Technical Lead, Machine Learning

Role overview Lead the machine learning systems that bring large-model research into dependable production capabilities for a proactive communication product. The scope covers data, training, evaluation, inference, and deployment, with hands-on technical leadership at the intersection of research, systems, and application engineering.

Responsibilities - Own end-to-end machine learning systems, from data and training through evaluation, inference, and deployment. - Build and improve training and fine-tuning pipelines for large models, including systems for real-world and synthetic data. - Develop evaluations for capability, robustness, safety, and performance in product use. - Architect inference services with attention to latency, GPU utilization, memory, cost, and reliability. - Set up production infrastructure for deployment, monitoring, and ongoing model improvement. - Partner with research and application teams, using measured outcomes to guide iterations and address production problems.

Requirements - Experience building and shipping machine learning systems used in production, beyond research prototypes. - Strong understanding of large-model training, fine-tuning, evaluation, and inference. - Strong software engineering and systems fundamentals, with high standards for correctness and reliability. - Experience operating machine learning workloads at meaningful scale, especially GPU-based systems. - Technical judgment and ability to navigate ambiguous problems independently. - A bias toward experimentation, measurement, and shipping.

Nice to have - Experience with Python and PyTorch or JAX, and familiarity with large-model failure modes.

Benefits and work setup The team is small and hands-on, with broad ownership, close collaboration, and quick decisions. Interviews generally involve three or four conversations, held virtually and/or onsite.