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baseten
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. Product at Baseten Product at Baseten is a nascent function. Our company today has a strong engineering culture, is heavily customer-obsessed, and moves fast.
We're building the product function now, and you'd be one of the people who defines it. You'll work directly with our founders and with some of the best systems and AI engineers and you'll set the standard for what product looks like here.
PMs at Baseten don't sit above engineers - you earn ownership by being technical, finding the truth in front of customers, building great cross-functional relationships, and just shipping great product experiences.
Once a model is deployed, keeping it fast, reliable, and economical at scale is where production inference is won or lost. You'll own the surface that makes that happen: how deployments autoscale, how traffic is routed, how the system fails over, and how workloads scale across clusters and regions.
You'll own these as products end to end - both how they work under the hood and how customers configure and observe them - and you'll help set and define the roadmap that infrastructure and product teams alike can build towards.
This space is largely still evolving - think Cloud Infrastructure in mid-2000s. Your job is to make it 10x easier to reliably scale and serve AI models in production and set the market standard.
Impact and outcomes you'll drive You will own how workloads scale and where they land — autoscaling to demand (up under load, down to zero when idle) and a single placement policy expressing region, compliance regime, and capacity preference, with compliance-bound workloads given right-of-way on sensitive capacity.
You will make production inference reliable by default — every request reaches a healthy replica, rolling deploys never drop traffic, region-aware routing with multi-region / active-active and fallback as first-class policy, and health-aware recovery from stuck or bad replicas. You will build the release engine beneath safe rollouts — the traffic-shifting that powers canary/shadow/A/B, warm-ups, drain, and probes.
You will push the cost/performance frontier for serving AI at scale — latency, throughput, uptime, and cost-efficiency, plus a measurable decline in MTTR through self-serve incident management.
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