Senior ML Infrastructure Engineer — High-Throughput AI Research

Preference Model; San Francisco (CA); On-site; USD 180,000 - 300,000; Full time

Benefits offered by this job

Cash and equity

Ownership & autonomy

Visa sponsorship

Relocation support

Lunch onsite

Weekly snacks

401K match

Health benefits

Job summary

Preference Model is seeking a Senior ML Infrastructure Engineer to build the systems powering post-training research on large language models. You will design and scale the compute, scheduling, and data infrastructure for in‑house RL environments and develop core ML framework primitives used by researchers daily.

You will partner with Research Engineers to translate research needs into infrastructure requirements, ensuring reproducible experiments and fast iteration.

Qualifications

• Strong software engineering fundamentals and production‑grade infrastructure experience.
• Experience with ML frameworks and distributed systems.
• Hands‑on with AWS/GCP and Kubernetes.
• Familiarity with LLM training/inference internals is a plus.

Responsibilities

• 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.

Skills

PyTorch

JAX

Distributed systems

Cloud platforms (AWS/GCP)

Kubernetes

Data pipelines

LLM training familiarity

Job description

Preference Model is seeking a Senior ML Infrastructure Engineer to build the systems powering post-training research on large language models. You will design and scale the compute, scheduling, and data infrastructure for in‑house RL environments and develop core ML framework primitives used by researchers daily.

You will partner with Research Engineers to translate research needs into infrastructure requirements, ensuring reproducible experiments and fast iteration.