ML Engineer

Mission

• Develop, train, and deploy machine learning models to solve complex problems in materials science and lab automation.
• Experiment with cutting-edge ML algorithms and techniques to improve performance and scalability.
• Collaborate with researchers on the pre- and post-training stages of model development.
• Conduct production-level deployments of our machine learning models and infrastructure.
• Continually evaluate model performance and maintain production integrity.
• Ensure reproducibility and traceability of models by leveraging version control systems and orchestration tools.
• Contribute to research initiatives and participate in knowledge-sharing across teams.
• Document workflows, experiments, and deployment procedures for future reference.
• Tackle challenging problems with new and different ideas, creativity and contrarian thinking.
• Mentor and guide junior team members and interns, promoting an environment of continuous learning and innovation.

About you

• Ph.D., M.S., or B.S. in Computer Science, AI, Machine Learning, Applied Maths, Engineering or a closely related field, or equivalent practical experience.
• Proven record of accomplishment in building machine learning systems, working with large generative and/or language models
• An excellent understanding of machine learning infrastructure and systems fundamentals.
• Strong background in algorithms, data structures, and software engineering principles.
• Working knowledge of various generative AI architectures, including transformers, diffusion models, CNFs, and flow matching.
• Proficiency in Python and relevant ML libraries (e.g., PyTorch), as well as tools for deploying ML models (e.g., MLflow, K8s, Docker).
• Passion for staying current with the latest trends in AI/ML technologies.
• Excellent collaboration and communication skills, capable of articulating complex technical ideas clearly and effectively.

Pluses

• Familiarity with distributed computing frameworks such as Ray, Spark, etc.
• Strong fundamental knowledge and practical experience using Linux systems, including working with large-scale computing clusters.
• Hands-on experience with MLOps tools (e.g., Kubeflow, Seldon, CI/CD pipelines for ML).
• Familiarity with serverless architecture and cost optimization for ML workflows.
• Strong publication record in top-tier AI/ML conferences or journals.

What we offer

• A competitive compensation package also includes the best in benefits:
• Medical, dental, and vision insurance for you and your family
• Mental health and wellness support
• Unlimited PTO and 14+ company holidays per year
• 401K 
• Work closely with a team on the cutting edge of AI research.
• A mission: an opportunity to fundamentally change the way humanity makes progress through materials science discovery.