Senior Machine Learning Engineer

Key Responsibilities: Design, implement, and optimize state-of-the-art generative models (e.g., VAEs, GANs, diffusion models) for real-world applications. Write production-quality Python code, develop reusable model components, and maintain clean, well-tested repositories. Lead end-to-end ML projects: data preprocessing, model training, hyperparameter tuning, evaluation, and deployment. Collaborate closely with research scientists and domain experts to translate scientific objectives into scalable ML solutions. Build and maintain data pipelines and infrastructure to support large-scale training and inference workloads. Deploy and monitor ML models in production using MLOps best practices (CI/CD, containerization, monitoring, and rollback).Profile and optimize model performance and inference latency for CPU/GPU environments, including mixed-precision and model compression techniques. Mentor and review code for junior engineers, contribute to team standards, and evangelize reproducible research practices. Document models, experiments, and deployment procedures to ensure cross-team transparency and knowledge transfer.

Qualifications: Strong background in Machine Learning with 5+ years of industry or research experience building ML systems. Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, JAX) and ecosystem tools. Demonstrated experience designing and training Generative Modeling architectures (VAEs, GANs, diffusion models, autoregressive models, etc.).Solid understanding of core ML fundamentals: probability, optimization, representation learning, and model evaluation. Experience deploying ML models to production, familiarity with MLOps tools and workflows (Docker, Kubernetes, CI/CD, model monitoring).Proven software engineering skills: version control, testing, code reviews, and clear documentation. Experience working with large datasets, feature engineering, and scalable data pipelines (Spark, Airflow, or similar) is preferred. Strong communication skills and experience collaborating with cross-functional teams to deliver measurable results.

Nice to have: experience with Biomolecular Simulation or applying ML to molecular/biophysical problems.

Nice to have: experience with cloud platforms (AWS, GCP, or Azure) and GPU-accelerated training environments.

Benefits: Vacation/PTOMedicalDentalVision 401kBonusRelocation