How to Become a Machine Learning Engineer

ML engineers productionise models: training pipelines, feature stores, serving infrastructure and evaluation. Software engineering rigour matters as much as modelling. Day to day, the work splits between building new capability and keeping existing systems trustworthy — the ratio shifts toward the latter as a company matures.

Interviews for these roles are usually four to six stages: recruiter screen, a technical screen on PyTorch or Python, a deeper practical or design round, and a hiring-manager conversation about scope and ownership.

Months 1–4: get fluent in PyTorch and Python. Build two projects that you could explain end to end in an interview, including what you'd change under load.

Months 5–8: add MLOps and Feature Engineering. Start reading real job descriptions weekly and note the vocabulary gaps between them and your resume.

Months 9–12: apply in volume within the first 72 hours of a posting going live, tailor each resume to the posting's exact terms, and run every version through an ATS check before submitting.