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Director of AI and Data Engineering

Everlywell
Full-time
On-site
Austin, Texas, United States
Everlywell is a digital health company pioneering the next generation of biomarker intelligence—combining AI-powered technology with human insight to deliver personalized, actionable health answers. We transform complex biomarker data into life-changing insights—seamlessly integrating advanced diagnostics, virtual care, and patient engagement to reshape how and where health happens.

Over the past decade, Everlywell has delivered close to 1 billion personalized health insights, transforming care for 60 million people and powering hundreds of enterprise partners. In 2024 alone, an estimated 1 in 86 U.S. households received an Everlywell test, solidifying our spot as the #1 at-home testing brand in the country. And we’re just getting started. Fueled by AI and built for scale, we’re breaking down barriers, closing care gaps, and unlocking a more connected healthcare experience that is smarter, faster, and more personalized.

Everlywell is entering a new era—one where data and AI will shape the future of diagnostics, consumer health, and enterprise health plan solutions. As the Director of AI and Data Engineering, you will lead the vision, strategy, and execution of our AI and data platform capabilities. This role will drive the foundation and innovation layer that powers predictive insights, personalized health journeys, and next-gen diagnostics intelligence—at scale.

You will be both a strategic architect and technical leader, managing a small, high-impact team while collaborating cross-functionally to make data a transformative, competitive advantage.

AI and Data Strategy & Architecture

    • Define and execute a comprehensive strategy for AI and data infrastructure, focused on scalability, reliability, and business impact.
    • Drive innovation in machine learning (ML) and large language model (LLM)-powered applications, from concept to production.
    • Evaluate and implement emerging AI techniques (e.g., retrieval-augmented generation, agentic workflows, multimodal models) aligned to our diagnostics and consumer product roadmap.

Technical Leadership:

    • Lead architectural decisions across data warehousing, pipelines, model deployment, and real-time inference systems.
    • Design and evolve our data ecosystem (including structured lab/test data, behavioral data, partner integrations) to serve both AI initiatives and enterprise analytics needs.
    • Oversee development and integration of ML models into customer-facing features, health recommendations, and operational tooling.

Team Management and Enablement:

    • Manage and mentor a high-performance team of data engineers and AI/ML specialists, including Principal-level technical contributors.
    • Act as a “player-coach”—deeply technical and capable of guiding architecture while fostering team execution and growth.
    • Establish scalable practices for MLOps, experimentation frameworks, and model performance tracking.

Cross-Functional and Business Impact:

    • Partner with Product, Clinical, Engineering, and Commercial teams to identify and prioritize AI use cases that align with core business goals.
    • Translate data and AI capabilities into clear, compelling value propositions for internal and external stakeholders.
    • Ensure systems and models comply with HIPAA and other regulatory requirements while maintaining a strong security and data governance posture.

Who You Are:

    • 10+ years of experience in data engineering, AI/ML development, or related fields, with 3+ years in a leadership role.
    • Proven track record of designing and deploying ML or AI systems in production environments, particularly in healthtech or regulated industries.
    • Strong experience with Cloud-native data architectures (AWS/GCP/Azure), Machine learning pipelines and model lifecycle (e.g., Vertex AI, SageMaker, MLFlow), Data processing frameworks (Spark, Airflow, DBT, etc.), and Modern LLM stacks (e.g., LangChain, transformers, RAG pipelines, fine-tuning workflows)
    • Deep understanding of enterprise data management, data governance, and secure system design.
    • Strategic mindset: able to balance short-term delivery with long-term platform vision.
    • Excellent communicator and cross-functional collaborator—able to align diverse teams around shared AI and data goals.

Preferred QualificationsL

    • Expertise in applied artificial intelligence.
    • Hands-on experience with machine learning (supervised, unsupervised, and reinforcement learning).
    • Deep familiarity with LLM architectures (e.g., GPT, BERT, LLaMA) and generative AI use cases, including RAG pipelines, fine-tuning, prompt engineering, and evaluation frameworks.
    • Experience implementing AI-powered features in production environments.
    • Significant experience architecting modern data ecosystems, including Scalable data pipelines and warehousing (e.g., Snowflake, BigQuery, Redshift), data modeling, quality frameworks, and governance standards and Integrating structured and unstructured data for use in advanced analytics and AI.
    • Experience with healthcare-specific data (e.g., lab test results, claims data, HL7/FHIR integration).
    • Familiarity with HIPAA, HITRUST, and other healthcare data privacy and security compliance frameworks.
    • Background in a consumer health or diagnostics startup, scale-up, or growth-stage environment where velocity, quality, and innovation must co-exist.
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