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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading the development and operationalization of machine learning systems, with a strong focus on MLOps, model effectiveness, and compliance in the healthcare sector. Proven ability to mentor and manage teams while translating complex business problems into actionable ML solutions.
Highest-signal resume keywords
Machine Learning EngineeringMLOps InfrastructurePython ProgrammingSQL ProficiencyTeam Leadership
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning LifecycleFeature EngineeringModel DeploymentEnsemble ModelsReinforcement LearningMulti-Armed BanditsData PipelinesModel MonitoringPersonalization SystemsRecommendation Engines
Soft Skills
MentoringCoachingCollaborationJudgmentCommunication
Industry Keywords
Healthcare TransactionsComplianceFairness GuardrailsPatient JourneyEconomic Adaptation
Tech Stack
Tools & technologiesPythonSQL
About the role
Key responsibilities & impact- Lead development of machine learning systems underlying Cedar Pay’s Personalization Engine
- Own the ML system and machine learning lifecycle end-to-end, including reliability, scalability, and model effectiveness
- Lead hands-on MLOps work including feature stores, model deployment, inference, and monitoring pipelines
- Lead sophisticated ML modeling, potentially including ensemble models, reinforcement learning, and multi-armed bandits
- Implement compliance and fairness guardrails
- Make hands-on technical contributions while guiding and empowering two ML engineers
- Conduct code and design reviews and elevate technical excellence
- Directly manage, mentor, and coach machine learning engineers
- Build production-grade systems capable of handling millions of healthcare transactions
- Rapidly operationalize, iterate on, and improve ML models and approaches
- Improve and expand feedback loops capturing patient reactions to real-time decisions
- Enable models to learn autonomously and adapt to changing economic and healthcare landscapes
- Partner with Product and Design to translate patient journey touchpoints into concrete ML problems
- Define where machine learning can provide leverage within the Cedar Pay ecosystem
Requirements
What you’ll need- 6+ years of professional experience in machine learning engineering
- Experience designing, building, and deploying ML-powered personalization systems or recommendation engines at scale for consumer-facing products
- Hands-on experience across the full ML lifecycle, including data pipelines, feature engineering, core model training, and MLOps infrastructure
- Expertise in Python and SQL highly preferred
- 1+ years of experience managing ML engineers and scientists
- Experience leading a team through a technical roadmap from ideation to production
- Candidates with an IC Tech Lead ML background and informal or dotted-line management responsibilities may be considered
- Experience collaborating with product leaders and software engineers
- Ability to formulate business problems as supervised, unsupervised, or reinforcement learning tasks
- Judgment to evaluate model benefits against development and operating costs
- Willingness to champion simpler non-ML solutions when they provide higher ROI
- Passion for applying machine learning to improve the healthcare financial experience
Benefits
Comp & perks- Equity eligibility
- Competitive benefits and wellness package
- Flexibility to work from home or in the office
- Unlimited PTO for vacation, sick, and mental health days
- Encouraged minimum of 20 vacation days per year
- 16 weeks paid parental leave with health benefits for all parents
- Flexible re-entry schedules for returning to work
- Competitive pay and health benefits
- Fertility and adoption assistance
- 401(k) matching: 100% of contributions up to 3% of annual compensation
- Hands-on mentorship
- Employee and management coaching
- Team discretionary budget for learning and development resources
- Diversity initiatives and employee resource groups