At Lightning AI, docs aren't just a support function—they're a product surface. They're often the first real experience a developer has with our platform, and in a PLG motion, that means they directly drive activation, retention, and revenue.

We're looking for a Technical Writer, Developer Experience who treats documentation that way: with product instincts, editorial craft, and the drive to own it end-to-end.

What You'll Own

The docs product. Audit what exists, cut what doesn't serve developers, and build a roadmap for what's missing. You'll own information architecture, structure, and coverage across Lightning Studios (AI Dev Platform) and Lightning Deploy (Inference Platform) — each with a UI, SDK, and CLI.

The getting-started experience. Our PLG flywheel runs through docs. You'll own the entry point where developers go from zero to value, and you'll treat time-to-first-success as a metric worth optimizing.

Voice and editorial standards. You'll uphold the bar for how we write — across docs, landing pages, and technical blog posts — and work directly with Product and Marketing leadership to expand our presence.

Cross-functional execution. You'll sit close enough to code to be accurate and far enough out to write for humans. Engineers, product managers, designers — you'll extract what needs to be written and make it good.

Community content strategy. You'll build an approach to docs that scales beyond what we ship internally.

What We're Looking For

Required

• 5+ years of technical writing experience at a company where docs were treated as a product, not an afterthought
• Demonstrated experience owning developer documentation end-to-end — SDK references, CLI guides, API docs, getting started flows
• Comfortable reading code; you don't need to ship it, but you need to understand it well enough to explain it accurately
• Strong information architecture instincts — you have opinions on structure, progressive disclosure, and example density, and you can defend them
• Experience collaborating directly with engineering and product teams to extract, shape, and ship content on release timelines
• Proficiency with modern docs tooling (Mintlify, Docusaurus, ReadMe, or similar)
• Actively uses AI tools (Claude, Cursor, or similar) to accelerate writing, research, and content production — this isn't optional; it's how we expect this role to scale

Nice to Have

• Experience at a developer-led or PLG company where docs were a measurable part of the growth motion
• Familiarity with ML/AI tooling, inference platforms, or developer infrastructure
• Experience building or selecting docs tooling, not just using it
• Background in technical blogging, developer education, or community content
• Experience growing or mentoring a writing team

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 field (or equivalent practical experience)
• Bonus: Prior experience at a neocloud, hyperscaler inference team, or AI infrastructure startup; familiarity with the PyTorch/Lightning ecosystem; background in GPU cluster products or consumption-based infrastructure pricing

We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits.