Associate Director, Data & AI Modeling

Axle; United States; Remote; USD 150,000 - 190,000; Full time

Benefits offered by this job

Health benefits

Paid time off

401K match

Educational benefits

Employee referral bonus

Flexible Spending Accounts

Job summary

Axle Informatics is seeking an Associate Director of Data and Modeling to lead data engineering, AI/ML, scientific computing, and modeling across biomedical research programs.

You will build durable, production-grade systems, mentor multidisciplinary teams, and partner with scientists and federal health partners to deliver reliable data platforms and analytical capabilities. Salary range listed below.

Qualifications

• Eight or more years of progressively responsible experience in software engineering, data engineering, machine learning engineering, computational science, data science, or a closely related technical discipline.
• Five or more years of leadership experience building and guiding multidisciplinary technical teams, including responsibility for hiring, technical direction, delivery, and staff development.
• Demonstrated experience personally designing, building, deploying, and operating production-grade data, AI/ML, software, or scientific computing systems.
• Strong technical judgment across modern data and AI architectures, distributed processing, containerized environments, CI/CD, MLOps or LLMOps, observability, and production operations.
• Demonstrated success moving analytical or AI/ML work from research and prototyping into reliable production use, including evaluation, deployment, monitoring, versioning, and ongoing operational ownership.
• Experience building or leading large and complex data pipelines with attention to interoperability, data quality, lineage, reproducibility, and repeatable transformation.
• Experience leading modeling, simulation, scientific computing, or computational research work directly or in close partnership with scientific subject matter experts.
• Ability to review technical designs, identify risk, challenge assumptions, resolve difficult engineering problems, and distinguish promising emerging methods from approaches that are not yet ready for production use.
• Experience delivering technical systems in research-intensive, regulated, or high-governance environments involving sensitive data, security controls, privacy requirements, or formal technical oversight.
• Strong communication skills and the ability to move comfortably between detailed technical discussion and clear explanation for scientists, program leaders, executives, and government stakeholders.

Responsibilities

• Technical Strategy and Stewardship: Set the technical direction for data platforms, AI/ML systems, scientific computing environments, and modeling capabilities. Establish reference architectures and reusable implementation patterns that help teams make sound decisions while preserving room for experimentation. Decide when to build, modernize, adopt, or partner, and make those decisions with long-term sustainability in mind.
• Production Data Platforms: Guide the design and operation of data systems that can ingest, transform, harmonize, and serve large, heterogeneous scientific and health datasets. Build repeatable approaches for data quality, validation, terminology translation, lineage, versioning, documentation, and change control so that data products remain understandable and trustworthy as programs evolve.
• AI/ML and Emerging Methods: Lead the development of AI/ML capabilities where they can create measurable scientific or operational value, including predictive modeling, computer vision, natural language processing, large language models, retrieval-augmented generation, and agentic workflows. Require thoughtful evaluation, traceability, privacy safeguards, human review where appropriate, and monitoring that continues after deployment.
• Modeling, Simulation, and Scientific Computing: Build a sustainable modeling and simulation practice that supports both specialized scientific work and reusable organizational capability. Establish standards for reproducible workflows, versioned inputs and environments, compute strategy, and scientific validation. Partner effectively with domain experts when the deepest subject matter expertise resides outside your own discipline.
• From Research to Reliable Systems: Help teams cross the difficult gap between promising prototypes and dependable production capabilities. Strengthen engineering practices around testing, CI/CD, containerization, observability, release management, incident response, documentation, and technical debt. Preserve the creativity of research environments while introducing the discipline required for systems that others depend on.
• Technical Organization Leadership: Build and lead multidisciplinary teams spanning software engineering, data engineering, machine learning engineering, data science, and computational science. Create clear roles, strong technical leadership paths, and expectations that reward both rigor and collaboration. Develop managers and technical leads who can make good decisions without creating single points of failure.
• Program Execution and Quality: Create an operating cadence that makes complex technical delivery visible and predictable. Establish clear priorities, risk checkpoints, release criteria, ownership, and measures of progress. Help teams sequence work thoughtfully, address technical debt without losing momentum, and communicate tradeoffs before they become surprises.
• Governance, Security, and Responsible Use: Work with security, privacy, governance, and scientific stakeholders to ensure that data and AI capabilities are appropriate for sensitive and highly governed environments. Promote practical controls for access, auditability, intended use, model review, data minimization, privacy, and responsible AI without allowing governance to become disconnected from how systems are actually built and used.
• Open Science and Community Engagement: Encourage technical publication, conference participation, open-source contribution, and active engagement with the broader research software community. Support continued stewardship of reusable scientific platforms and tools, including Polus, and look for opportunities where open collaboration can increase impact beyond a single project or client.
• Technical Growth and Partnership: Contribute to selected federal growth and proposal efforts as a senior technical leader. Shape credible solution architectures, technical approaches, staffing models, and implementation strategies. Help Axle pursue work that matches its technical strengths and can be executed with the same standards expected of its active programs.

Skills

Software engineering

Data engineering

Machine learning engineering

Computational science

Data science

Leadership

AI architectures

Distributed processing

Containers

CI/CD / MLOps

Observability

Production operations

Production deployment

Data pipelines

Modeling

Technical design review

Regulated environments

Communication

Education

Advanced degree in CS or quantitative field

Tools

OMOP

FHIR

PCORnet

CDISC

Job description

Axle Informatics is seeking an Associate Director of Data and Modeling to lead data engineering, AI/ML, scientific computing, and modeling across biomedical research programs.

You will build durable, production-grade systems, mentor multidisciplinary teams, and partner with scientists and federal health partners to deliver reliable data platforms and analytical capabilities. Salary range listed below.