Staff Technical Lead for Inference & ML Performance

At Magic, we are driven by our mission to develop safe Artificial General Intelligence (AGI) that propels humanity forward in addressing the most critical challenges. We firmly believe that the future of safe AGI lies in automating research and code generation, allowing us to enhance models and tackle alignment issues more effectively than humans alone can manage. Our innovative approach combines cutting-edge pre-training, domain-specific reinforcement learning (RL), ultra-long context, and efficient inference-time computation to realize this vision.Position OverviewAs a Software Engineer within the Inference & RL Systems team, you will play a pivotal role in designing and managing the distributed systems that enable our models to function seamlessly in production, supporting extensive post-training workflows.This position operates at the intersection of model execution and distributed infrastructure, focusing on systems that influence inference latency, throughput, stability, and the reliability of RL and post-training training loops.Our long-context models impose significant execution demands, including KV-cache scaling, managing memory constraints for lengthy sequences, batching strategies, long-horizon trajectory rollouts, and ensuring consistent throughput under real-world workloads. You will be responsible for the infrastructure that ensures both production inference and large-scale RL iterations are efficient and dependable.Key ResponsibilitiesCraft and scale high-performance inference serving systems.Optimize KV-cache management, batching methods, and scheduling processes.Enhance throughput and latency for long-context tasks.Develop and sustain distributed RL and post-training infrastructure.Boost reliability across rollout, evaluation, and reward pipelines.Automate fault detection and recovery mechanisms for serving and RL systems.Analyze and eliminate performance bottlenecks across GPU, networking, and storage components.Collaborate with Kernel and Research teams to ensure alignment between execution systems and model architecture.QualificationsSolid foundation in software engineering and distributed systems.Proven experience in building or managing large-scale inference or training systems.In-depth understanding of GPU execution constraints and memory trade-offs.Experience troubleshooting performance issues in production machine learning systems.Capability to analyze system-level trade-offs between latency, throughput, and cost.

At Gimlet Labs, we are pioneering the development of the first heterogeneous neocloud designed specifically for AI workloads. As the demand for AI systems surges, traditional homogeneous infrastructures face critical limits in power, capacity, and cost. Our innovative platform effectively decouples AI workloads from their hardware foundations, intelligently partitioning tasks and orchestrating them to the most suitable hardware for optimal performance and efficiency. This strategy fosters heterogeneous systems that span multiple vendors and generations, including cutting-edge accelerators, enabling significant enhancements in performance and cost-effectiveness at scale.In addition to this foundational work, Gimlet is establishing a robust neocloud for agentic workloads. Our clients benefit from deploying and managing their workloads via stable, production-ready APIs, without the need to navigate hardware selection or performance optimization intricacies.We collaborate with foundation labs, hyperscalers, and AI-native companies to drive real production workloads capable of scaling to gigawatt-class AI datacenters.We are currently seeking a Member of Technical Staff specializing in ML systems and inference. In this pivotal role, you will be responsible for designing and constructing inference systems that facilitate the execution of complete models in real production environments. You will operate at the intersection of model architecture and system performance to ensure that inference processes are swift, predictable, and scalable.This position is perfect for engineers with a deep understanding of modern model execution and a passion for optimizing latency, throughput, and memory utilization across the entire inference lifecycle.

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.

At Inferact, we are on a mission to establish vLLM as the premier AI inference engine, revolutionizing AI progress by making inference both more accessible and efficient. Our founding team consists of the original creators and key maintainers of vLLM, positioning us uniquely at the nexus of cutting-edge models and advanced hardware.Role OverviewWe are seeking a passionate inference runtime engineer eager to explore and expand the frontiers of LLM and diffusion model serving. As models evolve and grow in complexity with new architectures like mixture-of-experts and multimodal designs, the demand for innovative solutions in our inference engine intensifies. This role places you at the heart of vLLM, where you will enhance model execution across a variety of hardware platforms and architectures. Your contributions will have a direct influence on the future of AI inference.

