Senior Machine Learning Engineer

Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building. About the Role In this senior role, you're an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale. This is a hands-on, high-impact role focused on depth. What You'll Work On Build core ML systems that power a proactive, long-horizon AI product Own work end-to-end: data preparation, training, evaluation, inference, iteration Turn research ideas into working systems that run reliably in production Debug model failures and system issues using real production signals Iterate quickly: ship, measure outcomes, refine, repeat Collaborate closely with research, product, and engineering to deliver real user impact Mentor and review work from other ML engineers through example and technical judgment Work under real production constraints: latency, cost, reliability, and safety Requirements You've built and shipped ML systems used by real users You understand how modern ML models behave — and misbehave — in production You write strong, production-quality code and think in systems, not scripts You take ownership, work independently, and push work across the finish line You learn fast, communicate clearly, and improve through iteration Tech Stack Python · PyTorch / JAX · GPU-based training and inference systems What Success Looks Like ML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets Complex production issues are monitored, debugged, and resolved with minimal disruption Training, inference, and data pipelines are robust, scalable, and maintainable over time Measurable improvements in ML systems driven by real-world signals and user feedback Mentorship and technical guidance raise the overall ML engineering standard Cross-functional collaboration ensures ML features integrate seamlessly into products and meet business goals What to Expect The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently. Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process. If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision.