Lead Engineer, Machine Learning

About the role

The company builds proactive, agent-first mobile applications that bring intelligence to everyday communication, coordination and collaboration with minimal prompting. As Lead Engineer, Machine Learning, you own the execution layer of its intelligence, turning research and model capabilities into reliable, scalable production systems. You work across the model lifecycle: data, training, evaluation, inference and deployment.

Key responsibilities

• Own end-to-end ML systems, from data and training through evaluation, inference and deployment.
• Build and evolve training and fine-tuning pipelines for large models.
• Design evaluation systems measuring capability, robustness, safety and real-world product performance.
• Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost and reliability.
• Build data pipelines for high-quality real-world and synthetic training data.
• Establish production infrastructure for deploying, monitoring and continuously improving models.
• Partner with research and application engineering to turn model capabilities into product improvements.

Required profile

• Experience building and shipping ML systems used in production, not just research prototypes.
• Strong software engineering and systems fundamentals.
• Experience operating ML workloads at meaningful scale, especially GPU-based systems.
• Strong technical judgment, independence and a bias toward experimentation, measurement and shipping.
• Ideal: has built or shipped real ML systems used by people, understands large-model failure modes and writes production-grade code.

Required skills

• Python
• PyTorch / JAX
• Large-model training, fine-tuning, evaluation and inference
• GPU-based training and inference systems

Applications are evaluated by the technical team; interviews are virtual and/or onsite, and the interview process lasts 3 to 4 sessions.