Post-Training Research Engineer

Baseten; San Francisco (CA); On-site; USD 200,000 - 275,000; Full time

Benefits offered by this job

Competitive compensation

100% medical coverage

Generous PTO policy

Paid parental leave

401(k)

Exposure to ML startups

Job summary

A leading AI company in San Francisco seeks a research engineer to enhance post-training ML models. You will develop in-house tools, work with diverse model architectures, and interact with various technologies including Kubernetes and GPU computing. The role offers competitive compensation, comprehensive benefits including medical coverage and PTO, and a collaborative environment to drive innovation in AI.

Qualifications

• Deep understanding of ML model training techniques.
• Experience with GPU computations and performance profiling.
• Willingness to tackle complex problems collaboratively.

Responsibilities

• Build in-house tooling for model training efficiency.
• Collaborate across technical stacks for systems-level concepts.
• Support customers’ post-trained ML models.

Skills

Understanding of modern ML techniques

Advanced experience in PyTorch

Understanding of transformer training parallelism

Profiling distributed GPU programs

Ability to perform roofline analysis

Familiarity with HPC and distributed computing

Solid fundamentals in operating systems

Creativity and problem-solving skills

Tools

PyTorch

TensorFlow

Jax

Kubernetes

Slurm

Dask

Job description

About Baseten

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.

We are looking for an engineer with strong experience in machine learning and solid foundations in maths and computer science to join our growing Post-Training team at Baseten.

Custom models are instrumental to the success of Baseten customers. By inference volume, the overwhelming majority of traffic at Baseten is to and from models that have been post-trained in some way, whether that be through reinforcement learning, supervised finetuning, a recent technique from the literature, or an in-house research technique from Baseten. The Post-Training team is responsible for the success of our customers’ post-trained models, and we employ a wide array of techniques to produce models that are more efficient and higher quality than even the biggest closed source models for the customer's specific needs.

Your role as a research engineer is to build the in-house tooling to support all of this. We care about training a wide spectrum of different model architectures with a variety of techniques efficiently and at scale. At times this involves zooming deep into a particular technical topic, but more often it involves working across the stack as a whole - systems-level concepts like Kubernetes, cgroups, storage systems, and networking topologies, as well as PyTorch distributed tensor computation, and GPU kernels.

RECENT RESEARCH

• Dense, on-policy or both?
• Repeated kv cache for long-running agents
• Distillation without the dark – replicating black-box on-policy distillation on Baseten

We don’t have a rigid set of skills, but here’s some of what we’re looking for:

• A deep understanding of modern ML techniques and tools for training transformers
• Advanced experience in a tensor/array computation library like PyTorch, TensorFlow, Jax, or similar
• A detailed understanding of transformer training parallelism strategies like data parallelism, sharded data parallelism, tensor parallelism, pipeline parallelism, context parallelism
• The experience and knowledge to profile and improve the performance of a distributed GPU program in PyTorch or a similar library
• The ability to perform roofline analysis on a transformer training setup
• A willingness to dive into messy problems, work with researchers, derive specifications by asking important questions, and execute
• Familiarity with HPC and distributed computing platforms like Slurm, Ray, Kubernetes, and Dask
• Familiarity with cluster networking technology like Infiniband, RoCE, GPUDirect
• Solid fundamentals in operating systems concepts like processes, files, kernel drivers, containerisation, and networking protocols
• A sense of creativity and willingness to ask difficult questions about our approach, assumptions, and tooling choices

Benefits

• Competitive compensation, including meaningful equity.
• 100% coverage of medical, dental, and vision insurance for employee and dependents
• Generous PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
• Paid parental leave
• Company‑facilitated 401(k)
• Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

Apply now

Embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward‑thinking team, we would love to hear from you.

At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Compensation Range: $200K - $275K