Senior Machine Learning Engineer in United

Our client is transforming grocery shopping by giving people time back - shoppers handpick fresh groceries and household essentials and deliver them in as little as one hour. We’re hiring a Senior Machine Learning Engineer for our Personalization Platform team within the Membership organization. You’ll partner closely with Data Scientists to design, deploy, and scale personalized recommendation and ranking models that drive engagement across the Shipt marketplace. This is a hands-on senior technical role spanning architecture, ML infrastructure, and production systems - from training and serving pipelines to CI/CD and monitoring. If you have an AI-first mindset and enjoy building platforms that make other engineers and scientists more effective, this is your team.

• MS or PhD in Computer Science, Engineering, Mathematics, or equivalent practical experience. \n

• 5+ years in machine learning and backend software engineering. \n

• Strong proficiency in Python, plus at least one other backend (Go or Java ). \n

• Deep understanding of user modeling, embeddings, similarity search, and ranking models. \n

• Strong experience with serving architectures (REST/gRPC APIs, model servers) and low-latency inference. \n

• Hands-on experience with modern AI development tooling - MCP servers/clients, PydanticAI, LangGraph, LiteLLM, or similar. \n

• Experience with ML pipeline and experiment-tracking tools (MLflow, Kubeflow, Airflow, Weights & Biases, or similar). \n

• Solid grasp of distributed systems, microservices, and system design. \n

• Cloud experience (AWS, GCP, or Azure) and containerized development (Docker, Kubernetes). \n

• Experience with SQL and NoSQL databases and large-scale datasets. \n

• Familiarity with the full data science workflow - data ingestion, feature engineering, experimentation, deployment, and monitoring., * Strong written and verbal communication; able to explain ML results and applications to scientists, engineers, and business partners.

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• Partner with Data Scientists, Product Managers, and peer engineering teams to build and ship AI product features \n

• Design, build, and maintain scalable ML infrastructure and production systems, including batch and streaming data pipelines for model training and serving \n

• Deploy, serve, and monitor models in public cloud environments via microservices that meet throughput and latency requirements \n

• Define best practices for AI product development: pipeline automation, model versioning, deployment, and scalable serving \n

• Design and manage CI/CD pipelines for machine learning deployments \n

• Integrate models into search and ranking systems (e.g., Elasticsearch first-stage retrieval and downstream ranking services) \n

• Own accountability for the implementation of AI product features end to end \n

• Use modern monitoring and telemetry tooling to identify, assess, and prioritize platform issues \n

• Support experimentation and A/B testing infrastructure, offline evaluation harnesses, and search-quality reporting \n

• Stay current on emerging AI/ML technology and bring the best of it to the team \n

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Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.