Baseten Logo

Baseten

Post-Training Research Engineer

Reposted 2 Months Ago
Be an Early Applicant
Hybrid
San Francisco, CA, USA
200K-275K Annually
Mid level
Hybrid
San Francisco, CA, USA
200K-275K Annually
Mid level
Build in-house tools for post-training models, focusing on improving model efficiency using various ML techniques across the system stack.
The summary above was generated by AI

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 math 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 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.

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

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, containerization, 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

  • (U.S. only) 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

  • (U.S. only) 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).

HQ

Baseten San Francisco, California, USA Office

San Francisco, CA, United States

Similar Jobs

11 Days Ago
In-Office
San Francisco, CA, USA
Entry level
Entry level
Artificial Intelligence • Robotics • Software
Develops post-training recipes and data pipelines for AI models, creates evaluations, builds agentic systems with custom tools, and integrates reasoning capabilities into live scientific workflows. The role collaborates with scientists, robotics engineers, and operations teams to improve experimental design and assay development while shaping the technical direction of a new machine learning team.
Top Skills: Agentic SystemsData PipelinesJaxLarge Language ModelsPythonPyTorchReinforcement Learning
21 Days Ago
In-Office
Palo Alto, CA, United States
609K-900K Annually
Senior level
609K-900K Annually
Senior level
Artificial Intelligence • Software • Generative AI
Own the post-training pipeline for AI roleplay models, transforming raw data into improved model behavior through supervised fine-tuning, reinforcement learning, reranking, data curation, and evaluation. The role involves closing the loop from evaluation signals to training runs, defining problems independently, and working onsite with a small, fast-moving engineering team.
Top Skills: Reinforcement Learning (Rl)RerankingSupervised Fine-Tuning (Sft)
25 Days Ago
Remote or Hybrid
San Francisco, CA, USA
165K-310K Annually
Senior level
165K-310K Annually
Senior level
Artificial Intelligence • Machine Learning • Software
Design, optimize, and deploy large language model training and post-training pipelines. Improve model quality through fine-tuning, reinforcement learning, preference optimization, evaluation, and experimentation. Build PyTorch-based infrastructure, optimize distributed multi-GPU training, diagnose performance and convergence issues, and develop production-ready AI systems. Collaborate with researchers, infrastructure engineers, platform teams, and customers while contributing to open-source projects and reusable training capabilities.
Top Skills: CudaDeepspeedDistributed TrainingDpoFsdpGpu Performance OptimizationGrpoHugging Face TransformersLightning FabricMegatron-LmMixed PrecisionMulti-Gpu SystemsNvidia MoltPeftPpoPythonPyTorchReinforcement LearningReward ModelingRlhfSglangSupervised Fine-TuningTensorrtTransformer-Based Language ModelsTritonTrlVllm

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account