Principal Product Manager - Agentic SQA

Compensation: $170k – $250k

Who We’re Looking For: This role sits at a rare intersection: real code intelligence depth, real AI recency, and real product craft. Very few people hold all three - most who built the systems intuition became security researchers, compiler engineers, or founders instead of product managers. If you’re one of the few PMs who lived in code analysis, verification, or developer security tooling, or if your path ran through engineering or founding and you’re ready to drive product, this role was written for you.

What You’ll Do:

• Drive the Agentic SQA charter: partner with the SVP, IR Labs on vision and strategy, then run the day-to-day: shape the roadmap, sequence the work, and ship thin slices fast. You’ll own execution; big-call direction stays a shared decision.
• Land in mid-beta and accelerate it: you’ll arrive while the beta is live. Pick up the cohort, deepen the feedback loop, and turn real usage into the next set of capability releases.
• Talk to users: really talk to them - run continuous discovery with engineers, reviewers, platform leads, and QA owners. Sit in their workflows, watch how triage actually happens, and earn the trust of deeply technical audiences. Turn what you hear into crisp use cases and shipped changes, not survey decks.
• Work in a new way: AI has collapsed the distance between idea and working artifact, and we expect you to live in that collapsed space. Prototype directly in modern AI tooling (Cursor, Claude Code, v0, similar), wire up workflows, and put working artifacts in front of users in days, not sprints. The running prototype is the spec.
• Bring taste: when execution is cheap, judgment is the bottleneck. Know what a great developer tool feels like, hold a high bar for evidence quality and workflow fit, and be willing to say no to features that would make the product bigger but worse.
• Translate deep tech into value: partner daily with the engineering team to turn LLVM/IR, knowledge-graph, and agentic research spikes into scoped, testable features that engineers adopt.
• Own the evaluation loop: define what “good” looks like for a non-deterministic system (signal quality, triage usefulness, false-positive rate, evidence quality, reviewer confidence) and stand up the eval harnesses to measure it. Tie model and product metrics to user and revenue outcomes.
• Prioritize across horizons: balance quick-win improvements in the current beta workflow with longer bets in expanded bug class coverage, RAG grounding, guardrails, and the broader verification direction.
• Launch and iterate: drive trials, docs, release notes; validate product-market-fit and feed rapid iteration loops; run the path from beta to a live, installable, purchasable MVP.
• Hold the positioning: keep us honest about what Agentic SQA is (a staged, reviewable, evidence-first system) and what it isn’t yet (a one-click autonomous code fixer). Sharpen our differentiation across detection, diagnosis, and verification.
• Guard cost & safety: enforce token/compute budgets, sandboxing of analyzed code, secrets hygiene, guardrails against prompt-injection; partner on compliance (SOC2/GDPR, customer source-code confidentiality) and reliability SLOs.
• Drive GTM: shape pricing & packaging (seat/usage/value-based), product-led growth onboarding, and collateral; support sales, community, and exec/board storytelling.

What You Bring to the Table:

• Domain expertise in code intelligence: you’ve lived in static analysis, program analysis, SAST, fuzzing, verification, or compiler-adjacent products, ideally with C/C++ intuition. You might have built product at places like GitHub (CodeQL / Advanced Security), Snyk, SonarSource, Semgrep, Code Intelligence, Mayhem, or in the safety-critical tooling world—or built the analysis engines yourself.
• Experience building real AI products: you’ve shipped LLM-powered or agentic products to production, not just demos. You understand how these systems actually behave: non-determinism, evals, regressions, cost/latency tradeoffs, guardrails.
• A creative mindset for a new way of working: you’re fluent enough with modern AI coding tools (Cursor, Claude Code, similar) to prototype workflows, wire up agents, write small scripts, and ship working artifacts yourself. You treat AI as leverage, invent your own workflows, and don’t wait for permission. “Write the spec and wait” isn’t the job.
• Taste: strong, defensible opinions about what makes a developer tool great. You can tell the difference between a feature that demos well and one that survives daily use, and you know which details are worth fighting for.
• You can actually talk to users: a track record of real discovery with technical audiences: engineers respect you, open up to you, and tell you the truth. You convert conversations into product decisions.
• Proven 0→1 execution: shipped technical products from blank page to first paying customers; built backlogs, dashboards, evals, and working artifacts, not just decks. 5+ years in product management or equivalent product leadership from an engineering or founder path.
• Metrics & evaluation discipline: cohort analyses, experiment design, and evaluation suites that tie model performance to user and revenue outcomes—and that you’ve used to make scope and shipping decisions.
• GTM & monetization sense: experience with pricing/packaging, PLG funnels, and unit-economics; can run data-backed experiments.
• Leadership & influence: can brief execs, align engineering, and tell a clear story to customers and the community. Comfortable operating where the SVP holds final call on big-direction decisions.
• Startup mentality: bias for action, comfort with ambiguity, and willingness to do unglamorous work to move the needle.