Senior Applied Machine Learning Scientist - Recommendations

Work Location Type: Hybrid

About Grainger:

W.W. Grainger, Inc., is a leading broad line distributor with operations primarily in North America, Japan and the United Kingdom. At Grainger, We Keep the World Working® by serving more than 4.5 million customers worldwide with products and solutions delivered through innovative technology and deep customer relationships. Known for its commitment to service and award-winning culture, the Company had 2024 revenue of $17.2 billion across its two business models. In the High-Touch Solutions segment, Grainger offers approximately 2 million maintenances, repair and operating (MRO) products and services, including technical support and inventory management. In the Endless Assortment segment, Zoro.com offers customers access to more than 14 million products, and MonotaRO.com offers more than 24 million products. For more information, visit

Compensation

The anticipated base pay compensation range for this position is $121,500.00 to $202,500.00. This position is not eligible for any form of OPT sponsorship now or in the future. Individuals requiring OPT sponsorship should not apply.

Rewards and Benefits:

With benefits starting on day one, our programs provide choice and flexibility to meet team members' individual needs, including:

• Medical, dental, vision, and life insurance plans with coverage starting on day one of employment and 6 free sessions each year with a licensed therapist to support your emotional wellbeing.
• 18 paid time off (PTO) days annually for full-time employees (accrual prorated based on employment start date) and 6 company holidays per year.
• 6% company contribution to a 401(k) Retirement Savings Plan each pay period, no employee contribution required.
• Employee discounts, tuition reimbursement, student loan refinancing and free access to financial counseling, education, and tools.
• Maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and up to 4 weeks paid leave for non-birth parents.

For additional information and details regarding Grainger’s benefits, please click on the link below:

The pay range provided above is not a guarantee of compensation. The range reflects the potential base pay for this role at the time of this posting based on the job grade for this position. Individual base pay compensation will depend, in part, on factors such as geographic work location and relevant experience and skills.

The anticipated compensation range described above is subject to change and the compensation ultimately paid may be higher or lower than the range described above.

Grainger reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion at any time, consistent with applicable law.

Position Details:

Grainger's Product Discovery team seeks a seasoned Senior Applied ML Scientist to develop cutting-edge search and recommendation solutions. Leveraging technologies such as Product Graphs, Deep Learning, Graph Neural Networks (GNNs), Large Language Models (LLMs), and Embeddings, our goal is to streamline operational efficiency and facilitate seamless customer experiences. At the heart of our mission, "We Keep the World Working," lies the crucial role of Search and Recommendations. Our innovative solutions enable customers to navigate through product categories effortlessly, discover premium products tailored to their needs, and access the most pertinent information, empowering them to make informed purchasing decisions confidently. We are open to a candidate who will work remotely.

You Will:

• Design, build, and maintain advanced machine learning models for Search, Recommendations, and Product Associations, ensuring scalability, efficiency, and business impact.
• Develop and deploy deep learning and embedding-based models such as DSSM, transformer-based re-rankers, and retrieval + rerank pipelines.
• Build and maintain product-product association systems (e.g., co-viewed, substitutes, complements) using collaborative filtering, GNNs, and rule-enhanced graph methods.
• Leverage GNNs, LLMs, and multimodal embeddings enhance product discovery and personalization across Grainger’s digital experience.
• Apply LLMs to niche tasks such as recommendation quality verification and latent feature extraction.
• Deploy ML models on Kubernetes-based GPU/CPU clusters , ensuring robust and efficient inference in production.
• Manage the full ML lifecycle , from data mining (including trillion-row SQL/Snowflake queries) to model training, evaluation, monitoring, drift detection, and A/B testing.
• Collaborate cross-functionally with ML scientists, engineers, and product managers to integrate models into production systems and drive roadmap alignment.
• Design robust training datasets and labeling strategies to support model development and evaluation.
• Stay current with state-of-the-art ML research and tools , incorporating relevant innovations into production work.
• Share knowledge with peers to foster continuous learning and team growth.

You Have:

• Minimum of 3 years of experience in the industry delivering ML solutions
• PhD or Master's degree in a field such as Applied Mathematics, Physics, Engineering, Computer Science, Electrical Engineering or equivalent experience
• Ability to effectively communicate technical solutions to engineering teams and business audiences
• Experience developing recommendation systems, including candidate generation and ranking, with a strong focus on real-time, low-latency infrastructure and performance
• Skills in large-scale data processing and analysis using tools like PySpark.
• Experience with deep learning frameworks such as PyTorch, Jax, TensorFlow, Keras along with libraries like Hugging Face and Sentence-Transformers to train models efficiently.
• Demonstrated proficiency in state-of-the-art deep learning (e.g. transformer-based models) and/or causal inference models
• Understanding of LLMs, including their development and application in processing and understanding natural language, to enhance search and recommendation capabilities
• Experience wrapping models in C/Python code and serving them as APIs
• Experience deploying models into the cloud with tooling like Docker & Kubernetes
• Experience automating data pipeline deployments and refresh processes using orchestration tools such as Airflow and Bash Scripting

Preferred:

• Experience with sequential user behavior modeling, including next-item or next-click prediction, in recommendation applications.
• Experience with GNNs to model relationships and interactions within data, crucial for building sophisticated recommendation systems based on Product Graphs
• Experience delivering recommendation system solutions at scale in e-commerce settings, from development through production deployment.
• Familiarity with using Graphs for in-session recommendation or personalization
• Understanding of LLMs, including their development and application in processing and understanding natural language, to enhance search and recommendation capabilities
• Experience optimizing inference speeds of LLMs via quantization, distillation or other means when served as endpoints

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, protected veteran status or any other protected characteristic under federal, state, or local law. We are proud to be an equal opportunity workplace.

We are committed to fostering an inclusive, accessible work environment that includes both providing reasonable accommodations to individuals with disabilities during the application and hiring process as well as throughout the course of one’s employment, should you need a reasonable accommodation during the application and selection process, including, but not limited to use of our website, any part of the application, interview or hiring process, please advise us so that we can provide appropriate assistance.

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