SMARTFOX
Machine Learning Engineer
Get notified of more jobs like this · No spam, ever
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
The Role
You'll design, build, deploy, and monitor machine learning systems that deliver real value to our customers. You'll work alongside data scientists, data engineers, and software engineers, with plenty of opportunity to learn and grow, whether you're early in your career or already have a few years of hands-on experience.
What You'll Do
- Develop, train, and evaluate machine learning models for [e.g. recommendation, forecasting, NLP, computer vision, anomaly detection]
- Build and maintain production-ready ML pipelines for training, deployment, and inference
- Turn data science prototypes into scalable, reliable services
- Monitor model performance in production, detecting drift and retraining as needed
- Work with data engineers to build and improve feature pipelines and datasets
- Write clean, tested, and well-documented Python code
- Run experiments, track results, and share findings with technical and non-technical stakeholders
- Contribute to MLOps best practices, including CI/CD, versioning, and reproducibility
- Apply responsible AI principles, including fairness, explainability, and UK GDPR compliance
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
What We're Looking For
Essential
- 0 to 4 years' experience in machine learning, data science, software engineering, or a similar role (graduates and career changers with strong projects are welcome)
- Strong Python skills and experience with libraries such as scikit-learn, pandas, and NumPy
- Hands-on experience with a deep learning framework (PyTorch or TensorFlow)
- Solid understanding of core ML concepts: supervised and unsupervised learning, model evaluation, feature engineering, overfitting, and bias/variance
- Good grasp of statistics and maths fundamentals
- Working knowledge of SQL and Git
- Strong problem-solving skills and the ability to communicate technical ideas clearly
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Desirable
- Experience deploying models via APIs (e.g., FastAPI, Flask) or batch pipelines
- Familiarity with a cloud platform (AWS, Azure, or GCP) and ML services such as SageMaker, Vertex AI, or Azure ML
- Exposure to MLOps tools such as MLflow, Kubeflow, Airflow, or Weights & Biases
- Experience with Docker, Kubernetes, or CI/CD pipelines
- Experience with LLMs, NLP, or generative AI (e.g., Hugging Face, LangChain, RAG)
- Degree or Master's in Computer Science, Maths, Statistics, Engineering, or a related field, or equivalent practical experience
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Location