At Bugcrowd, we are redefining the landscape of cybersecurity. Since our inception in 2012, we have been committed to empowering organizations to regain control and stay ahead of cyber threats. By harnessing the collective creativity and expertise of our clients and an elite network of hackers, we leverage our patented AI-driven Security Knowledge Platform™. Our diverse community of hackers excels in uncovering vulnerabilities, swiftly adapting to the evolving threat landscape, including zero-day exploits. With our innovative CrowdMatch™ technology, we provide scalable, tailored solutions to enhance your security posture. Join us as we usher in a new era of crowdsourced security that outpaces cyber adversaries. For more information, visit www.bugcrowd.com. Headquartered in San Francisco and New Hampshire, Bugcrowd is supported by leading investors including General Catalyst, Rally Ventures, and Costanoa Ventures.Job SummaryThe Bugcrowd Reinforcement Learning and Reasoning Team is dedicated to advancing autonomous cybersecurity through the creation of authentic reinforcement learning environments tailored for foundational model applications. As a Staff Engineer, you will be at the forefront of AI Reinforcement Learning development and implementation. Your primary responsibility will be to design and build the infrastructure and tools that convert real-world vulnerability research into extensive reinforcement learning environments for training state-of-the-art AI systems.In this unique role, you will develop training environments that instruct AI systems on hacking and defending software. Your contributions will directly impact the capabilities of next-generation AI models. Rather than focusing on a single application, you will create the underlying infrastructure that generates thousands of environments for training leading-edge AI technologies.Our team operates at the intersection of AI, security research, and systems engineering, crafting environments that enable models to acquire essential skills such as vulnerability detection, exploitation, and remediation.

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're dedicated to crafting a future where everyone can harness the power of AI to meet their unique needs and aspirations.Our team comprises scientists, engineers, and innovators who have developed some of the most widely utilized AI products, including ChatGPT and Character.ai, as well as open-weight models like Mistral, in addition to renowned open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking a talented Infrastructure Research Engineer to architect and develop the foundational systems that facilitate the scalable and efficient training of large models using reinforcement learning.This position exists at the crossroads of research and large-scale systems engineering, requiring a professional who not only comprehends the algorithms behind reinforcement learning but also appreciates the practicalities of distributed training and inference at scale. You will have a diverse set of responsibilities, from optimizing rollout and reward pipelines to enhancing the reliability, observability, and orchestration of systems. Collaboration with researchers and infrastructure teams will be essential to ensure reinforcement learning is stable, rapid, and production-ready.Note: This is an evergreen role that we maintain on an ongoing basis to express interest. Due to the high volume of applications we receive, there may not always be an immediate position that aligns perfectly with your skills and experience. We encourage you to apply, as we continuously review applications and reach out to candidates when new opportunities arise. You may reapply after gaining more experience, but please refrain from applying more than once every six months. Additionally, you may notice postings for specific roles that cater to unique project or team needs; in those circumstances, you are welcome to apply directly alongside this evergreen role.What You’ll DoDesign, implement, and optimize the infrastructure that supports large-scale reinforcement learning and post-training workloads.Enhance the reliability and scalability of the RL training pipeline, including distributed RL workloads and training throughput.Create shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility of RL systems.Work closely with researchers to translate algorithmic concepts into production-quality training pipelines.Develop evaluation and benchmarking infrastructure to assess model performance based on helpfulness, safety, and factual accuracy.Publish and disseminate insights through internal documentation, open-source libraries, or technical reports that contribute to the advancement of scalable AI infrastructure.

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.

About Liquid AIBorn from the innovation of MIT CSAIL, Liquid AI is at the forefront of developing general-purpose AI systems that operate seamlessly across various deployment platforms, including data center accelerators and on-device hardware. Our solutions prioritize low latency, minimal memory consumption, privacy, and reliability. We collaborate with leading enterprises in sectors such as consumer electronics, automotive, life sciences, and financial services. As we experience rapid growth, we seek extraordinary talent to join our mission.The OpportunityJoin our Edge Inference team, where we transform Liquid Foundation Models into highly optimized machine code for resource-limited devices such as smartphones, laptops, Raspberry Pis, and smartwatches. As key contributors to llama.cpp, we establish the infrastructure necessary for efficient on-device AI. You will collaborate closely with our technical lead to tackle complex challenges that demand a profound understanding of machine learning architectures and hardware constraints. This role offers high ownership, allowing your code to be deployed in production environments and directly influence model performance on real devices.While San Francisco and Boston are preferred, we welcome applicants from other locations.

