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Baseten

Product Manager, Training Platform

San Francisco; New YorkHybrid · United States
FullTime · EPD
POSTED 11 NOVVERIFIED 1H AGO
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Role
0 days opentrue age
Closes in ~84d at the median (84d)
Posted 11 Nov · n=11
Payats field
$200k–400kposted
▲ +48% vs market median $203k
$168k$400k
Market p25–p75
$168k–$251k
Baseten p25–p75
$293k–$300kn=7
SF Bay Area · n=256 · 365d to 11 Nov
CompanyBaseten →
103open roles
Opened · 30d
+8
Closed · 30d
−5
11d of snapshots · 30d window
Market
4,035open · Product
Opened · 7d
+3689% of open
All product
8,169open
All tracked boards · live 11 Nov · n=4,035
Visa
22H-1B LCAs · FY26
Median wage
$201k
LCAs FY26 · n=22 · to 2026-06-17
Spark

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.

PRODUCT AT BASETEN

Baseten is building a new cloud for AI. Our ambition is to make inference, training, and agent execution part of one coherent platform, and to shape how the next generation of AI products gets built. Product decisions here reach from the economics of GPU infrastructure to the abstractions developers build on every day.

We’re hiring builders and tastemakers with the ambition to help create that future. You should bring a strong point of view about what should exist, the taste to make complex technology feel simple, and the technical judgment to build it alongside engineers, researchers, and designers. You’ll work directly with customers and forward deployed teams, turn emerging capabilities into new possibilities for developers, and carry an idea from early conviction through to production and adoption.

THE ROLE

Help make training and fine-tuning AI models dramatically easier.

Training a model is hard enough. Developers shouldn't have to spend their time wrestling with infrastructure, waiting for GPUs, or figuring out why a job failed.

We're building a training platform that lets AI teams focus on improving their models instead of managing the systems underneath them.

As Product Manager for Training Platform, you'll own the developer experience for running training jobs on Baseten, from environment setup and job submission to scheduling, debugging, checkpointing, and recovery.

You'll work alongside engineers and researchers solving some of the hardest problems in distributed training, helping turn that complexity into a product developers can depend on.

RESPONSIBILITIES

Own the product experience for training jobs, from setup and submission through execution, monitoring, and recovery

Make distributed training easier to use, with intuitive workflows for scheduling, GPU availability, logs, checkpoints, and debugging

Work closely with research and infrastructure teams to support different training workloads, including supervised fine-tuning and reinforcement learning

Help developers recover from failures, reproduce experiments, and spend less time waiting on infrastructure

Connect training with the rest of Baseten's platform, including Loops, model evaluation, and inference deployment

Work directly with customers to understand how they train and adapt models, then turn those insights into product improvements

REQUIREMENTS

Experience building and shipping AI/ML products or developer platforms

A technical background in software engineering, ML engineering, research, or equivalent hands-on work

Direct experience working with teams that train, fine-tune, or adapt machine learning models

Strong understanding of distributed training concepts, including GPU memory, scheduling, data access, checkpointing, and fault recovery

Familiarity with the infrastructure needs of supervised fine-tuning and reinforcement learning

Strong product instincts for developer workflows and the ability to simplify technically complex systems

Experience partnering with researchers and engineers to take new capabilities from concept to production

NICE TO HAVE

Hands-on experience running distributed training, building ML developer tools, or working with trainer and sampler systems for reinforcement learning.

WHAT SUCCESS LOOKS LIKE

AI teams can run more experiments, recover from failures faster, and spend more of their time improving models instead of managing compute. Baseten becomes a natural place to train, fine-tune, and deploy custom AI models.

Source · BasetenFirst seen 11 NovOriginal ↗