Roles › Chief Executive Officer › Chief Data (& Analytics) Officer › Data Science & ML › ML Engineers / MLOps

Data Science & ML

What does a Machine Learning Engineer do?

Turn prototype models into reliable, monitored services.

High demand Team: ML Engineers / MLOps Reports into: Data Science & ML

What they actually do

Training and inference pipelines.
Deploy models as APIs or batch jobs; version and roll back.
Monitor drift, latency, cost.
Feature stores and model registries.

Hiring demand

High demand. Large, steady volume of postings across industries. A realistic target for an active job search.

Ad space

Who they work with

Data scientistsData engineersDevOps

Typical tools

PythonDocker / KubernetesMLflowCI/CD

Skills to get there

Software engineering
ML fundamentals
Cloud
DevOps

Title ladder

Common titles for this role at each level. Companies vary; see Role titles decoded.

Level	Typical titles
Entry	Junior / Associate Machine Learning Engineer · Machine Learning Engineer I
Mid	Machine Learning Engineer · Machine Learning Engineer II
Senior	Senior Machine Learning Engineer · Lead Machine Learning Engineer
Expert (IC track)	Staff / Principal Machine Learning Engineer
Management track	Manager, ML Engineers / MLOps → Director of ML Engineers / MLOps → VP

See the free training path →

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How to get here

Salary (US) as of 2026-11

Median$136K

Typical range$100K–$200K

Senior / manager$251K

Sources:

Mapped to BLS 15-1252 (no MLE-specific code). Posted-salary and big-tech total comp (Levels.fyi MLE title median $285,800) run well above the BLS/Payscale base figures.

Experience to get here

Entry level1-3 years (software or data science background)

Typical title3-6 years; most postings ask for about 5 years hands-on

Bachelor's in CS or related is typical (79% of postings require a degree); PhD appears in ~1/3 of principal-level postings. Production experience matters more than degree level.

Typical prior roles

Software EngineerData ScientistData EngineerMLOps / Platform Engineer

Must have

Python at a software-engineering level (79% of postings)
Deep learning frameworks, chiefly PyTorch and Hugging Face Transformers (56% name deep learning)
Cloud deployment (AWS SageMaker, GCP, Azure) named in 42% of postings
MLOps: Docker, CI/CD, model serving, monitoring and versioning (29%)
Designing and consuming APIs; collaborating with data scientists and DevOps

Milestones people hit first

Deployed a model as an API or batch job with monitoring and rollback
Built a training/inference pipeline others can rerun, with versioned data and models
Cut inference latency or cost on a production service and measured it
Served a transformer/LLM model in production, including evaluation

Certifications

AWS Certified Machine Learning Engineer - AssociateAmazon Web Services

Preferredin postings: sometimes

Exam fee	$150
Training	AWS Skill Builder free tier; paid prep optional
Renewal	recertify by exam every 3 years
Typical total	$150-400

Time: 6-10 weeks part-time
Prerequisites: none; AWS suggests 1 yr with SageMaker and 1 yr ML engineering

Replaces the retired ML Specialty; AWS is the most-named cloud in ML postings, and SageMaker is explicitly preferred in some.

Google Cloud Professional Machine Learning EngineerGoogle Cloud

Preferredin postings: sometimes

Exam fee	$200
Training	Google Skills learning path
Renewal	valid 2 years; renew via Google Skills courses
Typical total	$200-500

Time: 6-10 weeks part-time
Prerequisites: none; 3+ yrs industry and 1+ yr GCP recommended

Covers Vertex AI and MLOps; valued at GCP-based companies.

Databricks Certified Machine Learning AssociateDatabricks

Nice to havein postings: sometimes

Exam fee	$200
Training	Databricks Academy self-paced
Renewal	valid 2 years; retake current exam
Typical total	$200-400

Time: 4-6 weeks part-time
Prerequisites: none; ~6 months hands-on recommended

MLflow, feature store and Spark ML are core MLOps tools at Databricks shops.

Training & learning

Deep Learning Specialization DeepLearning.AI on Coursera · course · Coursera subscription ($49/mo or Coursera Plus $239/yr); financial aid available · about 3 months at 10 hrs/weekDeep learning appears in 56% of ML engineering postings; this is the standard structured route.
Machine Learning Specialization FREE Stanford Online / DeepLearning.AI on Coursera · course · free to audit; $49/mo for certificate · about 95 hoursML fundamentals for engineers coming from software; the modeling half of the job.
DeepLearning.AI short courses (serving, RAG, evaluation, agents) FREE DeepLearning.AI · learning path · short courses free · 1-2 hours eachFast, hands-on coverage of LLM serving and eval patterns that postings now list alongside classic MLOps.
The ML Engineering Job Market: 13,800 Postings Analyzed FREE Axial Search · community · free · 20 minutesTells you which skills and seniority bands employers post for, so you can target gaps.
AWS Machine Learning Engineer - Associate exam prep FREE Amazon Web Services (Skill Builder) · learning path · free tier; exam $150 · 6-10 weeksStructured path through SageMaker, pipelines and monitoring that doubles as cert prep.

Sources & notes

No certification is 'often' required: the Greenhouse Software senior MLE posting lists none, and the 13,776-posting Axial analysis does not report cert frequency. 'Sometimes' ratings rest on AWS appearing in ~20% of adjacent DS postings (365 Data Science) and SageMaker being named as preferred in the Greenhouse posting. AWS ML Specialty retired 3/31/2026; TensorFlow certificate exam is closed. Greenhouse posting salary was in CAD ($142.7k-$214.1k) so not used for US figures.

Figures are US, from the sources listed, and change often; verify before deciding.

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