(ID: 2026-1573)
Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).
Axle is seeking a visionary and operationally astute Associate Director of Artificial Intelligence, Modeling, and Data to lead our charge at the frontier of biomedical innovation. In this pivotal leadership role, you will architect the future of AI-driven discovery, bridging the gap between advanced computational science and life-saving clinical research.
Sitting at the nexus of our Data Science and Biomedical Research divisions, you will oversee the strategic development and deployment of high-performance AI/ML solutions that empower federal health agencies, including the National Institutes of Health (NIH) and the National Center for Advancing Translational Sciences (NCATS). You will be the technical conscience and strategic driver for a diverse portfolio of projects, ranging from the optimization of our open-source Polus platform to the implementation of Generative AI pipelines for drug discovery and high-dimensional data analysis.
This is not merely a management role; it is a mission-critical position for a leader who is passionate about the "public good" of science. You will galvanize interdisciplinary teams of PhD scientists, machine learning engineers, and software architects to solve complex health challenges, such as pandemic preparedness and rare disease treatment. As an Associate Director, you will combine deep technical expertise with executive presence, serving as a trusted advisor to government stakeholders and a key architect of Axle's long-term growth in the federal health IT sector.
Visionary Architecture: Architect and execute a comprehensive AI/ML strategy that aligns Axle's technical capabilities with the NIH Strategic Plan for Data Science (2025–2030). You will define the long-term vision for integrating Generative AI, Large Language Models (LLMs), and Agentic Workflows into federal research environments, moving beyond static analysis to active, AI-assisted discovery.
Platform Evolution (Polus): Spearhead the evolution of the Polus platform, transitioning it from a robust image analysis tool into a fully integrated, multi-modal research ecosystem. You will oversee the roadmap for new feature development, ensuring scalability, security, and interoperability across cloud environments (AWS/GCP/Azure) using containerized architectures (Docker/Kubernetes).
AI Governance & Compliance: Establish and enforce rigorous AI Governance frameworks. You will operationalize the NIST AI Risk Management Framework (RMF) across all projects to ensure fairness, interpretability, and compliance with federal ethical standards. You will lead "Gap Analysis" and "Risk Management" exercises to ensure all AI deployments are trustworthy and transparent.
Petabyte-Scale Engineering: Direct the design and implementation of high-throughput data pipelines capable of ingesting and analyzing petabyte-scale datasets (genomics, proteomics, EHR). You will ensure these systems adhere to FAIR data principles (Findable, Accessible, Interoperable, Reusable), facilitating seamless data sharing across NIH institutes and global research centers.
Translational Science De-risking: Oversee the development of predictive models for translational science, focusing on "de-risking" drug discovery and clinical trial design. This involves guiding technical teams in the application of deep learning techniques to identify molecular targets, predict therapeutic outcomes, and simulate clinical scenarios (Digital Twins).
Operational Excellence (MLOps): Optimize MLOps and DevSecOps processes to ensure the rapid, secure deployment of models from prototype to production. You will champion a culture of "automation first," reducing time-to-insight for researchers by streamlining the transition from Jupyter notebooks to containerized, cloud-native services.
Lead Solution Architect: Partner with the Growth and Capture teams to drive new business acquisition. You will serve as the Lead Solution Architect for major proposal efforts ($50M+), authoring technical volumes, developing win themes, and creating compelling solution graphics that demonstrate Axle's technical differentiation.
Proposal Authorship: Personally write key sections of technical proposals, including the "Technical Approach," "Staffing Plan," and "Risk Mitigation" volumes. You will translate complex agency requirements into winning narratives that score highly with federal evaluators.
Client Liaison: Galvanize relationships with key federal stakeholders (Project Officers, CIOs, Lab Chiefs). You will act as the primary technical liaison, translating complex agency requirements into deliverable technical solutions and presenting these visions in competitive "Black Hat" sessions and oral presentations.
Interdisciplinary Team Building: Cultivate a high-performance, interdisciplinary team culture. You will manage and mentor a diverse group of data scientists, bioinformaticians, and software engineers, fostering an environment of psychological safety where "expert" scientific knowledge seamlessly integrates with "agile" engineering practices.
Continuous Learning: Drive continuous learning and upskilling initiatives. You will establish internal "Communities of Practice" for AI and Data Science, ensuring that Axle's workforce remains at the bleeding edge of technologies like Graph Neural Networks and Federated Learning.
Democratization of AI: Democratize access to AI tools within the client environment. You will lead efforts to create "low-code/no-code" interfaces and training programs that empower non-technical NIH researchers to utilize advanced analytics independently.
Education:
Ph.D. in Computer Science, Bioinformatics, Computational Biology, Data Science, or a related quantitative discipline is highly preferred to ensure peer-level credibility with NIH scientists.
Alternatively, a Master's degree in one of the above fields with exceptional, demonstrated leadership experience in a federal or research-intensive setting will be considered.
Experience:
8–10+ years of progressive experience in data science, AI/ML engineering, or computational biology, with a focus on high-dimensional data.
3–5+ years of leadership experience managing cross-functional teams (e.g., managing both PhD researchers and software developers) in a matrixed organization.
Federal Contracting Experience: Demonstrated experience with Federal Business Development, including writing technical proposals and supporting capture activities for contracts valued at $15M+.
Regulatory Experience: Proven track record of delivering complex AI/ML solutions in a regulated environment, with specific familiarity with HIPAA, FedRAMP, or NIST AI RMF compliance.
Core AI/ML: Expert-level understanding of Deep Learning frameworks (PyTorch, TensorFlow), Classical Machine Learning (Scikit-Learn), and Generative AI architectures (Transformers, LLMs, RAG).
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Languages: Proficiency in Python (primary) and R (secondary); familiarity with Java or C++ (for Polus backend optimization) is a strong plus.
Cloud Architecture: Extensive experience with Cloud-Native AI pipelines on AWS (SageMaker, HealthLake), GCP (Vertex AI, BigQuery), or Azure. Knowledge of the NIH STRIDES initiative and cloud economics is essential.
Data Engineering: Mastery of big data technologies (Spark, Databricks) and workflow orchestration tools (Airflow, Nextflow, Cromwell).
MLOps & DevOps: Strong knowledge of containerization (Docker, Kubernetes), CI/CD pipelines (GitHub Actions, Jenkins), and model monitoring/governance tools.
Visualization & Platforms: Experience with advanced visualization tools (DeepZoom, WebGL) and platform development (building APIs, microservices).
NIH Ecosystem Experience: Direct experience working with NIH, NCATS, NIAID, or similar federal health agencies. Understanding of the specific data challenges within the federal health sector is highly valued.
Open Source Leadership: Contributions to or leadership of open-source scientific software projects. Specific familiarity with the Polus platform or the National COVID Cohort Collaborative (N3C) data enclave is a distinct advantage.
NIST AI RMF Practitioner: Demonstrated experience implementing the NIST AI Risk Management Framework (Map, Measure, Manage, Govern) in a real-world setting.
Domain Expertise: Specialized knowledge in High-Content Imaging, Cheminformatics, Genomics, or Real-World Data (RWD) analytics.
Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.
The diversity of Axle's employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.
Accessibility: If you need an accommodation as part of the employment process please contact: [email protected]
This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate's experience, qualifications, skills, and location.
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