Responsibilities
- Develop and implement a strategic AI and machine learning roadmap aligned with the NIH's 2025–2030 Data Science priorities.
- Define a forward-looking vision for incorporating Generative AI, Large Language Models, and Agentic Workflows into federal research settings.
- Lead the transformation of the Polus platform from an image analysis tool into a comprehensive, multi-modal research environment.
- Manage the development roadmap for new features, ensuring scalability, security, and interoperability across AWS, GCP, and Azure using Docker and Kubernetes.
- Create and enforce strong AI governance policies across all initiatives.
- Implement the NIST AI Risk Management Framework to uphold fairness, transparency, and compliance with federal ethical standards.
- Lead assessments to identify gaps and manage risks, ensuring trustworthy and auditable AI deployments.
- Oversee the creation of high-performance data pipelines capable of processing petabyte-scale datasets in genomics, proteomics, and electronic health records.
- Ensure systems comply with FAIR principles—Findable, Accessible, Interoperable, Reusable—to support data sharing across NIH and global research institutions.
- Lead the development of predictive models to improve drug discovery and clinical trial design by reducing technical and operational risks.
- Guide teams in applying deep learning to discover molecular targets, forecast treatment outcomes, and model clinical scenarios using Digital Twins.
- Enhance MLOps and DevSecOps practices to accelerate secure model deployment from prototype to production.
- Promote an automation-first mindset, reducing analysis time for researchers by streamlining workflows from notebooks to cloud-native services.
- Collaborate with Growth and Capture teams to support business development and new contract acquisition.
- Serve as Lead Solution Architect for major proposals exceeding $50 million, defining technical approaches and creating persuasive solution designs.
- Author critical sections of technical proposals, including methodology, staffing, and risk mitigation strategies.
- Convert complex federal requirements into compelling, evaluator-friendly narratives that align with scoring criteria.
- Build and maintain strong relationships with federal decision-makers, including Project Officers, CIOs, and Lab Chiefs.
- Act as the primary technical point of contact, translating agency needs into actionable solutions and presenting them in competitive review settings.
- Foster a high-performance culture that integrates scientific expertise with agile engineering practices.
- Mentor and manage a multidisciplinary team of data scientists, bioinformaticians, and software engineers in a psychologically safe environment.
- Support ongoing learning and skill development to keep technical teams current with emerging trends.
- Launch internal Communities of Practice focused on AI and Data Science to advance knowledge in areas like Graph Neural Networks and Federated Learning.
- Expand access to AI tools for non-technical researchers through intuitive, low-code or no-code interfaces.
- Lead training initiatives that enable NIH researchers to independently use advanced analytics tools.
Benefits
- Full coverage of medical, dental, and vision insurance for employees
- Paid time off and paid public holidays
- 401K matching up to 5% of salary
- Career development and educational support programs
- Employee referral incentive program
- Healthcare Flexible Spending Account (FSA)
- Parking expense reimbursement account (PRK)
- Dependent care assistance program (DCAP)
- Transportation expense reimbursement account (TRN)
Compensation
Market-competitive salary with base range dependent on experience, qualifications, skills, and location.
Accessibility
Applicants requiring accommodations during the hiring process should contact careers@axleinfo.com.
Other
This position offers a market-competitive salary with a base range as listed; final compensation will vary based on experience, qualifications, skills, and geographic location.
