Machine Learning Engineer Job Description Template (Free, 2026)

Machine Learning Engineer [Company Name] - [City, State] / Remote Full-Time | AI / ML Platform Team | Reports to: Head of AI or VP Engineering About the Role: We are hiring a Machine Learning Engineer to build the models and infrastructure powering [describe your AI features - e.g., recommendations, search, anomaly detection, or generative AI features]. You will work across the full ML lifecycle: data preparation, model training and evaluation, deployment to production, and ongoing monitoring. You bring strong software engineering fundamentals alongside deep ML expertise, and you take ownership of model quality from research to production. Responsibilities: - Design and implement ML models for [classification / regression / ranking / NLP / generative AI / computer vision] use cases - Build end-to-end training pipelines using PyTorch or TensorFlow, from raw data ingestion through feature engineering and model evaluation - Deploy trained models as low-latency REST API endpoints or batch inference services using FastAPI, TorchServe, or BentoML - Build and maintain the feature store and feature engineering pipelines that feed models in both training and serving contexts - Implement A/B testing and shadow scoring frameworks to safely roll out model updates - Monitor model performance in production, detect data drift and distribution shift, and trigger retraining workflows when needed - Collaborate with Data Scientists to translate research prototypes into production-ready, maintainable code - Evaluate and integrate LLM-based capabilities including fine-tuning, RAG architectures, and vector search using tools like LangChain, Pinecone, or Weaviate - Participate in MLOps infrastructure decisions including experiment tracking (MLflow, W&B), model registry, and orchestration - Write thorough documentation for models, training procedures, and API contracts Required Qualifications: - 3+ years of experience building and deploying ML models in production - Strong Python skills with hands-on experience in PyTorch or TensorFlow - Experience building data preprocessing and feature engineering pipelines at scale - Solid understanding of core ML concepts: supervised and unsupervised learning, regularization, cross-validation, and evaluation metrics - Familiarity with model serving architectures and API design for low-latency inference - Experience with experiment tracking and reproducibility tooling (MLflow, W&B, or similar) - Comfortable with SQL and working with large datasets - Proficiency with Git and collaborative development workflows Nice to Have: - Experience fine-tuning or working with large language models (LLMs) using PEFT, LoRA, or RLHF - Familiarity with vector databases (Pinecone, Weaviate, Chroma) and RAG pipeline design - Background in distributed training using Horovod, DeepSpeed, or FSDP - Experience with MLOps platforms such as SageMaker, Vertex AI, or Azure ML - Advanced degree in Computer Science, Statistics, or a related quantitative field Compensation and Benefits: - Base salary: [Salary Range] (see benchmarks below) - Equity: [Stock options / RSUs] - Health, dental, and vision insurance - [X] days PTO plus public holidays - Remote-friendly work environment - GPU compute budget for research and experimentation - Annual conference and learning budget About [Company Name]: [Write 2-3 sentences about your company, AI roadmap, and what the engineer will build.] [Company Name] is an equal opportunity employer committed to a diverse and inclusive team.