Senior Machine Learning Engineer (REMOTE)
About The Position
We have many opportunities available on our other career site pages. Click here to link to our careers page! Signet Jewelers is the world's largest retailer of diamond jewelry, operating more than 2,800 stores worldwide under the iconic brands: Kay Jewelers, Zales, Jared, H.Samuel, Ernest Jones, Peoples, Banter by Piercing Pagoda, Rocksbox, JamesAllen.com and Diamonds Direct. We are a people-first company and this core value is at the heart of everything we do, from empowering our valued team members, to collaborating with our customers, to fostering the communities in which we live and serve. People – and the love their actions inspire – are what drive us. We’re not only proud of the love we inspire outside our walls, we’re especially proud of the diversity, inclusion and equity we’re inspiring inside. There are dynamic career paths awaiting you – rewarding opportunities to impact the lives of others and inspire love. Join us! We are looking for a hands-on Senior Machine Learning Engineer to operationalize advanced models across elasticity, uplift, forecasting, and other AI use cases. This role sits at the intersection of Data Engineering, MLOps, and Applied Machine Learning, ensuring that models developed by Data Science teams are production-ready, performant, and scalable.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Applied ML, or equivalent experience
- 3–6 years of industry experience in ML Engineering or MLOps
- Experience in retail analytics — such as demand forecasting, pricing, promotions, inventory optimization, customer segmentation, or e-commerce metrics — is highly preferred
- Strong programming skills in Python (pandas, PySpark, FastAPI, etc.)
- Experience building and managing ETL/ELT pipelines
- Hands-on experience deploying ML systems on AWS (SageMaker, Lambda, ECS/EKS, S3, Kinesis/Streams, etc.)
- Experience with CI/CD tools (GitHub Actions, CodePipeline, Jenkins, etc.)
- Familiarity with monitoring and observability for ML (model drift, feature drift, inference latency, cost monitoring)
- Experience with containerization & orchestration (Docker, Kubernetes) is a plus
Nice To Haves
- Experience building data products or ML-powered APIs that expose predictions or insights back to business applications.
- Experience with feature stores (SageMaker Feature Store / Feast)
Responsibilities
- Design, build, and automate production-grade data pipelines to support elasticity, uplift, and other analytical models
- Implement clean, reusable data transformation logic that ensures consistency across modeling, analytics, and reporting layers
- Develop and maintain real-time inference services (e.g., AWS SageMaker endpoints) to allow business teams and applications to consume model outputs seamlessly
- Establish MLOps best practices, including: Model performance monitoring and drift detection Automated retraining and evaluation pipelines Feature / model versioning and lineage tracking
- Enable CI/CD for ML deployments, ensuring reliability, reproducibility, and rapid iteration
- Partner with Data Science teams to accelerate experimentation and automate recurring workflows
- Identify and drive automation opportunities across the broader AI & Data Science ecosystem to improve scalability, reliability, and cost efficiency
Benefits
- Comprehensive healthcare, dental, and vision insurance to keep you and your family covered - with benefits active on day 1 of employment
- Generous 401(k) matching after just one year to help secure your financial future
- Ample paid time off, plus seven holidays to recharge and unwind
- Exclusive discounts on premium merchandise just for you
- Dynamic Learning & Development programs to support your growth
- And more!
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Number of Employees
5,001-10,000 employees
