Careers at Preference Model

About Us Preference Model is building automated ML research engineering. Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential. About the Role Frontier research moves only as fast as its infrastructure permits. Building solid infrastructure is foundational to our mission of pushing self-directed learning as far as it can go. We are looking for Senior ML Infrastructure Engineers to build the infrastructure and systems that power the frontier of post-training on large language models . This role involves building scalable infrastructure to enable high-throughput systems and shape how our research is run, bringing us closer to models that can train themselves on what they aren't yet good at. What You Will Do Design, build, and scale the compute, scheduling, and data infrastructure that powers post-training research on our in-house RL environments Develop and maintain core ML framework primitives and internal tooling that researchers rely on daily, accelerating reproducible experimentation and reducing time from idea to result Build evaluation and benchmarking infrastructure, monitoring, logging, and debugging tooling, and automated testing and deployment systems, so failures are caught early and infrastructure stays reliable as it scales Partner directly with Research Engineers to translate research needs into infrastructure requirements, and ship fast in response to their feedback What We are Looking For Strong software engineering fundamentals and hands-on experience building production-grade LLM inference and training infrastructure (ideally from the ground up) Experience building LLM training/inference internals such as transformers, distributed training, and working on inference libraries like vLLM, SGLang, Megatron Experience working on RL training frameworks like Slime, veRL, Ray Train, SkyRL Significant experience and understanding of distributed systems principles, and have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes), building systems for high-throughput, low-latency workloads Have experience with data engineering tools and building robust, scalable data pipelines Proficiency in core ML frameworks such as PyTorch or JAX Can balance production rigor with the pace of fast-moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists What We Offer: Competitive cash and equity compensation (>90th percentile) Ownership and autonomy in a fast moving startup environment Opportunity to work alongside senior and staff engineers from frontier labs and infrastructure companies, plus top ML engineers Health, vision, dental, benefits 401K match Lunch provided everyday onsite Weekly snack orders Visa sponsorship & relocation support available We value diverse perspectives and experiences. If you're excited about this role but don't check every box, we still encourage you to apply.

About Us Preference Model is building automated ML research engineering. Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential. About the Role We’re hiring experienced Machine Learning Engineers to design and build reinforcement learning environments to safely advance model capabilities in machine learning research and engineering. Specifically, you'll be teaching frontier models to do the work of an ML engineer or researcher at a frontier lab. This role blends research and engineering . It will require you to stay up to date with the latest research, develop novel approaches, and realize them in code. You will have full ownership and autonomy of the environments you build. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers and engineers. You will join our Capabilities org, a small, high-ownership team and contribute directly to the data layer that powers frontier LLM capability. Note: This role is only for experienced ML Engineers . We have a separate opening for New Grads . What You Will Do: Design and build RL environments and reward functions that produce clean, learnable signals for frontier models on ML research and engineering tasks Build deep expertise across the frontier of ML research, training, and inference infrastructure Collaborate with others to brainstorm and create new ideas and tools to improve the environment building process What We are Looking For (Qualifications): 5+ years of experience working in machine learning or research, primarily on LLMs and transformer models You have strong ML fundamentals and broad research interests. You read many papers or tutorials, understand topics deeply and have the creativity to translate them into RLVR problems Proficiency in Python and systems programming and at least one of PyTorch or JAX Problem solvers who take ownership and drives solutions end-to-end Passion for staying current with the rapidly evolving ML infrastructure landscape Ability to meet throughput expectations and respond quickly to feedback You may be a good fit if you also: Have expert knowledge in an active DL/ML research area, with publications or public code to show for it. Research experience (PhD, MS) is a big plus Have deep understanding of transformer internals, training/inference of modern LLMs, experience with inference libraries (vLLM, SGLang, etc) Have strong expertise in kernel development (CUDA, Triton, Pallas) Have built complex interactive RL environments What We Offer: Competitive cash and equity compensation (>90th percentile) Ownership and autonomy in a fast moving startup environment Opportunity to work with top machine learning engineers Health, vision, dental, benefits 401K match Lunch provided everyday onsite Weekly snack orders Visa sponsorship & relocation support available We value diverse perspectives and experiences. If you're excited about this role but don't check every box, we still encourage you to apply.