Associate Director of AI and Data

Core Responsibilities

• 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).
• Spearhead the evolution of the Polus platform, transitioning it from a robust image analysis tool into a fully integrated, multi-modal research ecosystem.
• Establish and enforce rigorous AI Governance frameworks, operationalizing the NIST AI Risk Management Framework (RMF) across all projects to ensure fairness, interpretability, and compliance with federal ethical standards.
• Direct the design and implementation of high-throughput data pipelines capable of ingesting and analyzing petabyte-scale datasets (genomics, proteomics, EHR).
• Oversee the development of predictive models for translational science, focusing on "de-risking" drug discovery and clinical trial design.
• Optimize MLOps and DevSecOps processes to ensure the rapid, secure deployment of models from prototype to production.
• Partner with the Growth and Capture teams to drive new business acquisition, authoring technical volumes, developing win themes, and creating compelling solution graphics.
• Galvanize relationships with key federal stakeholders (Project Officers, CIOs, Lab Chiefs) and 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.

Required Qualifications

• Education: Ph.D. in Computer Science, Bioinformatics, Computational Biology, Data Science, or a related quantitative discipline.
• 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 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.
• Technical Skills: 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.
• 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).