SDK applications.Take ownership of product development from concept through to production.Collaborate closely with customers to cultivate their loyalty while enhancing the product.Craft clear and concise documentation.

As a Technical Staff Member specializing in Machine Learning, you will:Engage in the complete development lifecycle of innovative large-scale deep learning models.Curate datasets, architect solutions, implement algorithms, and train and assess models to enhance our offerings.Work collaboratively with engineers and researchers to convert groundbreaking research into real-world applications.Join us at a pivotal time, take on diverse roles, and contribute to building transformative products from the ground up!

At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We strive to build a future where everyone has access to the knowledge and tools essential for making AI work effectively for their unique objectives.Our team comprises scientists, engineers, and innovators who have contributed to some of the most widely adopted AI products, including ChatGPT and Character.ai, as well as notable open-weight models like Mistral and popular open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleThe Post-Training Researcher position is pivotal to our roadmap. It serves as a crucial connection between raw model intelligence and a system that is genuinely beneficial, safe, and collaborative for human users.This role uniquely combines fundamental research with practical engineering, as we do not differentiate between these functions internally. Candidates will be expected to produce high-performance code and analyze technical reports. This position is ideal for individuals who relish both deep theoretical inquiry and hands-on experimentation, aiming to influence the foundational aspects of AI learning.Note: This position is classified as an 'evergreen role', meaning we continuously accept applications in this research domain. Given the high volume of applications, an immediate match for your skills and experience may not always be available. However, we encourage you to apply; we regularly review submissions and reach out as new opportunities arise. You are welcome to apply again after gaining more experience, but we ask that you refrain from applying more than once every six months. Additionally, specific postings for singular roles may be available for distinct projects or team needs, in which case you are welcome to apply directly in conjunction with this evergreen role.What You’ll DoDevelop and Optimize Recipes: Refine post-training recipes, encompassing various datasets, training stages, and hyperparameters, while assessing their impact on multiple performance metrics.Iterate on Evaluations: Engage in a continuous process of defining evaluation metrics, optimizing them, and recognizing their limitations. You will be accountable for enhancing performance metrics and ensuring they are meaningful.Debug and Analyze: During the fine-tuning of training configurations, you may encounter results that appear inconsistent. You will be responsible for troubleshooting and cultivating a deeper understanding to apply to subsequent challenges.Scale and Investigate: Assess and expand the capabilities of our models while exploring potential improvements.

Overview: Join Listen Labs as we respond to a surge in market demand with an ambitious 6-month product roadmap. We are expanding our engineering team and are on the lookout for a highly skilled technical expert (our current team includes three IOI medalists) who is eager to build a transformative product that reshapes decision-making for businesses. If you have a passion for solving intricate problems from start to finish, we want to connect with you.About Listen LabsListen Labs is an AI-driven research platform designed to help teams quickly extract insights from customer interviews in a matter of hours rather than months. We empower our clients by enabling them to analyze conversations, identify key themes, and make faster, more informed product decisions.Why Work with Us?Exceptional Team: Founded by seasoned entrepreneurs with a successful AI exit, along with talent from renowned companies such as Jane Street, Twitter, Stripe, Affirm, Bain, and Goldman Sachs, our team boasts impressive credentials including IOI and ICPC backgrounds.Rapid Growth: As a 40-person team backed by Sequoia Capital, we have achieved a remarkable growth trajectory, scaling from $0 to a $14 million run-rate in less than a year. We prioritize craftsmanship and thrive on collaboration with individuals who take ownership.Impressive Traction: We are experiencing rapid growth across various sectors, securing enterprise clients such as Google, Microsoft, Nestlé, and Procter & Gamble.Proven Performance: We maintain an industry-leading win rate driven by our uniquely differentiated product.Market Validation: We consistently attract customers from diverse segments, achieving six-figure contracts that facilitate quick expansions.Viral Product: Our interviews reach tens of thousands of viewers, promoting product-led growth, organic expansion, and daily interest from Fortune 500 companies.Technical Challenges Await:Research Agent Development:Unlike traditional software purchases, hiring McKinsey offers valuable opinions, expertise, and execution. We aim to provide users with an AI agent that possesses complete knowledge about our platform and best research practices, assisting them in project setup, interview conduction, and response analysis.Human Database Creation:One of our core offerings is the ability to identify target users effectively (e.g., "power users of ChatGPT and Excel"). We are in the process of building a comprehensive database that connects users with the insights they need.

About the RoleIn the realm of machine learning, pretraining lays the foundation for a general model, while post-training refines that model, enhancing its utility, controllability, safety, and performance in real-world applications. As a Post-Training Research Scientist, you will transform large pretrained robot models into production-ready systems through methodologies such as fine-tuning, reinforcement learning, steering, human feedback, task specialization, evaluation, and on-robot validation at scale. This position offers a unique opportunity for individuals from diverse backgrounds to evolve into full-stack ML roboticists, adept at swiftly identifying challenges across machine learning and control domains. This is where innovative research converges with practical implementation.Your Responsibilities Include:Crafting fine-tuning and adaptation strategies tailored for specific robotic tasks and embodiments.Developing methodologies to enhance reliability, robustness, and controllability of robotic systems.Establishing evaluation frameworks to assess real-world robot performance beyond just offline metrics.Collaborating with ML infrastructure teams to optimize inference-time performance, including latency, stability, and memory usage.Utilizing advanced techniques such as imitation learning, reinforcement learning, distillation, synthetic data, and curriculum learning.Bridging the gap between model outputs and tangible outcomes in the physical world.You Might Excel in This Role If You:Possess experience in fine-tuning large models for downstream applications, including RLHF, imitation learning, reinforcement learning, distillation, and domain adaptation.Have a background in embodied AI, robotics, or real-world machine learning systems.Demonstrate a strong commitment to evaluation, benchmarking, and failure analysis.Are comfortable troubleshooting and debugging across the entire ML stack, from analyzing loss curves to understanding robot behavior.Enjoy rapid iteration and thrive on real-world feedback loops.Aspire to connect foundational models with practical deployment scenarios.About GeneralistAt Generalist, we are dedicated to realizing the vision of general-purpose robots. We envision a future where industries and homes benefit from collaborative interactions between humans and machines, enabling us to achieve more than ever before. Our focus is on building embodied foundation models, starting with dexterity, and advancing the frontiers of data, models, and hardware to empower robots to intelligently engage with their environments.

Role overview OpenAI is looking for a Researcher focused on Agentic Post-Training, based in San Francisco. This role centers on analyzing and improving how AI systems behave after their initial training. The goal is to broaden the capabilities of AI and refine how models respond in complex situations. What you will do Study and assess agentic behaviors in trained AI models Create new approaches to strengthen these behaviors after training Collaborate with a talented team on projects that shape the future of artificial intelligence research Collaboration and impact This position involves hands-on research with other specialists at OpenAI. The work directly supports the advancement of AI capabilities and helps define new benchmarks for agentic performance in artificial intelligence.