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Post-Training Research Engineer

Join Baseten as a Post-Training Research Engineer to build in-house tooling for efficient and high-quality machine learning models.

Location
San Francisco
Compensation
Not disclosed
Level
mid
Type
full time

Posted by employer 5 months ago

First seen on Joblaze 1 week ago

Last verified on the company career page 1 day ago

Apply at baseten → Save job Scanned from baseten.co

Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.

Benefits

401k Match Fertility and family-building stipend Flexible PTO Equity/Stock Options Health Insurance Parental Leave

Joblaze summary

In the role of Post-Training Research Engineer at Baseten, the individual will focus on developing in-house tools to enhance the efficiency and quality of custom AI models for clients. Key skills include expertise in machine learning frameworks like PyTorch and a solid grasp of distributed computing concepts, particularly in relation to transformer training. This position is well-suited for someone with a strong technical background and a collaborative mindset, eager to tackle complex challenges in a fast-paced environment. Baseten's rapid growth and commitment to innovation in AI make it an exciting place for professionals looking to make a significant impact.

Joblaze insights

Quick facts

What's the tech stack?
Joblaze extracted these technologies from the posting: Dask, GPU kernels, GPUDirect, HPC, Infiniband, JAX.
What seniority level is this role?
baseten targets mid-level candidates for this position.
Is this full-time or contract?
Full-time for this Post-Training Research Engineer role at baseten.

From the original posting

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 $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn 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.

THE ROLE

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 if 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.

The Manifesto: https://labs.baseten.co/manifesto

RECENT RESEARCH

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

  • Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)

  • Paid parental leave

  • Fertility and family-building stipend through Carrot

  • Company-facilitated 401(k)

  • Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

Apply now to 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).

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