Machine Learning Engineer
Build the systems that learn from data and make predictions.
What does a Machine Learning Engineer do?
Machine learning engineers build, train, and deploy models that improve with data — from recommendation engines to fraud detection to language models. You sit at the intersection of software engineering and data science, making sure models actually work in production. This path takes time to get into but pays off significantly. Good fit if you enjoy deep technical problems and want to specialise in something most people never attempt.
Who it fits
People who enjoy mathematics, like building things that improve over time, and want to work on some of the most impactful technology being built today.
Suits structured learners who enjoy mathematics and working through courses with strong theoretical foundations.
Skills you need
Tools you will use
Starter projects
Good projects to practise and add to a portfolio.
- Train a classifier on the Iris or MNIST dataset
- Build a sentiment analyser with scikit-learn
- Fine-tune a pre-trained model with Hugging Face
- Deploy an ML model as a REST API with FastAPI
// ROADMAP
Machine Learning Engineer career roadmap
Estimated time: 12–18 months
Badges show what each resource is. Only items marked certification lead to a professional credential — everything else is learning material.
Python, Linear Algebra & Statistics
8–10 weeksML engineering requires real mathematical foundations. Learn NumPy, pandas, linear algebra (vectors, matrices), and probability. 3Blue1Brown makes the maths intuitive before you write any code.
Machine Learning Fundamentals
8–10 weeksAndrew Ng is the best ML teacher in the world. Work through his specialisation on Coursera. Focus on understanding the algorithms, not just running them.
Deep Learning with PyTorch or TensorFlow
6–8 weeksLearn neural networks, CNNs, RNNs. Pick PyTorch (preferred in research) or TensorFlow (preferred in production). fast.ai teaches this from a practical angle — you build real things first.
MLOps & Model Deployment
4–6 weeksBuilding models is only half the job. Learn how to deploy them, monitor them, and retrain them when they drift. MLflow for tracking, FastAPI for serving, Docker for containerisation.
Projects, Kaggle & Job Ready
OngoingBuild a portfolio of end-to-end ML projects — from raw data to deployed API. Compete on Kaggle to benchmark your skills. Roles include ML engineer, applied scientist, and AI engineer.
// START LEARNING
Machine Learning Engineer learning resources
Videos first — they're the fastest way to get moving — then reading, hands-on practice and courses. Only resources badged professional certification award a formal credential.
Machine Learning with Python and Scikit-Learn
freeCodeCamp · 18 hours
An end-to-end introduction to model training, evaluation and practical machine learning.
Learning resource. This does not award a professional certification.
Kaggle Learn
Kaggle
Practical micro-courses in Python, pandas, SQL, machine learning, data visualisation and AI.
Certificate availability depends on the course provider.
Machine Learning Crash Course
Google for Developers
A practical introduction to ML concepts with interactive visualisations, exercises and production guidance.
Certificate availability depends on the course provider.
Hugging Face LLM Course
Hugging Face
A free course covering transformers, model fine-tuning, datasets, tokenizers and sharing models.
Certificate availability depends on the course provider.
PyTorch Tutorials
PyTorch
Official tutorials from tensor fundamentals through neural networks, computer vision and distributed training.
Learning resource. This does not award a professional certification.
OpenCV-Python Tutorials
OpenCV
Official tutorials for image processing, feature detection, video analysis and computer vision.
Learning resource. This does not award a professional certification.
// PRACTISE
Gain Machine Learning Engineer work experience
Build practical evidence before your first role. Provider terms and eligibility can change.
Extern · Remote company externships
externship · REMOTE
Work on structured real-company projects with flexible remote delivery.
GitHub · Good first issues
open-source · REMOTE
Find newcomer-labelled issues and build evidence of collaboration in public repositories.
Devpost · Hackathons and build challenges
hackathon · MIXED
Join time-boxed challenges, form teams and ship portfolio-ready prototypes.
// FIND WORK
Find Machine Learning Engineer jobs
Start with “Machine Learning Engineer”. Platforms without stable public search URLs open with this suggested phrase.
LinkedIn Jobs
Broad professional job search with keyword, location, experience-level and remote filters.
Wellfound
Startup and technology roles with company and compensation context.
Dice
Specialist technology roles across engineering, data, security and infrastructure.
Built In
Technology and startup job discovery, including remote and city-focused listings.
UK visa and international opportunities
Sponsorship depends on the employer, vacancy and current immigration rules. Use the linked official guidance and verify every role before applying.
Going independent
ML consulting is in extremely high demand. Most companies know they should use ML but do not know how — a skilled ML engineer can bridge that gap.
ML Engineering Consulting
Medium effortHelp businesses build, deploy, and maintain machine learning systems in production.
Examples
- Building recommendation systems for e-commerce
- Deploying ML models in production (FastAPI + Docker)
- Data pipeline and feature store design
- Model monitoring and retraining systems
Getting started
- 1. Build an end-to-end ML project and document it as a case study
- 2. Position around a specific industry where ML adds clear ROI
- 3. Write LinkedIn content on practical ML engineering — not AI hype
- 4. Reach out to data teams who have models in notebooks but not in production
AI Product Building
High effortUse ML expertise to build AI-powered products that solve specific problems.
Examples
- Specialised AI applications for niche industries
- ML model marketplace or fine-tuning service
- AI-powered analytics products
- Dataset creation and curation businesses
Getting started
- 1. Find a domain problem where ML provides clear improvement over existing solutions
- 2. Build MVP with existing models rather than training from scratch
- 3. Sell enterprise contracts rather than SaaS initially for faster revenue
Communities & tools
// EARNING POTENTIAL
Earning potential
2025Salaries are rising fast as demand for production ML systems grows. Engineers who can ship and maintain models in production earn more than those focused only on model building.
Freelance rates
Day rate
£400–£950
Hourly rate
£50–£120
What affects salary
- Production MLOps experience commands a premium over pure research skills
- LLM fine-tuning and RLHF experience is the highest-value specialisation currently
- PhD is beneficial for research roles but not required for engineering positions
- Kaggle Grandmaster status or top-ranked competition results are strong signals
- Financial services and biotech ML engineering pays above the UK average
🌍 US ML engineers at top labs earn $180k–$300k+. UK AI companies like DeepMind, Wayve, and others offer competitive but lower packages.
Based on: LinkedIn Salary · Glassdoor UK · Levels.fyi · AI Jobs board. All figures approximate.
// RESOURCES
Free resources to get started
Recommended starting points — no payment required.
- Course
- Course
- Course
- Tutorial
- Video
Paid courses worth considering
These are not required — free resources above can get you far.
- Course
- Course
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