Architect & ship: Design end-to-end Python services (batch + streaming) that ingest model outputs, run constrained optimization, and surface real-time dispatch decisions.
Model & simulate: Build/extend ML models (gradient-boosting, deep learning, OR-Tools) and run time-horizon simulations to quantify cost vs. service-level trade-offs.
Operationalize: Automate training, validation, A/B rollout, and monitoring (SageMaker / Airflow).
Lead & collaborate: Partner with Product, Ops, and Data Engineering; mentor a small squad of ML engineers; present findings to execs.
Continuously improve: Instrument NPS / cost telemetry, identify failure modes, and iterate.

Requirements

6 + yrs experience in ML Engineering with ownership of production systems.
Expert-level Python.
Hands-on optimization (Mixed Integer Programming / Linear Optimization / Stochastic Optimization) and modern ML (XGBoost, PyTorch).
Proven record designing cloud-native pipelines on AWS, GCP or Azure (AWS).
Strong SQL, feature-store design, and data-quality mindset.

Benefits

Health and Wellness: Healthcare, dental, vision, disability, life insurance, and mental health benefits for associates and their families.
Financial Security: 401(k) plan with company match and tuition assistance to support your future goals.
Work-Life Balance: Flexible time off, paid sick leave, and ten paid holidays annually.
Family Support: Parental planning benefits to assist associates through life’s milestones.
Bonus/Incentive Programs

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Pythonmachine learninggradient boostingdeep learningmixed integer programminglinear optimizationstochastic optimizationSQLfeature-store designdata quality

Soft Skills

leadershipcollaborationmentoringcommunication