Machine Learning Lead Engineer, Machine Learning

• Own the end-to-end Machine Learning (ML) systems powering the company, from data and training to 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 and systems for high-quality real-world and synthetic training data
• Establish reliable production infrastructure for deploying, monitoring, and continuously improving models
• Partner with research and application engineering to turn model capabilities into product improvements
• Make pragmatic technical trade-offs and rapidly iterate based on real-world performance
• Detect, debug, and resolve production issues quickly while minimizing user impact
• Support and align team members to deliver high-impact ML work

• Machine Learning (ML) experience is required
• Experience building and shipping Machine Learning (ML) systems used in production, not just research prototypes
• Strong understanding of modern large-model training, fine-tuning, evaluation, and inference
• Strong software engineering and systems fundamentals
• Experience operating Machine Learning (ML) workloads at meaningful scale, particularly GPU-based systems
• Strong technical judgment and the ability to navigate ambiguous problems independently
• A bias toward experimentation, measurement, and shipping
• High standards for correctness, reliability, and production quality
• Python, PyTorch/JAX, and GPU-based training and inference system
• Must be willing to take a 60 minute coding assessment

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