Senior Design Authority

Copello
Harlow
1 month ago
Applications closed

Related Jobs

View all jobs

Data Science and Analytics Senior Business Analyst

Senior Machine Learning Product Manager (Deploy)

Senior Data Analyst - Finance and Treasury

Senior ADAS Engineer

Senior Data Consultant

Senior Data Engineer - DV Cleared

Senior Systems and Hardware Design Authority

Location-Harlow

Contract-Permanent


Summaryof Role:

This is an exciting opportunity for a Senior Systems and Hardware Engineer to work within the Assured Position Navigation and Timing Systems team (APNT), producing Digital GPS Anti-Jam Systems and Alternative Navigation Solutions primarily for the military market.

The role will provide Design Authority (DA) support across a range of APNT products, providing Systems and Hardware Engineering expertise/direction across the product lifecycle, from requirements capture, Analysis, Design through to testing, qualification, certification and in-service support.

Main Duties:

This role will initially provide support to the Lead Design Authority for our Flagship Anti-Jam products with a view to becoming the Lead Design Authority once enough training and experience has been gained.

Main activities include DA support to: Customer Programme Management Reviews (including attendance to PMR held in Georgia every 6 months, April and October), monthly customer IPT meetings, weekly internal IPT obsolesce, risk and FRACAS reviews.

Primary responsibilities include production support and obsolescence maintenance, management of project requirements, generation of technical documentation, systems analysis, hardware design, participation in technical design reviews and technical direction/custodianship of the products.

Candidate Requirements:

Essential

  • Experience across the systems engineering lifecycle from requirements through to project completion.
  • Experience with generation of technical documentation at all levels from requirements capture through to Declaration of Design & Performance (DDP).
  • An understanding of the rules of change management and configuration management in a highly regulated industry where safety and performance are paramount
  • Understanding of policies, practises and procedures, and the ability to communicate these to their team.
  • Sense of ownership, resilience and decisiveness to provide technical direction/guidance in the development of electronic solutions, and have the confidence and experience to assess and sign off on solutions

Desirable

  • Experience with the IBM Rational DOORS toolset (or similar)
  • Experience with RF (including Antennas) and Digital Hardware design and test
  • Algorithm development incorporating Modelling experience in MATLAB/Simulink
  • Experience with SysMl or UML in a Systems Engineering environment
  • Experience with configuration management system


JBRP1_UKTJ

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.