Post-Training ML Research Engineer — Equity & PTO

Baseten; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive compensation including equity

100% coverage of medical, dental, and vision insurance

Generous PTO policy including company-wide Winter Break

Paid parental leave

Company-facilitated 401(k)

Exposure to variety of ML startups

Job summary

A leading AI technology company in San Francisco is seeking a research engineer with strong machine learning experience and solid foundations in mathematics and computer science. The role involves building in-house tooling for post-trained models using various techniques efficiently at scale. Candidates should have a deep understanding of modern ML techniques, advanced experience with libraries like PyTorch, and familiarity with distributed computing platforms. The company offers competitive compensation and comprehensive benefits, contributing to a collaborative and diverse workplace.

Qualifications

• Strong experience in machine learning and solid foundations in maths and computer science.
• Willingness to dive into messy problems and work collaboratively.

Responsibilities

• Build in-house tooling to support post-trained models.
• Work across system-level concepts like Kubernetes and tensor computation.
• Ensure efficient operation of various model architectures.

Skills

Deep understanding of modern ML techniques and tools for training transformers

Advanced experience in a tensor/array computation library

Knowledge of transformer training parallelism strategies

Experience profiling and improving performance of distributed GPU program

Ability to perform roofline analysis

Familiarity with HPC and distributed computing platforms

Solid fundamentals in operating systems concepts

Creative problem-solving skills

Tools

PyTorch

TensorFlow

Jax

Kubernetes

Slurm

Ray

Dask

Job description

A leading AI technology company in San Francisco is seeking a research engineer with strong machine learning experience and solid foundations in mathematics and computer science. The role involves building in-house tooling for post-trained models using various techniques efficiently at scale. Candidates should have a deep understanding of modern ML techniques, advanced experience with libraries like PyTorch, and familiarity with distributed computing platforms. The company offers competitive compensation and comprehensive benefits, contributing to a collaborative and diverse workplace.