Senior Principal Machine Learning Engineer

Transcarent is the One Place for Health and Care™, bringing medical, pharmacy, and point solutions together with theWayFinding™ experience, the first and only generative AI-powered health and care platform for health consumers. Our WayFinding experience, paired with transparent and consumer-driven pharmacy care, 2nd.MD expert medical opinions, and virtual primary care, works seamlessly with comprehensive Care Experiences – Cancer Care, Surgery Care, and Weight Health – to support people with all of their health needs, simple or serious. More than 1,700 employers and health plans rely on us to provide information, guidance, and care, empowering health consumers with more choice, an experience they love, access to higher-quality care, and lower costs for 21 million Members. For more information, visit transcarent.com, and follow us on LinkedIn.

We are looking for teammates to join us in building our company, culture, and Member experience who:

• Put people first, and make decisions with the Member's best interests in mind
• Are active learners, constantly looking to improve and grow
• Are driven by our mission to measurably improve health and care each day
• Bring the energy needed to transform health and care, and move and adapt rapidly
• Are laser focused on delivering results for Members, and proactively problem solving to get there

About this role

The Sr. Principal ML Engineer is the most senior individual-contributor technical role in our Data and AI organization — spanning our data platform, data warehouse, ELT pipelines, ML systems, and GenAI capabilities. This person sets technical direction with full autonomy, moving fluidly between deep hands-on execution and high-level architecture to unblock our highest-leverage initiatives, and serves as a technical multiplier for the broader engineering team.

What you'll do

• Own the technical architecture and roadmap across our data platform, warehouse, ELT pipelines, ML systems, and GenAI components
• Independently identify, scope, and resolve systemic architectural issues across the Data and AI/ML stack
• Contribute directly to the codebase with high velocity, moving fluidly between implementation and high-level designBring the energy needed to transform health and care, and move and adapt rapidly
• Plan and deliver complex initiatives spanning multiple teams and systems, proactively surfacing risks and decisions to leadership
• Embed data governance, quality, lineage, and observability practices into every initiative from the outset
• Partner with cross-functional stakeholders to clarify requirements and own the technical path from framing through delivery
• Raise the technical bar across the organization through code reviews, design feedback, and direct coaching
• Design fault-tolerant distributed data pipelines with checkpointing, replay, and schema evolution.
• Establish end-to-end data lineage, data quality SLAs, and automated data contract enforcement across platform initiatives.

What we're looking for

• 12+ years of professional software/data engineering experience, including significant time as a senior IC at Principal level or above

• Expert-level command of data engineering fundamentals: distributed processing (Spark, Flink), stream/batch pipelines, and data warehouse/lakehouse architecture

• Strong ML engineering proficiency across the model lifecycle, from training and evaluation through deployment and production monitoring

• Hands-on experience with modern GenAI/LLM workflows, including RAG architectures, fine-tuning, and prompt engineering

• Cloud-native platform fluency (AWS, GCP, or Azure) and proficiency in Python and SQL

• Demonstrated ability to lead complex, cross-functional technical initiatives without relying on organizational authority

• Excellent written and verbal communication skills with both technical and non-technical audiences

• Experience tuning warehouse/lakehouse performance through partitioning, query optimization, and workload isolation.

• Experience implementing model drift detection, reproducible ML deployments, and production rollback strategies.

• Experience evaluating RAG quality using retrieval metrics, groundedness testing, and automated regression gates.

Nice to have

• Experience with Databricks, dbt, Kafka, and MLflow or equivalent ecosystem toolsExpert-level command of data engineering fundamentals: distributed processing (Spark, Flink), stream/batch pipelines,
• Experience in healthcare, benefits, or another regulated, compliance-sensitive industryHands-on experience with modern GenAI/LLM workflows, including RAG architectures, fine-tuning, and prompt engineering
• Track record of using GenAI tools as a velocity and quality multiplier
• Experience defining data governance and observability frameworks at platform scale
• Prior experience mentoring senior engineers or leading technical communities of practice

Additional Notes

• Team size and scope: Individual contributor role; the most senior IC in the Data and AI organization, with broad architectural scope and no direct reports.
• Supervisory responsibilities: None formally; provides technical mentorship and coaching as part of day-to-day delivery.
• Work location expectations: Remote US.
• Travel expectations: Minimal (occasional team or offsite travel,