Director, Applied AI Engineering - Life Sciences Markets

United StatesRemoteFull-timeexecutive

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Remote Work: Remote position open to candidates in the United States, aligned with Eastern, Central, or Pacific time zones.

Requirements Summary

Production AI Engineering: Demonstrated experience personally building and shipping production AI applications, with end-to-end technical ownership of complex systems and measurable business outcomes.

Technical Tools

Machine Learning EngineerData

The Director of Applied AI Engineering will lead the technical development and delivery of advanced AI applications that transform complex proprietary datasets into actionable insights for the life sciences market. This is a highly hands-on leadership role, combining software engineering, AI architecture, and team management with direct responsibility for building and deploying production-ready solutions. You will guide a multidisciplinary engineering squad while personally contributing to system design, coding, model integration, and technical problem-solving. Working closely with data scientists, domain experts, and clients, you will turn pharmaceutical market questions into scalable AI-powered products. The role offers the opportunity to shape technical strategy, establish engineering standards, and deliver reliable solutions that support analysis of treatment adoption, competitive dynamics, and commercial drug performance.

Hands-On AI Development: Personally design, build, and deliver core AI services, applications, and workflows, taking ownership of critical implementation tasks and resolving technical challenges alongside engineers, data scientists, and domain specialists.
Technical Leadership and Team Management: Lead the engineering team and oversee overall squad execution. Establish technical and implementation standards through practical contributions, review code and architecture, and coordinate priorities, dependencies, and delivery.
Strategy and Roadmap Development: Identify high-value life sciences market questions that can be addressed using available data. Prioritize opportunities, define success metrics, and translate business needs into a focused technical and product roadmap.
AI Architecture and Implementation: Design and implement solutions that combine machine learning models, large language models (LLMs), retrieval systems, and workflow automation. Make informed trade-offs involving model quality, reliability, latency, scalability, and cost.
Engineering Quality and Model Evaluation: Establish automated testing, evaluation datasets, and monitoring processes to ensure the quality and reliability of AI outputs and applications. Maintain rigorous predictive model validation through point-in-time backtesting, baseline comparisons, and checks for data leakage and coverage bias.
Production Deployment and Ownership: Move prototypes into production and take responsibility for models, APIs, integrations, deployment, observability, and incident resolution. Develop reusable technical capabilities that can scale across therapeutic areas, companies, and clients.
Client and Domain Expert Collaboration: Work directly with clients and subject matter experts to understand analytical workflows, facilitate working sessions, explain findings, challenge assumptions, and incorporate feedback into product priorities.
Business and Technical Alignment: Translate complex analytical findings into clear, evidence-based conclusions for senior stakeholders and clients, including communicating limitations where the available data does not support a particular conclusion.

Requirements

~2 min read

Production AI Engineering: Demonstrated experience personally building and shipping production AI applications, with end-to-end technical ownership of complex systems and measurable business outcomes.
Technical Leadership: Proven ability to lead cross-functional technical delivery while remaining actively involved in system design, modeling, coding, debugging, and deployment.
AI and LLM Expertise: Deep practical experience integrating machine learning models and LLMs into production systems, including retrieval, tool use, structured outputs, and evaluation methodologies.
Model Assessment: Ability to understand model limitations, evaluate performance, and select appropriate approaches based on the available data and business requirements.
Client-Facing Communication: Experience working directly with technical and non-technical client stakeholders, leading collaborative working sessions, and translating complex analysis into clear recommendations and conclusions.
Data and Software Engineering: Strong hands-on experience with scalable data pipelines, automated testing, continuous integration and continuous delivery (CI/CD), and cloud infrastructure. Experience with AWS is preferred.
AI Deployment and Infrastructure: Ability to manage both application development and AI deployment, ensuring solutions are reliable, scalable, and suitable for production environments.
Product Development: Experience transforming bespoke analytical projects, prototypes, or pilot initiatives into repeatable products adopted by multiple clients or teams.
Industry Knowledge: Background in life sciences, pharmaceutical analytics, or capital markets is highly advantageous but not mandatory.
Leadership and Problem-Solving: Strong technical judgment, ownership, collaboration, and communication skills, with the ability to balance hands-on engineering responsibilities and broader delivery leadership.

What We Offer

~2 min read

✓Remote Work: Remote position open to candidates in the United States, aligned with Eastern, Central, or Pacific time zones.

✓Technical Leadership: Opportunity to lead a specialized engineering squad while remaining directly involved in designing and delivering advanced AI applications.

✓Strategic Impact: Influence the development of AI-powered products that generate insights into pharmaceutical markets, treatment adoption, competitive dynamics, and commercial performance.

✓Cross-Functional Collaboration: Work alongside engineers, data scientists, domain experts, and clients to solve complex analytical and technical challenges.

✓Product Innovation: Build scalable AI capabilities and turn advanced technical concepts into production solutions with practical business value.