Senior Machine Learning Engineer

Dyno Therapeutics

Senior ML Engineer building scalable training, inference, and agentic AI systems at Dyno Therapeutics. Advancing AI-driven genetic medicine through protein design research infrastructure and GPU optimization.

Posted 8/18/2026full-timeRemote • Massachusetts • 🇺🇸 United StatesSenior💰 $178,662 - $213,680 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building scalable and reliable machine learning systems, with a strong focus on GPU optimization, containerization using Docker and Kubernetes, and cross-functional collaboration. Proficient in translating research into robust tools while adhering to high standards of safety and performance.

Highest-signal resume keywords

Machine Learning Software DevelopmentGPU Profiling and OptimizationDocker and KubernetesDistributed Training with RayMLOps Practices

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Hard Skills

Object-Oriented DesignAPI DesignVersion ControlDependency ManagementPerformance EngineeringCustom KernelsTechnical System DesignModel MonitoringModel VersioningCI/CD

Soft Skills

Proactive Problem-SolvingCross-Functional CollaborationTechnical Direction Contribution

Tools & Technologies

DockerKubernetesRayCUDATriton

Industry Keywords

Agentic AIBioinformaticsProtein ModelingStructural BiologyScientific Computing

Tech Stack

Tools & technologies

DockerKubernetesNode.jsRay

About the role

Key responsibilities & impact

Build modular, generalizable, and portable ML training systems supporting protein design models.
Improve scalability, reliability, and performance of ML training and inference infrastructure.
Optimize model performance using GPU profiling, custom kernels, and accelerated computing frameworks.
Develop and standardize agentic AI workflows that increase research velocity while maintaining safety and reliability.
Translate research prototypes into robust, reusable tools and systems with AI scientists, protein engineers, and ML engineers.
Contribute across modeling, GPU-level optimization, distributed training, and multi-node orchestration.
Evaluate and adopt emerging ML engineering and agentic AI tools.
Support delivery and communication of Dyno’s work internally and externally.
Collaborate cross-functionally to drive results.

Requirements

What you’ll need

5+ years of professional experience building software for machine learning
Strong software engineering fundamentals, including object-oriented design, testing, version control, dependency management, and API design
Hands-on experience with Docker and Kubernetes for containerizing code in remote environments
Experience with large-scale distributed training or inference using Ray or a similar framework
Familiarity with ML performance engineering, including bottleneck identification, resource analysis, profiling, and 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, and documentation
Alignment with Dyno’s core values and high-expectation environment
Proactive problem-solving mindset
Preferred: professional or academic ML research/scientific computing experience
Preferred: internal platforms or developer tools experience
Preferred: MLOps tools and practices, including model monitoring, model versioning, CI/CD, and model registries
Preferred: GPU programming experience with CUDA, Triton, or similar tools
Preferred: proficiency with agentic and modern AI software-development tools
Preferred: exposure to biology, bioinformatics, structural biology, or protein modeling

Benefits

Comp & perks

Competitive compensation & equity
Annual performance-based bonus
Stock options
Comprehensive medical, dental, and vision coverage
401(k) plan
Flexible paid time off and holidays
On-campus gym membership
Onsite lunch
Commuter support
Company provided laptop
Mission-aligned, high-trust environment
Career-defining experience at the forefront of AI-driven genetic medicine