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Baseten

Product Manager, Inference Platform

San Francisco; New YorkHybrid · United States
FullTime · EPD
POSTED 11 NOVVERIFIED 1H AGO
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$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
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22H-1B LCAs · FY26
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$201k
LCAs FY26 · n=22 · to 2026-06-17
SparkPyTorchLLMs

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

Make AI inference faster, smarter, and more efficient at scale.

Making AI models faster and cheaper is one of the most important engineering challenges in the industry. But a faster runtime alone isn't enough. Customers need to understand what improved, whether it works for their models, and how to take advantage of it in production.

We're looking for a deeply technical Product Manager to work alongside our inference engineers and researchers, turning advances in model serving and performance into capabilities customers can confidently adopt.

You'll help bridge the gap between cutting-edge systems engineering and real-world production workloads.

RESPONSIBILITIES

Work with inference engineering and research teams to bring new performance capabilities to customers.

Translate advances in batching, caching, quantization, speculative decoding, and serving architectures into useful product features.

Define benchmarks and evaluations that demonstrate meaningful improvements in latency, throughput, quality, and cost.

Understand customer workloads and help prioritize the optimizations that will have the greatest impact.

Make performance features easier to configure, evaluate, and adopt in production.

Help customers understand tradeoffs across hardware, model configurations, autoscaling, and routing.

Partner with engineering on release readiness, compatibility, and regression testing so customers can trust new capabilities.

REQUIREMENTS

Experience building and shipping AI/ML products or infrastructure, particularly where performance, scalability, or production behavior mattered.

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

Strong understanding of model serving architectures, GPU economics, and latency, throughput, quality, and cost tradeoffs.

Experience working closely with researchers or infrastructure engineers to bring technically complex capabilities into production.

A rigorous approach to benchmarking, experimentation, and performance measurement.

Strong product judgment and the ability to translate deep technical work into clear customer value.

NICE TO HAVE

Hands-on experience with PyTorch, vLLM, TensorRT-LLM, SGLang, NVIDIA Dynamo, or similar technologies.

Experience with open-source inference projects, model optimization, or ML systems research.

WHAT SUCCESS LOOKS LIKE

Customers can take advantage of Baseten's latest inference optimizations without becoming experts in the underlying runtime. Performance improvements translate into measurable gains in speed, reliability, and cost for real production workloads.

BENEFITS

Competitive compensation, including meaningful equity

Source · BasetenFirst seen 11 NovOriginal ↗