Lead Engineer, Machine Learning

Role overview Lead the engineering work that turns machine learning research and model capabilities into dependable production systems. This is a hands-on role spanning the model lifecycle—from data and training to evaluation, inference, and deployment—with close ties to research and product engineering.

Responsibilities - Own the systems supporting ML development and production, including data pipelines, training, evaluation, inference, and deployment. - Build and improve training and fine-tuning pipelines for large models. - Create evaluations for capability, robustness, safety, and real-world product performance. - Design efficient inference systems, balancing latency, GPU use, memory, cost, and reliability. - Establish infrastructure for deployment, monitoring, and ongoing model improvement. - Work with research and application engineers to translate model advances into product improvements, using measured results to guide iteration.

Requirements - Track record building and shipping production ML systems used by people. - Strong understanding of large-model training, fine-tuning, evaluation, and inference. - Strong software engineering and systems fundamentals. - Experience operating ML workloads at meaningful scale, especially GPU-based workloads. - Sound technical judgment, independence in ambiguous situations, and a bias toward measured experimentation and delivery. - High standards for correctness, reliability, and production quality.

Nice to have - Experience with Python, PyTorch or JAX, and GPU-based training and inference systems. - Comfort working with large models and understanding their failure modes.

Benefits and work setup The role is part of a small, hands-on team with broad engineering ownership and quick decision-making. Engineers are expected to work independently, collaborate closely, and balance fast iteration with sound engineering practices.