Senior Principal Machine Learning Engineer

Transcarent is looking for a Sr. Principal ML Engineer to lead a team focused on enabling Data and AI/ML solutions and products across the company, with a particular focus on ML platform our flagship WayFinding™ experience.

In this role, you will set technical direction with full autonomy, identifying systemic issues before they become crises, defining pragmatic architecture, and moving fluidly between hands-on execution and high-level design to unblock the initiatives that matter most. You will partner closely with engineering leadership, product, and cross-functional stakeholders to translate ambiguous business problems into scalable technical solutions, and serve as a multiplier for the broader engineering organization through mentorship, design review, and technical influence.

The ideal candidate brings strong machine learning engineering experience, sound technical judgment, and a people-first leadership style. You should be comfortable operating in a fast-moving environment, partnering closely with product, engineering, clinical, data, and business stakeholders to deliver AI/ML solutions that improve the Member experience and support Transcarent’s mission to measurably improve health and care.

Though understanding of AI/ML is crucial for this role, we kindly request that you refrain from using GenAI while going through the interview process to allow fair evaluation of your skillset.

Core Competencies

• Strategic and systems-level thinking
• Technical depth in Data and AI/ML paired with hands-on execution
• Cross-functional influence without direct authority
• Data-driven, evidence-based decision-making
• Ownership, accountability, and bias for action
• Clear communication with technical and non-technical audiences
• Mentorship and technical coaching
• Adaptability in ambiguous, fast-changing environments

Required Qualifications What We're Looking For / What You Need

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 12+ years of professional software/data engineering experience, including significant time operating as a senior individual contributor at Principal level or above.
• Expert-level command of data engineering fundamentals: distributed data processing (Spark), stream and batch pipeline design, data warehouse/lakehouse architecture, and ELT/ETL patterns.
• Strong ML engineering proficiency, including feature engineering, model training and evaluation, deployment, and production monitoring.
• Hands-on experience with modern GenAI and LLM workflows, including RAG architectures, fine-tuning, prompt engineering, and responsible AI practices.
• Cloud-native platform fluency across one or more major providers (AWS, GCP, Azure), including managed data and ML services.
• Proficiency in Python and SQL as primary languages; comfort with Go, Java, or equivalent as needed.
• Demonstrated ability to lead complex, cross-functional technical initiatives from ambiguity through production without relying on organizational authority.
• Excellent written and verbal communication skills, with a track record of influencing senior technical and non-technical stakeholders.

Preferred Qualifications

• Experience with key ecosystem tools such as Databricks, dbt, Kafka, and MLflow.
• Experience in healthcare, benefits, or another regulated, compliance-sensitive industry.
• Track record of applying GenAI tools as a velocity and quality multiplier for yourself and your team.
• Experience defining data governance, lineage, and observability frameworks at platform scale.
• Prior experience mentoring senior engineers or informally 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,