About AfterQuery AfterQuery develops training data and evaluation frameworks that leading AI labs use to improve their models. The team partners with major research institutions to build datasets and run assessments that go beyond standard benchmarks. As a post-Series A company based in San Francisco, AfterQuery values contributions from every team member. Work here directly shapes the next generation of AI models. Role Overview The Reinforcement Learning Environment Engineer designs datasets and evaluation systems that influence how advanced AI models learn and improve. This role involves close collaboration with research teams, hands-on experimentation with new data collection methods, and the creation of metrics to track model progress. Work moves from theoretical analysis to practical experiments, feeding directly into large-scale model training efforts. What You Will Do Develop data segments that expose key failure modes in sectors such as finance, software engineering, and enterprise operations. Refine reward signals for Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from Value Reinforcement (RLVR) systems. Define quantitative metrics for dataset quality, diversity, and their effects on model alignment and capability. Work closely with research teams to translate training objectives into concrete data requirements and evaluation criteria. This position is based in San Francisco.

Join the team at Mirendil as a Member of Technical Staff specializing in Machine Learning Systems. In this role, you will leverage your expertise to develop innovative solutions that enhance our ML frameworks and contribute to groundbreaking projects in the AI space. Collaborate with top talent in a dynamic environment that promotes creativity and technical excellence.

About UsAt Preference Model, we are pioneering the next generation of training data to fuel the evolution of AI technology. Although today's models demonstrate significant capabilities, they often fall short in diverse applications due to many tasks being out of distribution. We create reinforcement learning (RL) environments where models face research and engineering challenges, allowing them to iterate and learn from realistic feedback loops.Our founding team boasts experience from Anthropic’s data division, where we built data infrastructure, tokenizers, and datasets for Claude. Collaborating with leading AI labs, we aim to bring AI closer to its transformative potential, supported by a16z.About the RoleEvery RL environment we deploy must withstand a model actively attempting to exploit it. A task with a weak evaluation or an easily exploitable reward signal is counterproductive; it teaches the model to cheat instead of reason. We seek an individual dedicated to identifying these vulnerabilities before the model does.We have learned that domain knowledge alone does not make an effective reviewer. The ideal candidate is someone who has engaged in adversarial thinking: designing challenging problems that are difficult to exploit, dismantling others’ tasks, or directly researching reward hacking.Your ResponsibilitiesReview RL environments and training tasks for accuracy, robustness, and resistance to reward hacking.Identify potential ways a model could exploit grading systems, manipulate evaluation criteria, or bypass intended reasoning.Collaborate with environment authors to enhance grading systems, rectify reward signals, and redesign ineffective tasks.Develop and maintain review standards and checklists as we scale from hundreds to thousands of tasks monthly.Provide guidance on grader design during the planning phase of environments, ensuring quality before task construction.Who We Are Looking ForYou think like an attacker and have spent considerable time crafting problems that are challenging to exploit or deconstructing seemingly solid issues. A fundamental understanding of machine learning is essential, enabling you to anticipate model strategies, combined with enough engineering insight to assess whether a grader effectively tests its criteria.

Location: Preference for San Francisco, but remote candidates are welcome to apply.Duration: This internship will last for 10-12 weeks during Summer 2026.Compensation: This is a paid internship opportunity.About UsAt Preference Model, we are pioneering the next era of training data to fuel the advancement of AI technologies. While current models are impressive, they often struggle with diverse applications due to out-of-distribution tasks. Our focus is on developing reinforcement learning (RL) environments where models can engage with complex research and engineering challenges, iterating and learning from realistic feedback mechanisms.Our founding team boasts extensive experience from Anthropic's data division, where we built data infrastructure, tokenizers, and datasets that powered Claude. We collaborate with top AI labs to accelerate AI's journey toward its transformative potential and are proudly supported by a16z.About the RoleWe are seeking talented PhD students and exceptional undergraduate candidates to join us this summer in developing RL training environments tailored for large language models.What You'll DoDesign and implement RL environments to assess LLM reasoning across various ML, systems, and research problems.Produce clean, production-quality Python code (not just notebooks).Utilize Docker to create reproducible environments and troubleshoot issues as they arise.Translate ML research papers and concepts into actionable training tasks.Who We're Looking ForYou are either an undergraduate or a PhD student in Computer Science, Machine Learning, Mathematics, Physics, or a related discipline. You have a knack for writing real code beyond mere research prototypes and you enjoy reading ML literature in your spare time.Must-Have Qualifications:Proficient in Python programming.Understanding of large language models (LLMs), their strengths, and limitations.Self-motivated and capable of taking feedback to iterate quickly.Preferred Qualifications:Familiarity with transformer architecture and experience with training or inference code.Experience in writing CUDA kernels or engaging in low-level GPU programming.Deep knowledge in a particular research area (demonstrated by publications, public code, or strong coursework).A passion for continuous learning and research in the field of AI.

