Senior ML Infra Engineer for Post-Training LLM/RL Systems

Preference Model, Inc.; San Francisco (CA); Sur place; USD 180 000 - 280 000; Plein temps

Avantages offerts par ce poste

Competitive cash & equity ( >90th %ile

Ownership and autonomy

Work alongside senior staff from topML

Health, vision, dental, benefits

401K match

Lunch onsite

Weekly snacks

Visa sponsorship & relocation support

Résumé du poste

Preference Model, Inc. is seeking Senior ML Infrastructure Engineers in San Francisco to build the infrastructure powering post-training on large language models.

You will design and scale compute, scheduling, and data pipelines, develop core ML framework primitives, and implement evaluation, monitoring, and deployment tooling.

Join a frontier research team backed by strong funding, with equity, visa sponsorship, and a fast-moving startup culture.

Qualifications

• Strong software engineering fundamentals and production-grade LLM infra experience.
• Transformers, distributed training, and inference libraries experience.
• Hands-on with RL training frameworks and high-throughput workloads.
• Distributed systems knowledge and cloud/container orchestration.
• Proficiency in PyTorch or JAX for ML workflows.

Responsabilités

• Design, build, and scale compute, scheduling, and data infra for post-training research.
• Develop ML framework primitives and internal tooling to speed reproducible experiments.
• Create evaluation, monitoring, logging, debugging, and automated deployment tooling.
• Collaborate with Research Engineers to translate research needs into infrastructure requirements.

Connaissances

Strong software engineering

Production-grade LLM infra

Transformers & distributed training

RL training frameworks

Distributed systems

Cloud platforms (AWS/GCP)

Kubernetes

Docker

PyTorch or JAX

Data pipelines

Outils

Kubernetes

Docker

Terraform

vLLM tooling

Description du poste

Preference Model, Inc. is seeking Senior ML Infrastructure Engineers in San Francisco to build the infrastructure powering post-training on large language models.

You will design and scale compute, scheduling, and data pipelines, develop core ML framework primitives, and implement evaluation, monitoring, and deployment tooling.

Join a frontier research team backed by strong funding, with equity, visa sponsorship, and a fast-moving startup culture.