84.51˚
Lead AI/ML/Optimization Engineer (G3) – Labs Innovation Focus (P3227)
As a Senior AI/ML Engineer (G3) on the Labs team, you will serve as a hands‑on technical lead responsible for both implementing robust code and guiding the architectural direction of ML/AI/optimization‑based systems. This role blends deep engineering expertise, applied ML and optimization research, and system design to accelerate the transition from proof‑of‑concept to scalable business solution. You will contribute code daily, mentor junior engineers, and collaborate with cross‑functional partners to define, deliver, and scale the next generation of AI/ML/optimization capabilities across Kroger.
Responsibilities
Serve as a hands‑on developer responsible for building and maintaining end‑to‑end ML, AI, and optimization‑based solutions
Lead technical design, implementation, and review processes for POCs and production‑ready systems
Lead end‑to‑end solution lifecycle—from rapid prototyping through to scaling and hand‑off to production teams in partnership with other data scientists and engineers within Labs and across the business
Partner with researchers and data scientists to co‑develop, scale, and operationalize new algorithms
Architect and implement robust ML(AI)Ops pipelines that support experimentation, deployment, and monitoring
Build reusable ML components and APIs that enable modularity and scalability across business areas
Evaluate and adopt emerging technologies and tooling that can enhance experimentation and delivery speed
Drive technical best practices in code quality, documentation, observability, and team knowledge sharing
Drive experimentation and benchmarking to select performant solutions that balance complexity and business value
Contribute to Labs’ collaborative, research‑forward culture by learning, sharing, and mentoring both junior and senior engineers and researchers on industry‑leading and cutting‑edge technologies
Lead and participate in code reviews and technical architecture planning to ensure adherence to preferred patterns and standards
Represent Labs in technical forums; proactively mentor junior and peer engineers
Collaborate with product and business stakeholders to align technical execution with innovation goals
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Mathematics, or a related field
4+ years experience developing ML, AI, or optimization systems, including production deployment and scaling
Strong software engineering fundamentals and daily coding experience in Python
Deep proficiency in Python and fluency in NumPy, pandas, PySpark and at least 3 of the following MLand Optimization libraries – PyTorch, TensorFlow, scikit‑learn, and Pyomo
Hands‑on experience architecting and productionizing at least one type of optimization problem (e.g., network optimization, vehicle routing, scheduling, facility location, or resource allocation)
Practical experience with at least one industry‑standard optimization solver such as Gurobi, CPLEX, OR‑Tools, Pyomo, PuLP, CBC, or SCIP
Hands‑on experience designing CI/CD and MLOps workflows using tools such as MLflow, Azure ML, or Databricks
Familiarity with cloud platforms (Azure preferred), containerization (Docker), and orchestration (Kubernetes)
Experience with modern software development practices including testing, logging, observability, and version control
Ability to lead projects through ambiguity and collaborate in highly cross‑functional teams
Preferred Experience
Strong track record of partnering with researchers to translate early‑stage ML ideas into deployable systems
Experience prototyping and scaling AI solutions in applied environments
Experience designing experiment platforms or reusable ML/optimization infrastructure
Demonstrated leadership in evaluating trade‑offs between performance, complexity, and maintainability
Familiarity with real‑time or batch data processing systems
Leadership in navigating trade‑offs between performance, complexity, and long‑term maintainability
Pay Transparency And Benefits
The stated salary range represents the entire span applicable across all geographic markets from lowest to highest. Actual salary offers will be determined by multiple factors including but not limited to geographic location, relevant experience, knowledge, skills, other job‑related qualifications, and alignment with market data and cost of labor. In addition to salary, this position is also eligible for variable compensation.
Below is a list of some of the benefits we offer our associates:
Health: Medical with competitive plan designs and support for self‑care, wellness and mental health. Dental with in‑network and out‑of‑network benefit. Vision with in‑network and out‑of‑network benefit.
Wealth: 401(k) with Roth option and matching contribution. Health Savings Account with matching contribution (requires participation in qualifying medical plan). AD&D and supplemental insurance options to help ensure additional protection for you.
