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Job market data

AI and machine learning engineering is by far the largest area we track, with over 40,000 live US postings in the past twelve months. The heaviest concentration is in the San Francisco Bay Area, New York, Seattle, Austin, and Boston — though roughly one in eight senior ML and AI engineering postings is fully remote, particularly at AI-native product companies. Mid-senior base compensation generally sits in a $150K–$230K band before equity, and the strongest mid-senior MLEs we place are those who can own a model from framing through long-term operation, not just the training loop in the middle.

Key responsibilities

Design, build, and deploy machine learning models that solve clearly defined business problems end-to-end
Partner with data engineers, ML engineers, and product teams to move models from notebook to reliable production service
Implement and maintain MLOps practices — CI/CD, model registration, automated retraining, and live monitoring
Evaluate model performance against business KPIs, not just offline metrics, and iterate based on production behavior
Contribute to team decisions on model architecture, framework choices, and the right technical trade-offs for the problem at hand
Write production-grade Python and collaborate in code review with both ML and software engineers
Own the reliability and observability of the models you ship — drift, cost, and latency are part of the job
Share knowledge across the team through design reviews, write-ups, and informal mentoring

Candidate requirements

3+ years of hands-on experience shipping machine learning models to production
Strong Python with PyTorch or TensorFlow, and experience with modern ML tooling (MLflow, Weights & Biases, or equivalent)
Solid SQL and comfort working with large-scale data in Snowflake, BigQuery, Databricks, or similar
Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML)
Strong understanding of model evaluation, drift detection, and production monitoring
Clear communication — able to translate ML results to non-technical stakeholders

Machine Learning Engineer