Senior Machine Learning Engineer

This is a fully remote position, open to applicants in Massachusetts.

📋 Description

• Develop modular, adaptable, and portable machine learning training systems that support protein design models.

• Enhance the scalability, reliability, and performance of machine learning training and inference infrastructure.

• Optimize model efficiency through GPU profiling, custom kernels, and accelerated computing frameworks.

• Create and standardize agentic AI workflows that boost research velocity while ensuring safety and reliability.

• Transform research prototypes into robust, reusable tools and systems in collaboration with AI scientists, protein engineers, and ML engineers.

• Contribute to modeling, GPU-level optimization, distributed training, and multi-node orchestration.

• Assess and integrate emerging machine learning engineering and agentic AI tools.

• Facilitate the internal and external communication of Dyno's work.

• Work collaboratively across functions to achieve results.

⛳️ Requirements

• Over 5 years of professional experience in software development for machine learning.

• Strong foundation in software engineering principles, including object-oriented design, testing, version control, dependency management, and API design.

• Practical experience with Docker and Kubernetes for containerizing applications in remote environments.

• Familiarity with large-scale distributed training or inference technologies, such as Ray or similar frameworks.

• Knowledge of machine learning performance engineering, including bottleneck identification, resource analysis, profiling, and custom kernels.

• Proven experience in designing and managing technically complex systems throughout the requirements-setting, implementation, rollout, and maintenance phases.

• Capability to influence technical direction through design reviews, cross-team planning, and documentation.

• Alignment with Dyno's core values and a high-expectation environment.

• Proactive mindset for problem-solving.

• Preferred: experience in professional or academic ML research/scientific computing.

• Preferred: familiarity with internal platforms or developer tools.

• Preferred: experience with MLOps tools and practices, including model monitoring, versioning, CI/CD, and model registries.

• Preferred: GPU programming experience with CUDA, Triton, or similar technologies.

• Preferred: proficiency in agentic and modern AI software-development tools.

• Preferred: exposure to biology, bioinformatics, structural biology, or protein modeling.

🏝️ Benefits

• 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.