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

Preference Model; San Francisco (CA); On-site; USD 200,000 - 350,000; Full time

Benefits offered by this job

Health, vision, dental benefits

401K match

Lunch provided onsite

Weekly snack orders

Visa sponsorship & relocation support

Job summary

Preference Model in San Francisco seeks a Senior ML Infrastructure Engineer to design, build, and scale the infrastructure that powers post-training research on in-house RL environments. You will develop core ML framework primitives and tooling to accelerate reproducible experimentation and reduce time from idea to result.

Work closely with Research Engineers to translate research needs into scalable infra while tackling distributed systems challenges, cloud platforms, and high-throughput

Qualifications

• Proven experience building production-grade ML infrastructure.
• Hands-on with distributed training and high-throughput systems.
• Solid experience with cloud platforms and container orchestration.

Responsibilities

• Design, build, and scale compute, scheduling, and data infra for post-training research.
• Develop core ML framework primitives and internal tooling for reproducible experiments.
• Create evaluation, logging, and deployment tooling to catch failures early.
• Collaborate with Research Engineers to translate needs into scalable infra

Skills

LLM inference infrastructure

Distributed systems

Kubernetes

AWS/GCP

PyTorch/JAX

RL training frameworks

Tools

vLLM

Megatron

SGLang

Slime

veRL

Ray Train

SkyRL

Job description

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

Compensation Range: $200K - $350K