Member of Technical Staff - Machine Learning Infrastructure Engineer

About Us

Preference Model is automating ML engineering and focuses on models' abilities to develop software. The way we build software is evolving rapidly. Five years ago, we wrote every line of code by hand. Today, we don't. We are shaping the future of software development.

Recent models perform 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 this is the supply of hard, high-fidelity scenarios that highlight where the best models still break. That is what we build.

Our founding team has 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

Founding 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 ML Infrastructure Engineers to build the systems that power the frontier of post-training on large language models.

• 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, experience building production-grade infrastructure (ideally for ML or data-intensive systems), and proficiency in core ML frameworks such as PyTorch or JAX
• Understanding of distributed systems principles, and hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes), building systems for high-throughput, low-latency workloads
• Experience with data engineering tools and building robust, scalable data pipelines
• Somewhat familiar with LLM training/inference internals (transformers, distributed training, inference libraries like vLLM or SGLang) — deep expertise is a plus, not a requirement
• Able to balance production rigor with the pace of fast-moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists

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

Career Opportunities

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