Principal Machine Learning Engineer

We're building a world of health around every individual , shaping a more connected, convenient, and compassionate health experience.

As a Principal Machine Learning Engineer, you will join a leading retail organization's personalization and decisioning platform team at a pivotal moment of building a unified, large-scale machine learning platform that drives one-to-one offer, content, and channel decisions across millions of customers.

Responsibilities

Backend Engineering & Systems Design

• Architect and build scalable, production-grade backend services (APIs, microservices, batch and streaming pipelines) that serve machine learning decisions to downstream systems with strict latency, throughput, and reliability requirements.
• Design data contracts and interfaces between data engineering, model training, and production serving layers, with a focus on training-serving parity and point-in-time correctness.
• Own the design of versioned, queryable feature stores and point-in-time lookup APIs that eliminate feature leakage and support both training and real-time inference from a single code path.
• Design and optimize large-scale distributed computing workflows (containerized batch jobs, parallelized training, and optimization solves) that run reliably within tight operational SLAs.
• Make build-vs-buy and architecture tradeoff decisions (storage formats, orchestration frameworks, solver choice, serving patterns) and document them as durable architectural decisions.

Software Development Best Practices

• Set and enforce engineering standards across the ML codebase: modular design, dependency management, code review rigor, automated testing (unit, integration, regression), CI/CD, and documentation.
• Introduce and champion practices that make ML systems easier to change safely , contract tests between pipeline stages, reproducible environments, infrastructure-as-code, and rollback-safe deployments.
• Drive observability and monitoring for both software and model health: service-level metrics, data/feature drift, model calibration, and automated alerting , treating production ML with the same rigor as any other production system.
• Lead design and code reviews across the team, balancing pragmatism with long-term maintainability; prevent and resolve technical debt before it becomes a roadblock.

Tech Lead & Mentorship

• Act as the technical lead for the team's most complex, cross-repo initiatives , breaking down ambiguous problems into phased, de-risked implementation plans.
• Mentor and grow mid-level and senior machine learning engineers; provide hands-on guidance on system design, modeling tradeoffs, and production readiness.
• Anticipate and prevent problems and roadblocks before they occur, including proactively identifying scaling limits, failure modes, and operational risk in systems under design.
• Partner with engineering managers and product leadership to shape team roadmap, estimate complex work, and raise the technical bar org-wide without requiring formal people-management authority.
• Communicate complex technical concepts, tradeoffs, and risks clearly to both engineering peers and non-technical business stakeholders.

Machine Learning & Optimization

• Build and productionize large-scale models and decisioning systems , including causal inference / incrementality models, reinforcement-learning-based policies, and mixed-integer optimization for constrained allocation problems.
• Partner with data science to translate research prototypes into reliable, scalable, production-grade systems, closing the gap between notebook and production.
• Build and maintain experimentation infrastructure (holdouts, A/B tests, counterfactual validation) and monitoring for incrementality, drift, and model/business performance.

Requirements

• 7+ years of progressively complex software, ML, data science/engineering experience, including 5+ years building and operating production backend systems or data-intensive platforms at scale.
• 5+ years building, deploying, and owning machine learning systems in production, with a strong focus on software development best practices (testing, CI/CD, code review, modular design).
• Expert-level Python and SQL; deep understanding of software design patterns, API design, and distributed systems fundamentals.
• Proven experience architecting scalable, data-intensive backend systems (microservices, REST APIs, batch/streaming pipelines) in a major cloud environment (Azure, AWS, or GCP).
• Hands-on experience with containerization and orchestration (Docker, Kubernetes) and with ML/data pipeline orchestration tools (Kubeflow, Airflow, or similar).
• Demonstrated experience as a technical lead: setting technical direction, running design/code reviews, and mentoring other engineers.
• Strong track record anticipating risk and preventing production issues before they occur, and communicating technical tradeoffs clearly to both engineers and business stakeholders.

Preferred Qualifications

• Experience with large-scale mathematical/combinatorial optimization (MIP solvers such as Gurobi, PuLP, or SCIP) in a production decisioning system.
• Experience with causal inference (uplift modeling, CATE estimation, meta-learners) and/or reinforcement learning applied to real-world decisioning or recommendation problems.
• Experience designing versioned feature stores with point-in-time correctness and training-serving parity.
• Experience with distributed computing frameworks (Dask, Spark, or Ray) for large-scale data processing and model training.
• Experience in retail, ad-tech, or marketing personalization domains.
• Demonstrated proficiency across mathematical analysis methods, machine learning, and statistical/predictive modeling, with deep specialization in at least one area (e.g., causal inference, RL, or optimization).

Benefits 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.