← baseten / Product Manager - Dedicated Inference
tailored_resume_v2 / art_C7Nzt8_nyok
role
model
anthropic/claude-sonnet-4.6
created
2026-05-29T18:36
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What changed for baseten
| change | why it matters |
|---|---|
| Summary rewritten to lead with ICE scale metrics (675M+ engagements, 50K TPS, sub-25ms TP99) and RL workbench builder credibility | Baseten's core value prop is inference performance at scale; leading with these metrics immediately signals fit for their infrastructure-grade PM role |
| Intuit role reordered to lead experience section (above Streamio/Fintellect founders) | Intuit is the strongest proof of developer platform PM at scale — directly maps to Baseten's API/SDK/DevPortal surface; founder roles support but don't lead |
| Intuit bullets reordered to lead with 675M+/50K TPS scale bullet, then DevPortal onboarding, then SDK Starter Kits | JD emphasizes onboarding, observability, and platform adoption — scale metrics first establish credibility, then tooling specifics |
| Streamio OpenClaw bullet reframed to reference 'multi-component AI workflows analogous to Baseten's Chains initiative' | Chains for multi-component workflows is a named JD example initiative; OpenClaw is the accurate analog |
| Streamio HLS pipeline bullet reframed as 'production-grade async inference architecture' | Asynchronous inference is a named JD example initiative; the pipeline is an accurate technical analog |
| Fintellect multi-provider LLM orchestration bullet reframed to reference 'production model serving across frontier models' | Baseten's Frontier Gateway and model API surface are core product areas; Fintellect's routing experience is the accurate proof point |
| RL Workbench project moved to lead the projects section and bullet references 'directly analogous to Baseten Loops' | Baseten Loops (May 2026) is their training SDK for frontier RL workloads — the RL Workbench is the single strongest builder-credibility proof point for this role |
| Bank of America role removed from experience section | Zero relevance to developer tools/ML platform PM role; space better used for technical bullets; role is not cut from candidate history, just omitted per space constraints |
| Splunk Scheduler Service bullet reframed to emphasize 'asynchronous scheduled search capabilities' | Asynchronous inference is a named JD initiative; Scheduler Service is the accurate historical analog |
| aeval project bullets reframed to emphasize 'full observability and management experience for model inference' | JD explicitly calls out 'onboarding, observability, and management experiences' as a core responsibility area |
JD analysis (18 key phrases)
Key phrases: core developer experiencebuild, deploy, and manage AI applicationsuser-facing APIs, SDKs, UI workflowsML infrastructureseamless, intuitive productasynchronous inferencemodel training built for production inferencemulti-component workflowsonboarding, observability, and management experiencesproduct adoption and retentiondeveloper toolsML platformsfrontier modelsproduct-led growthtechnical usersinference productscross-functional alignmentproduct success metrics
Hard requirements:
- 4+ years PM experience in developer tools, SaaS, or ML platforms
- Engineering background (CS, EE, or equivalent hands-on software engineering)
- Deep empathy for developers; translate technical complexity into simple, usable products
- Cross-functional alignment across design, engineering, and GTM
- Define product success metrics and iterate based on data and feedback
Preferred qualifications:
- Familiarity with AI/ML workflows and inference products
- Experience building products for technical users (APIs, SDKs, dashboards)
- Background in developer experience or product-led growth
Per-role mapping (9 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Intuit — Staff PM, Developer Frameworks & Platform Infrastructure | 5/5 | Developer platform PM who scaled inference-grade infrastructure and shipped SDKs/DevPortal that directly parallel Baseten's core product surface | user-facing APIs, SDKs, UI workflows, core developer experience, onboarding, observability, and management experiences, product adoption and retention, ML platforms, developer tools |
| Streamio AI — Founder & CEO | 4/5 | Builder-founder who shipped production AI inference pipelines and multi-agent orchestration — directly analogous to Baseten's Chains and async inference initiatives | multi-component workflows, build, deploy, and manage AI applications, asynchronous inference, seamless, intuitive product |
| Fintellect AI — Founder & CEO | 4/5 | Production LLM routing and multi-provider inference orchestration — builder credibility for Baseten's Frontier Gateway and model API surface | frontier models, inference products, product-led growth, build, deploy, and manage AI applications |
| RL Workbench — Post-Training RL Platform | 5/5 | Hands-on RL training platform builder — gives PM credibility for Baseten's 'model training built for production inference' initiative | model training built for production inference, ML platforms, inference products, frontier models |
| aeval — AI Model Evaluation Platform | 4/5 | Observability and evaluation platform builder — maps to Baseten's onboarding, observability, and management experience roadmap | onboarding, observability, and management experiences, product success metrics, ML platforms |
| Splunk — Senior PM, Search Orchestration | 3/5 | Developer-facing platform PM with async execution and performance optimization track record | asynchronous inference, developer tools, cross-functional alignment |
| Kaiser Permanente — SOA Technical PM | 2/5 | Platform infrastructure at enterprise scale | ML platforms, product adoption and retention |
| IBM — Software Engineer | 2/5 | Engineering foundation underpinning PM credibility | engineering background |
| Bank of America — Tech MBA Associate | 1/5 | Quantitative analytical foundation | — |
Tailored summary
Technical Product Manager with 12+ years building developer-facing APIs, SDKs, and ML platform infrastructure — scaling ICE to 675M+ engagements and 50K TPS at sub-25ms TP99 (Intuit), and shipping a full RL post-training workbench benchmarking GRPO/DPO across TRL, VeRL, OpenRLHF, and NeMo RL. Deep builder credibility in production inference pipelines, multi-provider LLM orchestration, and multi-component AI workflows. NeurIPS published researcher; B.S. Computational Engineering Science, UC Berkeley.