Happiness: Hybrid work environment. Paid time off with flexibility to meet your life needs, including 5 weeks of vacation time, 7 health and wellness days, 3 floating holidays, as well as 6 company‑paid holidays per year. Paid leave for maternity, paternity and family care instances.
Pay Range $121,000—$201,250 USD
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Responsibilities
Serve as a hands‑on developer responsible for building and maintaining end‑to‑end ML, AI, and optimization‑based solutions
Lead technical design, implementation, and review processes for POCs and production‑ready systems
Lead end‑to‑end solution lifecycle—from rapid prototyping through to scaling and hand‑off to production teams in partnership with other data scientists and engineers within Labs and across the business
Partner with researchers and data scientists to co‑develop, scale, and operationalize new algorithms
Architect and implement robust ML(AI)Ops pipelines that support experimentation, deployment, and monitoring
Build reusable ML components and APIs that enable modularity and scalability across business areas
Evaluate and adopt emerging technologies and tooling that can enhance experimentation and delivery speed
Drive technical best practices in code quality, documentation, observability, and team knowledge sharing
Drive experimentation and benchmarking to select performant solutions that balance complexity and business value
Contribute to Labs’ collaborative, research‑forward culture by learning, sharing, and mentoring both junior and senior engineers and researchers on industry‑leading and cutting‑edge technologies
Lead and participate in code reviews and technical architecture planning to ensure adherence to preferred patterns and standards
Represent Labs in technical forums; proactively mentor junior and peer engineers
Collaborate with product and business stakeholders to align technical execution with innovation goals
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Mathematics, or a related field
4+ years experience developing ML, AI, or optimization systems, including production deployment and scaling
Strong software engineering fundamentals and daily coding experience in Python
Deep proficiency in Python and fluency in NumPy, pandas, PySpark and at least 3 of the following MLand Optimization libraries – PyTorch, TensorFlow, scikit‑learn, and Pyomo
Hands‑on experience architecting and productionizing at least one type of optimization problem (e.g., network optimization, vehicle routing, scheduling, facility location, or resource allocation)
Practical experience with at least one industry‑standard optimization solver such as Gurobi, CPLEX, OR‑Tools, Pyomo, PuLP, CBC, or SCIP
Hands‑on experience designing CI/CD and MLOps workflows using tools such as MLflow, Azure ML, or Databricks
Familiarity with cloud platforms (Azure preferred), containerization (Docker), and orchestration (Kubernetes)
Experience with modern software development practices including testing, logging, observability, and version control
Ability to lead projects through ambiguity and collaborate in highly cross‑functional teams
Preferred Experience
Strong track record of partnering with researchers to translate early‑stage ML ideas into deployable systems
Experience prototyping and scaling AI solutions in applied environments
Experience designing experiment platforms or reusable ML/optimization infrastructure
Demonstrated leadership in evaluating trade‑offs between performance, complexity, and maintainability
Familiarity with real‑time or batch data processing systems
Leadership in navigating trade‑offs between performance, complexity, and long‑term maintainability
Pay Transparency And Benefits
The stated salary range represents the entire span applicable across all geographic markets from lowest to highest. Actual salary offers will be determined by multiple factors including but not limited to geographic location, relevant experience, knowledge, skills, other job‑related qualifications, and alignment with market data and cost of labor. In addition to salary, this position is also eligible for variable compensation.
Below is a list of some of the benefits we offer our associates:
Health: Medical with competitive plan designs and support for self‑care, wellness and mental health. Dental with in‑network and out‑of‑network benefit. Vision with in‑network and out‑of‑network benefit.
Wealth: 401(k) with Roth option and matching contribution. Health Savings Account with matching contribution (requires participation in qualifying medical plan). AD&D and supplemental insurance options to help ensure additional protection for you.
Happiness: Hybrid work environment. Paid time off with flexibility to meet your life needs, including 5 weeks of vacation time, 7 health and wellness days, 3 floating holidays, as well as 6 company‑paid holidays per year. Paid leave for maternity, paternity and family care instances.
Pay Range $121,000—$201,250 USD
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