SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Dyno Therapeutics is seeking a Senior Machine Learning Engineer to build and scale ML infrastructure, tools, and workflows that power research efforts in AI-driven genetic medicine. This is a hybrid role based in Watertown, MA with remote flexibility.
You will partner closely with AI scientists, protein engineers, and fellow ML engineers to translate novel research into robust, reusable systems. Key responsibilities include:
• Design and build modular, generalizable ML training systems supporting protein design model development
• Improve scalability, reliability, and performance of ML training and inference infrastructure for rapid experimentation
• Optimize model performance using GPU profiling, custom kernels, and modern accelerated computing frameworks
• Develop and standardize agentic AI workflows that increase research velocity while maintaining safety and reliability
• Contribute across the full ML engineering stack—from modeling and GPU-level optimization to distributed training and multi-node orchestration
• Evaluate and adopt emerging ML engineering and agentic AI tools to advance research capabilities
• Work cross-functionally in a high-trust, high-impact culture balancing innovation with execution
Required qualifications:
• 5+ years professional experience building software for machine learning
• Strong software engineering fundamentals (OO design, testing, version control, dependency management, API design)
• Hands-on experience with Docker and Kubernetes for containerizing code
• Experience with large-scale distributed training/inference (Ray or similar)
• ML performance engineering expertise (bottleneck identification, resource analysis, profiling, custom kernels)
• Track record designing and owning technically complex systems through full lifecycle
• Ability to contribute to technical direction via design reviews, planning, and documentation
• Proactive problem-solving mindset and alignment with high-expectation culture
Preferred qualifications include ML research or scientific computing background, internal platform/developer tools experience, MLOps familiarity, GPU programming (CUDA, Triton), proficiency with agentic AI tools, and exposure to biology or protein modeling.
