Health Science AI and ML Engineer

Job Number: R0234211

Are you looking for an opportunity to make a difference and help build innovative systems that will have a positive impact on how scientific systems are delivered? What if you could find a position that is tailor‑made for your mix of science, AI, ML, and engineering skills? Scientific AI and ML engineers play a critical role in advancing research and operational innovation through cutting‑edge applications of AI and ML.

That's why we need an experienced AI and ML engineer like you to help us design and develop solutions that combine domain expertise, scientific rigor, and ML techniques to deliver actionable insights and support mission‑critical operations.

As a Scientific AI and ML Engineer on our team, you'll apply your deep technical expertise to create innovative AI and ML algorithms, frameworks, and tools to address complex scientific challenges. You'll collaborate with a multidisciplinary Agile development team to develop, validate, and optimize AI and ML models and workflows. Whether it's designing novel algorithms, enabling automation of AI workflows, or scaling solutions in a cloud environment, you'll have the opportunity to pioneer new approaches while improving scientific outcomes.

As a critical member of the team, you'll identify opportunities for leveraging AI and ML to solve real‑world problems, guide efforts to mitigate risks, optimize resources, and improve lives.

Work with us to apply the power of AI and ML to solve scientific problems at the intersection of health and technology, and protect America from health, safety, and security threats.

• Develop and optimize novel AI and ML algorithms tailored to scientific challenges, integrating domain knowledge to ensure results are actionable and relevant.
• Design, validate, and deploy end‑to‑end AI and ML workflows in cloud environments to address complex analytical needs across the organization.
• Collaborate with cross‑functional teams to design efficient frameworks for data preparation, feature engineering, model selection, and outcome interpretation across data sources.
• Build tools and infrastructure to enable seamless experimentation, rapid model iteration, and reproducibility of scientific AI and ML experiments.
• Scale AI and ML solutions using advanced techniques such as distributed computing, cloud environments, including Azure and Databricks, and containerized deployments.
• Implement automated pipelines for training, validating, and deploying models into production with rigorous monitoring and evaluation processes.
• Develop containerized applications and APIs for exposing AI and ML model capabilities, ensuring accessibility and interpretability for stakeholders.
• Identify and introduce state‑of‑the‑art AI and ML techniques and tools such as explainable AI (XAI), reinforcement learning, and probabilistic modeling, to enhance research outcomes and operational decision‑making.
• Support collaboration with data scientists, researchers, and engineers to bridge the gap between foundational AI and ML research and deployed, impactful applications.

• 5+ years of experience in bioinformatics or computational biology, including analysis and processing of biological and imaging data, such as FASTQ, BAM, CRAM, VCF, or DICOM.
• 3+ years of experience across data science, AI, and data engineering with ownership of end‑to‑end analytical or ML solutions.
• Experience designing and deploying AI and ML solutions, including model training, evaluation, and production.
• Experience with cloud‑based AI platforms, including Databricks, AWS, or Azure ML.
• Knowledge of Python or R for data analysis, modeling, and pipeline development.
• Ability to translate complex biological questions into analytical approaches and apply existing methods to novel datasets.
• Ability to work independently, lead technical initiatives, and deliver in a fast‑paced, evolving environment.
• Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements.
• Bachelor's degree in a Science field.

• Experience with PySpark or Spark
  
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• Experience with Kubernetes or another container management…