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Tier-1 employer, metro location, and a generalist senior ML title increase applicant competition.
Core MLOps and ML engineering skills transfer across industries, but biotech domain knowledge adds bias.
Explicit senior years plus required MLOps, production ML, and domain qualifications increases screening strictness.
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Design, build, and deploy production-grade ML services, APIs, and applications integrating predictive models into biologics discovery platforms.
Establish and maintain MLOps foundations including experiment tracking, model/version management, CI/CD pipelines, monitoring, and troubleshooting to ensure reliability and scalability.
Collaborate with scientists, ML researchers, and engineers to transform research prototypes into scalable, tested, and maintainable ML production services.
Doctorate degree with 4+ years or Master's degree with 8+ years in Data Science, Computer Science, Computational Biology, Bioinformatics, Computational Chemistry, or related field.
Hands-on experience with production ML systems, model-serving platforms, MLOps tools (MLflow, CI/CD), Docker, Kubernetes, and cloud-native deployment.
Strong Python programming skills including software engineering best practices (testing, code review, documentation, packaging, version control).
Work Experience Required: Minimum 4 years (for PhD holders) or 8 years (for Master's holders); Notice period: Not explicitly mentioned in the JD.
Experienced in building and supporting scalable production ML systems with strong MLOps expertise and tooling such as experiment tracking, model lifecycle management, and observability.
Comfortable collaborating cross-functionally with ML scientists, software engineers, and platform teams to translate research into operational ML services.
Demonstrates strong ownership with contributions evidenced through production ML deployments, open-source MLOps projects, or publications at ML research forums like MLSys, NeurIPS, ICML, or ICLR.