Technical Lead, Machine Learning

Job Title: Technical Lead, Machine LearningLocation: RemoteJob Type: Full timeAbout Company There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.Role As Technical Lead, Machine Learning, you own the execution layer of Company’s intelligence. You translate research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product. You are 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, and deployment.Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and 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 safetyOutcomes 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.Tech Stack PythonPyTorch / JAXGPU-based training and inference systemIdeal Experience You have built or shipped real ML systems used by people, not just demos.You are comfortable working with large models and understanding their failure modes.You write strong, production-grade code and care about system correctness.You are self-directed, pragmatic, and take full ownership of outcomes.You communicate clearly and collaborate well in small, high-trust teams.Applicant Notices & DisclaimersFor information on benefits, equal opportunity employment, and location-specific applicant notices, click here