Senior Principal Machine Learning Engineer
Posted Updated 80
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Description
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
What We're Looking For / What You Need
Required Qualifications
- 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,
