Technical Lead, Machine Learning

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 As Technical Lead, Machine Learning, you own the execution layer of the company's intelligence — translating research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product: you're responsible for making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, deployment Fine-tune and adapt models using state-of-the-art methods — LoRA, QLoRA, SFT, DPO, distillation Architect and operate scalable inference systems, balancing latency, cost, and reliability Design and maintain data systems for high-quality synthetic and real-world training data Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products Make pragmatic trade-offs and ship improvements quickly, learning from real usage Work under real production constraints: latency, cost, reliability, and safety Requirements You've built or shipped real ML systems used by people, not just demos You're comfortable working with large models and understanding their failure modes You write strong, production-grade code and care about system correctness You're self-directed, pragmatic, and take full ownership of outcomes You communicate clearly and collaborate well in small, high-trust teams Tech Stack Python · PyTorch / JAX · GPU-based training and inference systems What Success Looks Like Research and models reliably translate into production-ready solutions with clear performance and quality targets ML pipelines, training loops, and inference systems are stable, efficient, and maintainable Production issues are detected, debugged, and resolved quickly, minimizing user impact Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction Iterations on models and systems are measurable, safe, and improve user experience over time 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.