Machine Learning Engineer

Elecnor Deimos
Harwell
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Generative AI Data Scientist — Remote (SC Cleared)

Hybrid Data Engineer: Cloud Pipelines & Data Lake

Head of Data Science & ML Engineering

Sr. Data Engineer

Machine Learning Engineer

DEIMOS is looking for an engineer to join the Computer Vision/Artificial Intelligence (CV/AI) Competence Centre of the Avionics Business Unit, Flight Systems Directorate.

This role focuses on supporting Deimos’ AI/CV flight systems team in researching, developing, deploying and scaling our computer vision portfolio for onboard processing applications in Space. You will work on Machine Learning projects and products throughout their lifecycle – from early-phase R&D activities to productization and deployment.

The work of the AI/CV Competence Centre is oriented to the design, development, specification, and validation of Computer Vision solutions for embedded flight segment applications, including real-time advanced onboard data processing and intelligent decision making.

This preferred locations for this role are either Harwell, UK, or Madrid, Spain, although other Deimos sites may also be considered.

Duties:

The main responsibilities are:

Research, design, implement, and deploy machine learning models and algorithms that address specific challenges and opportunities related to on-board processing in Space. Collaborate with team-members and clients across Europe to understand project requirements, objectives, and constraints. Process and analyse datasets to extract meaningful insights and features for model development. Design, implement and maintain industry-standard MLOps infrastructure for new and existing ML products Optimize and standardize ML training and validation processes, data warehousing and pipelines.

Education:

Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field.

Professional Experience:

The position will be tailored to the level of experience; practical industry experience deploying and maintaining ML systems in production would be viewed very positively.

Technical Requirements:

Required:

Strong foundation in machine learning algorithms, statistics, and data structures within relevant technical projects. Proficiency in programming languages, frameworks, and tools, such as Python, TensorFlow, PyTorch. Experience with data preprocessing, feature engineering, and model evaluation techniques.

Highly Desirable:

Experience working on aerospace-related projects Experience deploying MLOps solutions and working within CI/CD frameworks Experience with Linux systems and cloud infrastructure (AWS, Azure, etc.) Experience developing embedded ML applications (C++, CUDA, TensorRT)

Language Skills:

Good level of English, spoken and written

Personal Skills:

Capability to integrate in and work within a trans-European team Solid organisational, analytical and reporting skills Autonomy and willingness to take initiative Excellent communication skills Energetic, positive team player mentality

Ref.:

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.