At a Glance
- Tasks: Design and solve complex machine learning challenges with real-world applications.
- Company: Join a leading tech firm at the forefront of AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with chances to mentor and lead projects.
- Why this job: Make a significant impact by developing cutting-edge machine learning solutions.
- Qualifications: Master’s or PhD in relevant fields and 2+ years of hands-on ML experience.
The predicted salary is between 60000 - 80000 £ per year.
We’re hiring experienced Machine Learning Engineers and Applied ML Researchers to design, solve, and evaluate complex machine learning challenges that reflect real‑world ML workflows. This role requires strong hands‑on modeling expertise, the ability to develop high‑quality reference solutions, and deep familiarity with modern machine learning techniques across a variety of domains and data modalities.
What You’ll Do
- Develop end‑to‑end machine learning solutions for challenging prediction and modeling problems
- Analyze datasets and define appropriate modeling approaches, validation strategies, and evaluation metrics
- Perform exploratory data analysis, feature engineering, and data preprocessing
- Train, tune, and evaluate machine learning models across tabular, text, image, and time‑series datasets
- Develop strong reference solutions using industry‑standard machine learning techniques and best practices
- Review and validate the technical quality of machine learning projects and deliverables
- Document methodologies, assumptions, and evaluation results in a clear and reproducible manner
- Identify opportunities to improve model performance through systematic experimentation and iteration
Required Qualifications
- Master’s degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering, or a related field from a top‑tier university
- 2+ years of hands‑on experience developing, training, evaluating, and optimizing machine learning models in a professional or research setting
- Strong proficiency in Python and modern machine learning frameworks (e.g., scikit‑learn, XGBoost, LightGBM, PyTorch, TensorFlow)
- Demonstrated experience building end‑to‑end machine learning solutions, including data preparation, model development, validation, and evaluation
- Strong understanding of model evaluation metrics, validation methodologies, and experimental design
- Experience with one or more of the following areas: Tabular machine learning, Natural language processing, Computer vision, Recommendation systems, Ranking systems, Time‑series forecasting
- Ability to work independently on open‑ended machine learning problems and deliver high‑quality technical outputs
Preferred Qualifications
- PhD from a leading research university
- Experience at leading technology companies, AI labs, research institutions, or high‑growth startups
- Participation in competitive machine learning or data science competitions
- Experience optimizing models against performance‑based evaluation metrics
- Familiarity with advanced techniques such as ensembling, hyperparameter optimization, transfer learning, foundation model fine‑tuning, or reinforcement learning
- Publications, patents, or significant open‑source contributions in machine learning or AI
- Experience reviewing, mentoring, or evaluating the work of other machine learning practitioners
Machine Learning Engineer - Expert in London employer: Obsidian
Join a forward-thinking company that values innovation and expertise in the field of machine learning. With a collaborative work culture that encourages continuous learning and professional growth, you will have access to cutting-edge resources and opportunities to tackle real-world challenges. Located in a vibrant tech hub, our team thrives on creativity and diversity, making it an ideal environment for passionate individuals looking to make a meaningful impact.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer - Expert in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Obsidian!
✨Show Off Your Projects
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Obsidian.
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We think you need these skills to ace Machine Learning Engineer - Expert in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Obsidian, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Obsidian. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Obsidian
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Obsidian!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
