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Senior Machine Learning Engineer

Omnicare
New York City, USA
Full-time
Senior · 3+ years experience
Hybrid
Discovered 2 weeks ago

Full Job Posting

What You’ll Do

  • Architect, design, build, and test end to end pipelines leveraging artificial intelligence, machine learning, reinforcement learning to drive Personalization and Marketing optimization and automation including customer targeting, content recommendation, frequency optimization,
  • propensity, uplift and LTV models etc.
  • Implement CICD for ML including automated testing, model versioning, monitoring, and
  • orchestration for models in production
  • Lead the design of Agentic AI solutions to marketing workflow automation, optimize ML infrastructure, and guide the development of data preparation and model optimization strategies.
  • Collaborate with Data Science, Engineering, and business teams to turn prototypes into production-ready solutions that meet business needs and drive measurable outcomes
  • Provide technical leadership on complex projects. Mentor other engineers and data scientists
  • in best practices, code quality, and scalable architecture. Influence our ML and data
  • roadmap.
  • What We’re Looking ForMust-Have
  • 3+ years of hands-on experience developing machine learning systems in productionenvironment
  • 3+ years of Python programming experience. Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and MLOps platforms (e.g., Kubeflow, Vertex)
  • 2+ years of SQL experience working with large-scale data (e.g., Snowflake, BigQuery)
  • 2+ years of leading design and architecture of new and existing system experience
  • Strong background in one or more of the following: reinforcement learning, causal inference, Agentic architecture
  • Experience as a mentor or tech lead
  • Ability to work and influence cross-functionally with strong communication skills
  • Solid backend engineering skills in Python, including APIs and data modeling
  • Preferred
  • Proficiency with full-stack framework (e.g, Node.js, React)
  • Experience with distributed modeling
  • Education
  • Bachelor’s degree in Computer Science, Engineering, Math, Statistics, or related field (or
  • equivalent experience)
  • Master’s degree preferred
  • Anticipated Weekly Hours
  • 40
  • Time Type
  • Full time
  • Pay Range
  • The typical pay range for this role is:
  • $111,240.00 - $222,480.00
  • This pay range represents the base hourly rate or base annual full-time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above.
  • Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong.
  • Great benefits for great people
  • We take pride in offering a comprehensive and competitive mix of pay and benefits that reflects our commitment to our colleagues and their families.
  • This full‐time position is eligible for a comprehensive benefits package designed to support the physical, emotional, and financial well‐being of colleagues and their families. The benefits for this position include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility.
  • Additional details about available benefits are provided during the application process and on Benefits Moments.
  • We anticipate the application window for this opening will close on: 10/25/2026
  • Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.

Qualifications

  • Experience with distributed modeling
  • Bachelor’s degree in Computer Science, Engineering, Math, Statistics, or related field (or

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