Machine Learning Software Engineer
Skills
- Production ML systems design and deployment
- Uncertainty quantification, active learning, and Bayesian optimization
- Proficiency in ML frameworks (PyTorch, TensorFlow, JAX) and Python ecosystem
- Experience with MLOps practices: version control, CI/CD, monitoring
- Cloud experience (AWS, GCP, or Azure) for training, deployment, and inference
- Experience with chemoinformatic libraries or working with chemical/biological datasets
- API/microservice integration between models and experimental platforms
Responsibilities
- Design, build, and deploy production-grade ML models for optimization of LNP formulation, synthesis, and in vivo performance
- Develop and implement uncertainty quantification methods to support real-time decision-making
- Engineer robust data pipelines to integrate multi-fidelity datasets (in silico and in vivo)
- Translate research models into maintainable software in collaboration with computational scientists
- Architect data transfer and model integration with experimental automation and infrastructure teams
- Partner with experimental teams to run iterative design–make–test–analyze (DMTA) cycles
- Communicate technical results and progress to stakeholders and leadership through reports and presentations
Education
- PhD or Master's in Computer Science, Applied Mathematics, Bioengineering, Chemical Engineering, or related quantitative field with strong ML and software engineering focus
Benefits
- Healthcare coverage
- Annual incentive program
- Retirement benefits
- Broad range of additional employee benefits
- Competitive compensation and potential future benefits updates
Average salary estimate
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