Machine Learning Platform Engineer Career Roadmap

1. 1

   Software Engineering Fundamentals

   6–8 weeks

   This role is software engineering first, ML-flavoured second. Real postings (Reddit's ML Feature Platform team especially) expect fluency in API design and distributed systems basics before any ML-specific tooling knowledge.

   Skills to learn

   • API design
   • Distributed systems basics
   • Python (deep)
   • Go or Scala (common secondary language)
   • Git & code review

   Start with

   • Designing Data-Intensive Applications (book)
   • Real Python – API Design Tutorials

   Next: One Cloud Platform & ML Certification

2. 2

   One Cloud Platform & ML Certification

   4–6 weeks

   Pick one major cloud (AWS, GCP, or Azure) and pair it with that platform's ML certification. Databricks' Machine Learning Associate cert is the single most directly relevant credential here — its exam explicitly covers feature stores and MLflow.

   Skills to learn

   • One major cloud platform (AWS/GCP/Azure)
   • Cloud ML services overview
   • Databricks Unity Catalog Feature Store

   Start with

   • Databricks Certified Machine Learning Associate
   • Google Cloud Professional Machine Learning Engineer

   Next: Feature Store Concepts & a Real Tool

3. 3

   Feature Store Concepts & a Real Tool

   4–5 weeks

   The feature store is the platform's centrepiece — a central, reusable repository of ML features shared across teams. Learn the concept, then get hands-on with Feast (open-source, self-managed) or understand Tecton (managed, sub-100ms serving).

   Skills to learn

   • Feature store architecture
   • Feast (open-source feature store)
   • Tecton (managed feature platform)
   • Online vs. offline feature serving

   Start with

   • Feast Documentation
   • Made With ML – Feature Store Module

   Next: Experiment Tracking

4. 4

   Experiment Tracking

   3–4 weeks

   MLflow and Weights & Biases are the two dominant real tools across every posting found — learn to instrument training runs, compare experiments, and make results reproducible for the ML engineers who'll depend on your platform.

   Skills to learn

   • MLflow tracking
   • Weights & Biases
   • Experiment reproducibility
   • Metric logging & comparison

   Start with

   • MLflow Documentation
   • Weights & Biases Quickstart

   Next: Model Registry & Versioning

5. 5

   Model Registry & Versioning

   3 weeks

   A model registry is how a platform tracks which model version is in production, staging, or archived — reinforced directly in Databricks' ML Associate certification content.

   Skills to learn

   • MLflow Model Registry
   • W&B Models
   • Model versioning & lineage
   • Staging → production promotion workflows

   Start with

   • MLflow Model Registry Docs

   Next: CI/CD & Orchestration for ML Pipelines

6. 6

   CI/CD & Orchestration for ML Pipelines

   5–6 weeks

   Orchestration tools that come up repeatedly in real postings: Kubernetes, Kubeflow, Metaflow, Argo Workflows, and Ray. Learn at least one deeply enough to build a CI/CD pipeline for a model end to end.

   Skills to learn

   • Kubernetes basics
   • Kubeflow or Metaflow
   • Argo Workflows
   • CI/CD for ML pipelines

   Start with

   • Metaflow Documentation
   • Kubeflow Documentation

   Next: Build & Document a Portfolio Platform Tool

7. 7

   Build & Document a Portfolio Platform Tool

   4–6 weeks

   Build a mini feature store or training-orchestration tool with real documentation — README, API docs, architecture diagram. This directly mirrors what real postings ask candidates to demonstrate: "build the reusable system... the feature store, the registry, the serving layer, the SDK."

   Skills to learn

   • End-to-end platform tool design
   • API/SDK documentation
   • Architecture diagramming
   • Explaining design trade-offs in interviews

   Start with

   • Uber Engineering – Michelangelo (reference architecture)
   • Valohai – Building ML Infrastructure at Netflix

jobroadmaps.com/roadmaps/ml-platform-engineer · Pay, demand and employer data for this role are further down the page.