Senior Machine Learning Engineer, Dyno Therapeutics

• A research-minded machine learning engineer who thrives at the intersection of cutting-edge AI, scientific research, and rigorous engineering
• This role is for someone excited to build and scale the models, infrastructure, and AI tools that accelerate Dyno’s work at the frontier of AI-driven genetic medicine
• 5+ years professional experience building software for machine learning
• Strong software engineering fundamentals (OO design, testing, version control, dependency management, API design)
• Experience containerizing code for remote environments including hands-on experience with Docker and Kubernetes
• Experience with large-scale distributed training or inference with Ray or a similar framework
• Familiarity with ML performance engineering (identifying bottlenecks, analyzing resource usage, profiling, writing custom kernels)
• Experience designing and owning technically complex systems through requirements-setting, implementation, rollout and maintenance
• Ability to contribute to technical direction through design reviews, cross-team planning, documentation, etc
• Alignment with Dyno’s core values - We seek individuals who step up when things get tough, recalibrate when priorities shift, and thrive in a high-expectation environment
• A proactive, problem-solving mindset - you don’t just identify challenges; you find solutions

Desirable

• As a Sr. Machine Learning Engineer, you will build and scale the ML infrastructure, tools, and workflows that power Dyno’s research efforts
• You will partner closely with AI scientists, protein engineers, and fellow ML engineers to turn novel research into robust, reusable systems—improving model training and inference, optimizing performance, and advancing agentic AI workflows that accelerate scientific discovery
• At Dyno, every role is mission-driven. Whether in science, engineering, operations, or business, each AAViator contributes to solving some of the most complex challenges in genetic medicine
• Build modular, generalizable, and portable ML training systems that support the ongoing development of protein design models
• Improve the scalability, reliability, and performance of ML training and inference infrastructure to enable rapid research experimentation
• Optimize model performance using tools such as GPU profiling, custom kernels, and modern accelerated computing frameworks
• Develop and standardize agentic AI workflows that increase research velocity while maintaining appropriate safety and reliability
• Partner closely with AI scientists, protein engineers, and other ML engineers to translate research prototypes into robust, reusable tools and systems
• Contribute across the ML engineering stack, from modeling and GPU-level optimization to distributed training and multi-node orchestration
• Stay current on emerging ML engineering and agentic AI tools and help the team evaluate and adopt approaches that advance Dyno’s research
• Support the delivery and communication of Dyno’s work, both internally and externally
• Work with urgency and adaptability, balancing innovation with execution
• Collaborate cross-functionally, leveraging Dyno’s high-trust, high-impact culture to drive results