Member of Technical Staff - Machine Learning Infrastructure Engineer, Post-training

Preference Model; Seattle (WA); On-site; USD 180,000 - 300,000; Full time

Benefits offered by this job

Health insurance

Vision insurance

Dental insurance

401K match

Lunch onsite

Weekly snacks

Job summary

Preference Model is seeking a Senior ML Infrastructure Engineer to build scalable systems powering post-training research on large language models. You will design compute, scheduling, and data infrastructure for in-house RL environments and maintain core ML framework primitives to accelerate experiments.

You will partner with Research Engineers to translate research needs into infrastructure requirements, ensuring reliability, low latency, and high throughput while scaling with cutting-edge AI

Qualifications

• Strong software engineering fundamentals for production-grade infra.
• Experience building ML/data-intensive infrastructure.
• Familiarity with ML frameworks such as PyTorch or JAX.

Responsibilities

• Design, build, and scale compute, scheduling, and data infrastructure for post-training research in in-house RL environments.
• Develop core ML framework primitives and internal tooling for reproducible experimentation.
• Build evaluation and benchmarking infra, monitoring, logging, and deployment systems to keep infra reliable as it scales.
• Collaborate with Research Engineers to translate research needs into infrastructure requirements and ship rapidly.

Skills

Software engineering

ML frameworks (PyTorch/JAX)

Distributed systems

Cloud platforms (AWS/GCP)

Kubernetes

Data pipelines

LLM training/inference basics

Infra reliability

Tools

JVM/CI tooling

Job description

About Us

Preference Model is automating ML engineering and a critical component is models' abilities to develop software.

The way we build software is changing fast. Five years ago we wrote every line of code by hand. Today, we don't. What does our work look like five years from now? We are shaping this future.

Recent models work well on narrow tasks but are still brittle on real software work: large codebases with real conventions and technical debt, judgment-heavy design decisions, and multi-step problems. The bottleneck on fixing that is the supply of hard, high-fidelity scenarios that find where the best models still break. That is what we build.

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
• 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.

Compensation Range: $180K - $300K