What a Machine Learning Engineer does
A Machine Learning Engineer (MLE) builds, ships, and maintains ML systems that solve real business problems. You’ll design features and models, write production-grade code, deploy services, automate training pipelines, and monitor models in the wild.
A week in the role (typical tasks)
- Translate a business need into an ML problem and a measurable metric.
- Build and version a training pipeline (data prep, features, training, evaluation).
- Package a model with an API (batch or real-time) and deploy it with CI/CD.
- Set up monitoring: performance, drift, latency, costs, and alerts.
- Iterate: improve data quality, optimize inference speed, and reduce operational risk.
Day-to-day deliverables
- Reusable feature engineering code and documented data contracts.
- Model artifacts with metadata (version, metrics, lineage).
- Model service (REST/gRPC/batch job) with clear SLAs.
- Automated pipelines for training, evaluation, and deployment.
- Dashboards and alerts for data drift, model quality, and system health.
Who this is for
- Developers who enjoy both data and systems engineering.
- Data scientists who want to productionize and scale models.
- Ops/Platform engineers curious about ML systems and automation.
Prerequisites
- Comfortable with Python and basic data manipulation (NumPy/Pandas).
- Familiar with Git and terminal workflows.
- Basic statistics and ML concepts (train/val/test, overfitting, metrics).
Mini task: are you ready?
Pick a simple dataset (e.g., Titanic or Iris). In a clean Python environment, train a small model, save it to disk, load it back, and run a prediction. If you can do this in under 60 minutes, you’re ready to start this path.
Hiring expectations by level
Junior
- Can implement pipelines from templates and follow coding standards.
- Understands core metrics and avoids basic leakage errors.
- Deploys models with guidance and writes unit tests for data/feature code.
Mid-level
- Owns a service end-to-end: data contracts, model lifecycle, and monitoring.
- Designs CI/CD for ML and resolves performance bottlenecks.
- Champions reproducibility, versioning, and incident response.
Senior
- Architects ML platforms (features, training, serving) for scale and reliability.
- Leads cross-team initiatives and improves organizational ML velocity.
- Balances accuracy, cost, latency, and governance; mentors others.
Salary ranges
- Junior: $70k–$110k
- Mid-level: $110k–$160k
- Senior/Staff: $160k–$220k+
Varies by country/company; treat as rough ranges.
Where you can work
- Industries: fintech, e-commerce, health, logistics, media, SaaS, gaming, gov/NGO.
- Teams: product ML, growth/ads, recommendations/search, risk/fraud, platform ML.
- Company sizes: startups (generalist), scale-ups (domain-focused), enterprises (platform specialization).
Skill map (what you’ll learn)
- Python: production-grade data and model code.
- ML Frameworks: scikit-learn, PyTorch/TensorFlow for training/inference.
- Feature Stores Concepts: consistent offline/online features and lineage.
- Model Serving APIs: REST/gRPC/batch patterns and latency trade-offs.
- MLOps Basics: versioning, reproducibility, and experiment tracking.
- CI/CD for ML: automated testing, data validation, and deployments.
- Containerization (Docker): environment parity and portable services.
- Monitoring ML Systems: data/quality drift, latency, costs, alerts.
- Cloud Basics: storage, compute, networking, roles, and costs.
- Data Pipelines: scheduled/batch/stream jobs and data contracts.
Learning path
- Python: write clean, testable code; manage environments and packaging.
- ML Frameworks: train baseline models, track metrics, and save artifacts.
- Data Pipelines: build repeatable feature generation with clear schemas.
- Containerization (Docker): containerize training and inference.
- Model Serving APIs: deploy a simple real-time or batch service.
- MLOps Basics + CI/CD for ML: automate tests, checks, and releases.
- Feature Stores Concepts: ensure offline/online consistency.
- Monitoring ML Systems: add drift/quality/latency dashboards and alerts.
- Cloud Basics: deploy and operate cost-aware, secure workloads.
Mini task: production mindset
Take a small model you trained and add: input validation, logging, model version in every log, and a basic latency timer. This is the minimum bar for production services.
Portfolio projects you can build
1) Real-time sentiment API
Outcome: Containerized API that classifies text sentiment with health checks, versioned model artifacts, and latency under 100 ms for short texts.
- Includes: input schema validation, logging, and basic monitoring counters.
- Stretch: add a canary release and rollback plan.
2) Churn prediction pipeline
Outcome: Batch pipeline that computes features daily, trains weekly, evaluates drift, and writes predictions to a data store with lineage.
- Includes: feature definitions with tests and a drift dashboard.
- Stretch: implement threshold auto-tuning based on cost functions.
3) Image classification service
Outcome: GPU-enabled training with PyTorch and a fast inference service with batching.
- Includes: model registry entry with metrics and resource usage notes.
- Stretch: add A/B testing between two model versions.
4) Feature store demo
Outcome: Offline/online feature write/read paths with one feature reused across two models.
- Includes: point-in-time correct historical joins.
- Stretch: backfill job with data quality validations.
5) Recommendation batch job
Outcome: Nightly job that computes recommendations and exposes them via a lightweight read API.
- Includes: SLAs, retries, and failure alerts.
- Stretch: add bias checks on top-N results.
Interview preparation checklist
- Explain train/val/test strategy and how you prevent leakage.
- Compare metrics for imbalanced problems (PR AUC vs ROC AUC vs F1).
- Walk through your CI/CD for ML: tests, data checks, and approvals.
- Describe a monitoring plan: signals, thresholds, and on-call response.
- Show a repo with reproducible training and a one-command deploy.
- Discuss trade-offs: latency vs accuracy vs cost; batch vs real-time.
- Security basics: secrets handling, PII, access roles.
Mini task: 3-minute system design
Sketch on paper: traffic source → feature store → model service → cache → downstream. Mark SLAs, scale assumptions, and failure modes. Practice saying it clearly in 3 minutes.
Common mistakes (and how to avoid them)
- Silent data drift: add input validation, drift metrics, and alerts from day one.
- Unreproducible training: pin versions, seed randomness, capture configs and dataset hashes.
- Overfitting to offline metrics: validate with robust CV and monitor online metrics post-release.
- One-off feature code: centralize features with definitions, tests, and owners.
- No rollback plan: keep previous model hot and document rollback steps.
Next steps
Pick a skill to start in the Skills section below. Build a small project, then layer in automation and monitoring. Keep it simple, repeatable, and observable.