Senior ML Engineer
New
S
ShopmonkeyAutomotive
Remote - California; Remote - Colorado; Remote - Massachusetts; Remote - North Carolina; Remote - Texas; Remote - WashingtonFull-TimeSenior
Salary165,000 - 200,000 USD per year
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Job Details
- Experience
- Minimum of 5+ years of industry experience in applied machine learning; advanced degrees (Master’s or PhD) may offset years of experience.
- Required Skills
- AWSPythonSQLGCPMachine LearningMLFlowPyTorchSnowflakeAirflowTensorflowDeep LearningMLOpsLangChain
Requirements
- 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.
- 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 with large-scale data.
- Experience designing or contributing to feature stores (e.g. Feast, VertexAI Feature Store, Tecton) for shared, reusable feature pipelines.
Responsibilities
- 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.
- Help build and ship AI agents that help automate key auto-shop business processes.
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