Senior Product Manager, Inference

Who We Are Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction. Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in. We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute. Our Values Move Fast : We act with speed and precision, breaking down big challenges into achievable steps. Focus : We complete one goal at a time with care, collaborating as a team to deliver features with precision. Balance : Sustained performance comes from rest and recovery. We ensure a healthy work-life balance to keep you at your best. Craftsmanship : Innovation through excellence. Every detail matters, and we take pride in mastering our craft. Minimal : Simplicity drives our innovation. We eliminate complexity through discipline and focus on what truly matters. What We're Looking For We're looking for a Founding Product Manager for Inference who will own this product end-to-end—roadmap, pricing, and GTM—from the ground up. This is a zero-to-one role at the intersection of deep technical fluency and commercial instinct. You'll define what we build and why, design the developer journey from first API call to production workload, and be the product voice in sales cycles and the market. The right candidate has lived inside the machine; they've operated model serving infrastructure or shipped products on top of it, and can move fluidly between a latency/throughput tradeoff conversation with an infra engineer and a positioning conversation with a sales lead. You will be joining the Product Team and report to our VP of Product working directly with our executive team as we grow this business.  This is a hybrid role based in our New York City or San Francisco office with in-office requirements of 2 days per week.  What You’ll Do Define Lightning AI's inference product vision and roadmap — what we build, what we don't, and in what order — translating the competitive landscape (vLLM, Together, Fireworks, Modal, hyperscaler inference APIs) into a differentiated strategy grounded in Lightning's compute and software advantage Own inference pricing and packaging end-to-end: design the model (per-token, per-second, reserved capacity), run pricing experiments with Growth and Finance, and define the tiers that convert self-serve developers into enterprise contracts Be the product voice in GTM: develop sales positioning, answer technical objections in the field, and partner with Marketing on the benchmarks, reference architectures, and developer content that builds credibility with ML engineers and platform teams Own the developer journey from API key to production-scale deployment — identify and remove friction across onboarding, documentation, SDK ergonomics, and dashboard observability Lead experiments across activation flows, pricing pages, and upgrade prompts; track and move DAU/MAU, Time to Value, Activation %, PQLs, and expansion revenue Partner with engineering to write tight specs and make fast build/buy/partner decisions; collaborate across Product to ensure inference coheres with training, fine-tuning, and storage surfaces Establish inference-specific metrics — throughput, latency SLAs, cold-start behavior, cost per token — and build the instrumentation to track them What You’ll Need 7+ years of product management experience, with at least 3 years in infrastructure, platform, or developer tooling products Direct, hands-on experience with model serving or inference infrastructure — you've shipped in this space; you understand quantization, batching strategies, KV cache, and speculative decoding at a level that lets you go deep with ML engineers Proven track record owning product pricing and packaging decisions, not just feature decisions — you've modeled unit economics and made calls that affected margin Experience with a PLG or trial-to-paid motion in a developer product; you know how to build self-serve growth loops and run rigorous A/B experiments Strong analytical skills — comfortable with product instrumentation, metrics, and dashboards; you pull your own data Excellent written and verbal communication; you can write a crisp one-pager, a technical spec, and a customer-facing benchmark brief with equal fluency Bias for action and comfort operating with high ambiguity in a fast-moving environment Bachelor's degree in Computer Science, Engineering, or related technical