ML Engineer

NOETIK is a frontier therapeutics company using AI to advance oncology drug discovery. The ML Engineer builds and operates distributed model training, batch inference, and researcher-facing infrastructure, while improving model performance and supporting ML researchers.

Responsibilities:

• Design, build, and evolve systems for distributed model training and batch inference as Noetik’s workloads grow in scale and complexity
• Develop workflows for experiment tracking, model evaluation, and model registry management that support reproducibility across the ML lifecycle
• Profile and optimize training and inference performance across PyTorch model code, data loading, GPU utilization, memory usage, distributed communication, and checkpointing
• Build researcher-facing CLIs, SDKs, and job submission tools that make it easy to move from experimentation to large-scale runs with minimal infrastructure knowledge
• Develop and operate reliable, observable, and cost-efficient GPU infrastructure using technologies such as Kubernetes, AWS, Ray, Kueue, Docker, Terraform, and high-performance networking
• Work directly with ML researchers to troubleshoot issues, understand evolving research needs, and translate them into platform improvements that support rapid iteration
• Use modern AI development tools and agents throughout the engineering workflow to accelerate implementation, debugging, profiling, testing, exploration, and maintenance