- Страна
- США
- Зарплата
- 121 000 $ – 201 250 $
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Lead AI/ML Engineer (P3227)
Привлекательная позиция в крупной компании с доступом к уникальным данным. Высокий уровень компенсации и отличный соцпакет (5 недель отпуска), однако требование 5-дневной работы в офисе может подойти не всем.
Сложность вакансии
Высокая сложность обусловлена требованием совмещать глубокие знания в ML и математической оптимизации с навыками системной архитектуры и лидерства. Позиция подразумевает ежедневное написание кода и работу в офисе 5 дней в неделю, что повышает планку дисциплины и вовлеченности.
Анализ зарплаты
Предложенный диапазон $121k – $201k полностью соответствует рыночным стандартам для позиций уровня Lead в таких хабах, как Чикаго. Нижняя граница подходит для кандидатов, только переходящих на уровень Lead, а верхняя — для экспертов с глубокой специализацией в оптимизации.
Сопроводительное письмо
I am writing to express my strong interest in the Lead AI/ML Engineer position at 84.51°. With over four years of experience in developing and scaling machine learning and optimization systems, I am particularly drawn to the Labs Innovation team's focus on bridging the gap between rapid prototyping and production-ready solutions. My background in Python, PyTorch, and Gurobi aligns perfectly with your requirement for a hands-on leader who can navigate complex optimization problems like vehicle routing and resource allocation.
Throughout my career, I have successfully led the end-to-end lifecycle of ML products, from initial research collaboration to implementing robust MLOps pipelines in Azure and Databricks. I pride myself on my ability to mentor junior engineers while maintaining a high bar for code quality and system architecture. I am excited by the prospect of leveraging Kroger's massive first-party dataset to drive customer-centric innovation and would welcome the opportunity to bring my technical expertise to your collaborative, research-forward culture.
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Описание вакансии
84.51° Overview:
84.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.
Powered by cutting-edge science, we utilize first-party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.
84.51° follows a 5‑day in‑office work schedule to support collaboration, alignment, and team connection.
Join us at 84.51°!
________________________________________________________
Lead AI/ML/Optimization Engineer (G3) – Labs Innovation Focus (P3227)
SUMMARY
As a Lead 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 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
#LI-SSS
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: 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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Навыки
- Python
- NumPy
- Pandas
- PySpark
- PyTorch
- TensorFlow
- Scikit-learn
- Pyomo
- Gurobi
- CPLEX
- OR-Tools
- MLflow
- Azure ML
- Databricks
- Docker
- Kubernetes
- CI/CD
- MLOps
Возможные вопросы на собеседовании
Проверка практического опыта в ключевой области вакансии — математической оптимизации.
Расскажите о самом сложном проекте по оптимизации (например, маршрутизация или распределение ресурсов), который вы вывели в продакшн. С какими ограничениями вы столкнулись?
Оценка навыков проектирования масштабируемых систем.
Как бы вы спроектировали MLOps-конвейер для модели, требующей частого переобучения на данных от 62 миллионов домохозяйств?
Вакансия требует тесного взаимодействия с исследователями.
Опишите ваш подход к переводу теоретических идей исследователей в стабильный и поддерживаемый программный код.
Оценка лидерских качеств и умения работать с кодом команды.
На что вы в первую очередь обращаете внимание при проведении Code Review для ML-проектов, чтобы обеспечить баланс между скоростью экспериментов и качеством кода?
Проверка умения выбирать инструменты под бизнес-задачи.
В каких случаях вы предпочтете использование коммерческого солвера (например, Gurobi) вместо open-source решений, и как вы обоснуете это решение бизнесу?
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- Страна
- США
- Зарплата
- 121 000 $ – 201 250 $