AD and NCAP Performance and Test (969)

Morson Talent
Wharley End
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst - Power BI

Data Analyst - Learning and Training Sector

Credit Risk and Data Analyst

People Data Analyst

Data Analyst (FCS94)

Contact Centre Systems and Data Analyst

AD and NCAP Performance and Test (969) Cranfield £27.94 per hour We are recruiting for an AD and NCAP Performance and Test for one of our automotive clients based in Cranfield. You will be working for a globally recognised automotive company. This is a long-term contract role. The contract is on an ongoing basis a may run for several years. Key Responsibilities of the ADAS Project Engineer: Support advanced engineering of AD and NCAP: • Support AD development • Gather data to define AD development scope • Retrofit and reprogram vehicle electronics for development test • Collect data and make initial analysis report • Facilitate alignment of specification fix between various departments within the company • Support NCAP activities • Participate in NCAP meetings for sharing of latest information • Actively make contribution to NCAP working group • Feed NCAP latest information to ADAS teams and align on latest internal strategy • Manage test activities (location, schedule, prototype vehicles, personnel and equipment) • Travel to test site in order to oversee/support testing activities. • Data collection report, data analysis report, fixed specification documentation • NCAP latest information report, NCAP development plan, NCAP test result, test result analysis. Qualifications & Experience for the ADAS Project Engineer: Be degree qualified (or equivalent) in a relevant discipline. • Have understanding of vehicle AD/ADAS systems and its development. • Have fluent written and verbal English language capability. • Have experience of software application into vehicle ECUs in the manufacturing environment – flashing, coding/configuration and calibration. • Be able to risk assess software modifications and develop appropriate test plans • Understand network communications and multiple bus principles (CAN, LIN, Ethernet). • Be able to operate CANalyzer, CAPL, CANoe, CANape, Matlab software or similar for capturing data, analysis of data and preparing/executing test procedures. • Be able to follow system circuit diagrams and identify electrical connections. • Be able to demonstrate and have practical experience in problem solving tools and techniques. • Hold a valid UK driver’s license and be able to pass driver training in order to drive vehicles for ADAS evaluation. • Have relevant experience in automotive electronic system development, completing a minimum of two vehicle developments, or be able to demonstrate equivalent experience. • Have good written and verbal communication. • Be able to travel abroad or within the UK, occasionally a few days, but on rare occasions travel may be long distance and for up to 10 weeks at short notice. • Competent with PC applications. Including Microsoft excel, powerpoint and project. • Self-motivating and self-managing. Pro-active. Energetic personality and flexible regarding working practice and working hours. • Ability to priorities and multitask between multiple projects simultaneously. LMIND

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.