Assistant Director of AI and Data

Axle•Rockville, MD

26d

About The Position

Axle is seeking a visionary and operationally astute Assistant 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 Assistant 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.

Requirements

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.
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).
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).

Nice To Haves

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.