Senior ML Engineer

Shopmonkey's vision is to help every shop thrive by equipping them with the tools they need to run and grow their business. Our cloud based all-in-one shop management software takes owners and technicians from quote to cashing out a satisfied customer. Our software has a modern and intuitive UI and our backend is powered by the latest technologies so our clients can focus on the things they do best. As a Senior ML Engineer at Shopmonkey, you will be a part of a globally distributed engineering team working closely with your product and design counterparts. You will have the chance to work on the frontier of ML modeling, data pipelines, MLOps, and agentic AI systems to meet real-world auto shop needs. Shopmonkey has the structured data, workflows, and operational maturity to deliver ML powered systems that are not only intelligent but trusted and useful. You’ll move fast to bring ML systems from discovery all the way through production, helping to shape the future of the automotive care experience. For any Bay area based candidates, this would be hybrid with 2-3 days/week office at our Morgan Hill, CA office for collaboration. What you will do: Design, build, and ship production-ready ML models across a range of problem spaces: regression, classification, clustering, ranking, and recommendation systems. Conduct end-to-end development of ML systems: data gathering, experimentation, feature engineering, model training, evaluation, deployment, and monitoring. Define and track model performance metrics, run A/B tests, and iterate based on real-world feedback. Help design and implement shared feature stores so that reusable features can serve multiple models consistently in both batch and real-time contexts. Work within a modern MLOps environment to ensure scalable and reliable deployment of models. Contribute to training infrastructure, model versioning, and CI/CD pipelines for ML workflows. Work closely with data scientists and data engineers to develop data driven solutions that are high impact for businesses. Translate complex ML workflows into digestible updates for cross-functional stakeholders. Contribute to backlog velocity by owning appropriate tickets and delivering high-impact work in a collaborative, fast-paced environment. Implement NLP and LLM-powered components for sentiment analysis, real-time conversation evaluation, and behavior optimization. Contribute to analytics and predictive features such as no-show prediction and sentiment dashboards. Help build and ship AI agents that help automate key auto-shop business processes. We are looking for people who have: Minimum of 5+ years of industry experience in applied machine learning; advanced degrees (Master’s or PhD) may offset years of experience. Proven experience shipping models into production (not just proof-of-concepts or notebooks). Proficiency in Python; experience with ML frameworks like PyTorch or Tensorflow. Strong foundations in classical ML/DL. Including some of the following: regression, classification, clustering, ranking, feature engineering, model evaluation, and experimentation. Bachelor’s degree in a STEM field, or equivalent practical experience. Strong collaboration and communication skills—comfortable working with PMs, designers, engineers and other cross functional team members. Nice to have: Candidates should have some experience in one or more of the following areas. Understanding of MLOps principles: model versioning, orchestration, evaluation, monitoring, model serving, and CI/CD for ML. Understanding of MLOps, and experience with modern tooling like MLFlow, DVC, Airflow, etc. Experience with LLMs and NLP frameworks (e.g., TensorFlow, Hugging Face, LangChain). Experience with/interest in LLM workflows and agentic workflows Cloud infrastructure experience, (e.g. GCP, AWS). Familiarity with vector databases (e.g. Pinecone, pgvector) and embedding-based retrieval or similarity search. Strong SQL skills for working wi ... (truncated, view full listing at source)