Machine Learning Engineer
TechnologyYou take the models data scientists build in Jupyter notebooks and somehow make them work in production without catching fire. You're half software engineer, half data scientist, and fully responsible when the recommendation engine starts suggesting garbage. Your life is pipelines, feature stores, and model serving infrastructure.
Salary Range
Low
$110k
Median
$165k
High
$260k
10-Year Growth
much faster
US Workers
50K
Education
Master's in CS/ML/Statistics (or strong portfolio + industry experience)
Environment
remote
Tools & Technical Skills
- ▸Python (scikit-learn, PyTorch, TensorFlow)
- ▸Model training, evaluation, and hyperparameter tuning
- ▸Feature engineering and data pipeline design
- ▸Cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
- ▸Experiment tracking (MLflow, Weights & Biases)
- ▸Docker and Kubernetes for model serving
- ▸SQL and Spark for data processing
People & Mindset Skills
- ▸Statistical reasoning
- ▸Problem decomposition
- ▸Collaboration with data scientists
- ▸Written communication
- ▸Intellectual curiosity
Learn the skills
Courses and certifications to get you job-ready
Python (scikit-learn, PyTorch, TensorFlow)
Cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
Docker and Kubernetes for model serving
SQL and Spark for data processing
What you'll actually do
- 01Deploy ML models to production and watch them behave nothing like they did in testing
- 02Build data pipelines that transform messy real-world data into something a model can digest
- 03Debug why the model's predictions are great on Tuesdays but terrible on Fridays
- 04Optimize inference latency because the model needs to respond in under 100ms
- 05Monitor for data drift and model degradation — your model is slowly getting dumber and you need to catch it
- 06Translate a data scientist's Jupyter notebook into production-ready code without crying
Related Shifts
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