Graduate Scientist

East Knighton
6 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist/Statistician

Graduate AI & Machine Learning Engineer | London, UK

Graduate Actuarial Analyst (Machine Learning)

Computational Biology & Machine Learning Scientist

Principal Data Scientist

Principal Data Scientist (Remote)

Graduate Scientist / Engineer

With a reputation for providing innovative underwater systems for the Royal Navy (RN) and export customers, Atlas Elektronik UK operates from their Headquarters on the Jurassic Coast in Dorset. Through science, engineering and R&D they convert data into information, knowledge and capabilities that challenge the status quo, and offer winning advantage at the frontline.

Atlas Elektronik UK have designed their Engineering Graduate Programme to support those with a passion for Engineering to grow into the next generation of specialists to develop cutting-edge maritime technology for worldwide customers and the UK Royal Navy.

Atlas Elektronik UK are dedicated to developing the next generation of talent in engineering and technology.

As a Graduate Scientist / Engineer you will use and develop technical knowledge to offer solutions to problems; thinking innovatively and creatively within a robust engineering framework. You will be offered a permanent role from day one and step onto a 2 year Graduate Scheme to develop your skills and hands on application of theory. Suitably experienced and qualified mentors are provided to each of the graduates to further support their professional progress. The scheme is closely monitored with regular reviews and a graduate training programme including a project set by the Senior Management Team. The graduates will continue to be mentored and get support from the
company as well as the committee that oversees the scheme until they achieve their desired incorporated or chartered status.

Scope:

As a Graduate Scientist / Engineer you will perform research and development contributing to new technology and knowledge in the Sonar and Underwater Platform Signatures domains.

Across these domains AEUK is involved in a broad range of technical areas includes:

Development of Sonar sensors and other hardware and software systems
Analogue and digital electronics
Signal processing, image analysis, data science and machine learning
Development of detection and classification algorithms
Analysis of acoustic and non-acoustic data
Simulation and modelling of the physics of sonar systems and generation and propagation of acoustic and non-acoustic signatures in the underwater environment
Test and measurement of acoustic and non-acoustic systems, including those on in-service submarine sonars

You will use technical knowledge to offer solutions to problems; thinking innovatively and creatively, you will provide technical input into bids and projects writing reports. You will provide advice and guidance on technical matters as well as providing support to the team in technical liaison with customers to support the agreement of requirements.

What we are looking for in you:

Interest or past experience in engineering or the marine industry
Demonstrate excellent written and verbal communication skills
Excellent interpersonal skills and be able to interact with people at all levels both within the company and externally
Computer literacy in order to operate information systems. Proficient in Microsoft Word, Excel, PowerPoint
Able to build rapport and develop working relationships
Strong team focus
Strong time management skills
Ability to work independently at times under own initiative
Ability to use own initiative when working under pressure
Prioritise and manage personal workload to ensure deadlines are achieved.
Willingness to travel in the UK and overseas

More information

Graduate intake planned for October 2025.

The successful candidate must be able to achieve full SC (Security Clearance)

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.