DICK’S Sporting Goods is seeking a Machine Learning Engineer II to design, build, and deploy advanced machine learning systems and AI applications that drive business impact. In this role, you will focus on productionalizing causal inference and Bayesian modeling solutions, developing and maintaining ML pipelines within our people-centric, collaborative environment.
What You'll Do
- Design and develop machine learning architecture and model deployment pipelines for batch and streaming use cases, integrating traditional ML models with causal inference methods.
- Optimize and improve the performance of existing ML models and systems, ensuring scalability, reliability, and efficiency.
- Leverage cloud deployment architecture for deploying ML and causal inference models as APIs for real-time inference with caching.
- Develop and maintain APIs for ML models to facilitate integration with other systems and applications.
- Collaborate closely with the ML Platform team to develop and maintain the ML Platform to meet business and Technology objectives.
- Collaborate closely with data scientists and engineers to validate and ensure data quality in production data.
- Develop solutions to monitor and address model drift, performance degradation, and assumption violations in deployed models.
- Document model assumptions, design decisions, deployment steps, and monitoring protocols for reproducibility and governance.
What We're Looking For
- 3+ years of experience in machine learning engineering and/or data science strongly preferred.
- Hands-on experience developing, deploying, and maintaining machine learning models and pipelines in production environments.
- Expert understanding of Python and experience with ML and deep learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Knowledge of software engineering principles, including secure and reliable software development.
- Experience with model deployment tools (MLFlow, Docker, Kubernetes, FastAPI).
- Experience with data versioning tools and experiment tracking (Weights & Biases, MLFlow).
- Ability to analyze and optimize model performance, reliability, and scalability.
- Strong communication skills for collaborating with cross-functional teams and documenting technical work.
- Bachelor's Degree in Computer Science, Engineering, Statistics, Mathematics, or a related field or equivalent level preferred.
Nice to Have
- Familiarity with causal inference libraries (EconML, DoWhy, CausalML) is a plus.
- Experience working with media data is a plus.
Technical Stack
- Languages & Frameworks: Python, TensorFlow, PyTorch, scikit-learn
- MLOps & Deployment: MLFlow, Docker, Kubernetes, FastAPI
- Experiment Tracking: Weights & Biases
- Causal Inference: EconML, DoWhy, CausalML
Team & Environment
You will collaborate closely with the ML Platform team, data scientists, and engineers.
Benefits & Compensation
- Competitive total rewards package that could include incentive, equity and benefits.
- Compliance with all state paid leave requirements.
- Generous suite of benefits.
- Compensation Range: $76,500.00 - $124,600.00
Work Mode
This is a fully remote position open to global candidates.
DICK’S Sporting Goods is committed to creating an inclusive and diverse workforce, reflecting the communities we serve.