Join firecrawl as a Research Engineer specializing in Reinforcement Learning (RL). In this role, you will leverage your expertise to conduct innovative research and develop advanced RL algorithms that push the boundaries of technology. Collaborate with a talented team of engineers and researchers to solve complex problems and contribute to groundbreaking projects.

Be Part of the Future of Autonomous RoboticsAt Bedrock Robotics, we are pioneering the transition of AI from theoretical frameworks to practical applications in the built environment. Our team is comprised of seasoned professionals who have been instrumental in the success of innovative companies such as Waymo, Segment, and Uber Freight. We are at the forefront of deploying autonomous technologies in heavy construction machinery, significantly enhancing the efficiency and safety of multi-billion dollar infrastructure projects across the nation.With backing from $350 million in funding, our mission is to address the urgent need for housing, data centers, and manufacturing facilities, while simultaneously responding to the construction industry's labor shortages.This position is where cutting-edge algorithms meet the practical world of construction. You will work alongside industry experts and top-tier engineers to tackle complex real-world challenges that cannot be simulated. If you are eager to leverage advanced technology for impactful problem-solving within a skilled team, we encourage you to apply.

About UsAt Preference Model, we are at the forefront of developing advanced training data essential for the evolution of artificial intelligence. While today's AI models exhibit significant power, they often fall short in diverse applications due to limitations in their training data. We specialize in creating reinforcement learning environments that present AI with authentic research and engineering challenges, enabling them to iterate and learn through realistic feedback loops.Our founding team boasts experience from Anthropic’s data department, where we established the data infrastructure, tokenizers, and datasets that supported Claude. We collaborate with top-tier AI research labs to bring AI closer to its groundbreaking potential and are proudly backed by a16z.About the RoleAs a Software Engineer on our team, your responsibilities will include:Designing and Developing Reinforcement Learning Environments: Architect comprehensive simulation platforms that encompass environmental context, task definitions, and reward functions to facilitate AI agents' learning and performance of intricate tasks.Building Robust Training Infrastructure: Create scalable systems for post-training AI models, focusing on orchestration, performance optimization, and monitoring capabilities.Implementing Realistic Model Evaluations: Develop metrics for evaluating AI agent performance and establish the infrastructure and tools necessary for conducting these evaluations.Influencing Technical Strategy: Take charge of architectural decisions, impact product roadmaps, and contribute significantly to our engineering culture as an early-stage team member.About YouYou might be a great fit for this role if you possess the following qualities:Adept at leveraging language models effectively.Ability to innovate and think outside the box.A minimum of 4 years of software engineering experience, showcasing your ability to take ownership of projects.Proficiency in Python, Rust, or TypeScript, with the capability to work across the entire software stack.Hands-on experience with modern deployment practices, containerization, and cloud infrastructure (such as Kubernetes, AWS, or GCP).Strong problem-solving skills demonstrated through algorithmic challenges or complex system design tasks.Nice-to-HavesPreferred candidates will have experience in:Machine learning infrastructure or reinforcement learning.

Join primeintellect as a Research Engineer focused on Reinforcement Learning Infrastructure. In this role, you will be instrumental in advancing our cutting-edge AI technologies. You will collaborate with interdisciplinary teams to develop robust frameworks that enhance machine learning capabilities and drive innovation.As a key player in our engineering team, you will work on designing, implementing, and optimizing systems that support reinforcement learning algorithms. Your contributions will directly impact the efficiency and effectiveness of our AI solutions.

Cohere builds and deploys advanced AI models used by developers and enterprises. These models support applications like content generation, semantic search, retrieval-augmented generation (RAG), and intelligent agents. The team’s work aims to make AI more accessible and practical for real-world use. Each person at Cohere plays a direct role in strengthening the models and increasing their value for clients. The company values practical outcomes and continuous improvement, focusing on delivering reliable technology to users. The team includes researchers, engineers, designers, and professionals from a wide range of backgrounds. Cohere believes that diverse perspectives help create better products. The company welcomes those interested in shaping the future of AI to join its mission.

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.