Member of Technical Staff - ML Infrastructure Engineer, Post-training

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 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 Have strong software engineering fundamentals, experience building production-grade infrastructure (ideally for ML or data-intensive systems), and proficiency in core ML frameworks such as PyTorch or JAX 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 Experience working on RL training frameworks like Slime, veRL, Ray Have some familiarity with LLM training/inference internals (transformers, distributed training, inference libraries like vLLM or SGLang); deep expertise is a plus, not a requirement 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.

Design and scale compute, scheduling, and data infrastructure to power post-training research on in-house RL environments. Develop core ML framework primitives and evaluation tooling to accelerate reproducible experimentation for research engineers.

Requires strong software engineering fundamentals with experience building production-grade ML or data-intensive infrastructure. Proficiency in PyTorch or JAX, distributed systems, cloud platforms, and familiarity with LLM internals is expected.

• Competitive cash compensation • Equity compensation • Health insurance • Vision insurance • Dental insurance • 401K match • Daily onsite lunch • Weekly snack orders • Visa sponsorship • Relocation support

• KubernetesSouhaitée
• Amazon Web ServicesSouhaitée
• Google CloudSouhaitée

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Software Engineering
• PyTorch
• JAX
• Distributed Systems
• AWS
• GCP
• Kubernetes
• Data Engineering
• LLM Training
• LLM Inference
• vLLM
• SGLang
• Compute Scheduling
• Data Infrastructure
• Post-training Research
• RL Environments
• Large Language Modeling
• Self-Directed Learning
• Transformer (Machine Learning Model)
• Distributed Machine Learning
• Research
• Artificial Intelligence
• Amazon Web Services
• Automation
• Test Automation
• Benchmarking
• Debugging
• Scalability
• Machine Learning
• Team Building
• Tooling
• Scheduling
• Technical Debt
• Low Latency
• Data Pipelines
• PyTorch (Machine Learning Library)
• Machine Learning Frameworks
• Claude AI
• Machine Learning Infrastructure

• Software
• Engineering
• Technology
• Data & Analytics
• Science & Research
• Infrastructure Engineer
• Machine Learning Engineer
• Software Developers
• Computer and Information Research Scientists

Expérience minimale

5+ ans

Langue de l’offre

anglais

Heures de travail

40 heures par semaine

Autorisation de travail

Parrainage de visa mentionné