Backed by a16z, GV and Lux.
About the role
Sr. Machine Learning Engineer . 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.
What they're looking for
- 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
More about this role
Sr. Machine Learning Engineer . 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.
Job Type: Full Time
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...
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