Principal Application Software Engineer - Degree, Node.js

Cambridge
11 months ago
Applications closed

Related Jobs

View all jobs

Principal Machine Learning Engineer

Senior Data Scientist (Senior Software Engineer), BBC Verify

Data Engineer Architect

Data Engineer - AI Practice Team

Senior Data Scientist

Principal Data Scientist London, United Kingdom

This job description is for a Principal Application Software Engineer role based in Cambridge, UK with a hybrid working model and graduates are welcome to apply: Here's a breakdown of the key points:

About the Company

  • A pioneering machine learning and artificial intelligence software house.
  • Renowned for developing cutting-edge technologies and highly respected in the AI domain.
  • Led by experienced entrepreneurs with a history of producing award-winning tech companies.
  • The team includes some of the brightest minds in technology.
    ________________________________________
    Job Responsibilities
  • Technical Leadership: Manage and oversee complex technical projects within a commercial setting.
  • Communication: Adapt communication style to work effectively with a diverse software team.
  • Team Mentoring: Lead and mentor a small team, fostering growth for junior team members.
  • SDLC Expertise: Proficient in the full software development life cycle-design to implementation.
    ________________________________________
    Required Skills and Qualifications
  1. Education:
    o Degree educated with a 2.1 or higher in a relevant field (Computer Science, Physics, Natural Sciences, Engineering, etc.).
    o Mathematically inclined with strong problem-solving abilities.
  2. Technical Expertise:
    o Hands-on experience with the following:
     Node.js, Python, Java
     PostgreSQL, Elasticsearch, Redis
    o General engineering mindset and problem-solving skills.
  3. Professional Experience:
    o Several years of experience in a commercial setting managing complex technical projects.
    o Proven ability to lead a small team to success.
  4. Relocation:
    o Open to relocating to Cambridge, as the role is not fully remote.
    ________________________________________
    Benefits
  • Opportunity to join a globally respected software house.
  • Competitive salary (£depending on experience) and benefits.
  • Chance to work alongside top industry professionals in the AI domain.
    ________________________________________
    Application Notes
  • Applications must provide detailed evidence of qualifications, experience, and achievements-not just a list of skills.
  • The company's recruitment process involves direct discussions about your CV before sharing it with the employer.
  • Prepared to relocate to Cambridge ________________________________________
    Keywords
    Software Engineer, Principal Engineer, SDLC, Node.js, Python, Java, PostgreSQL, Elasticsearch, Redis, AI, Machine Learning, Cambridge, Adecco, Engineering, Application Development.
    If this role aligns with your qualifications and career aspirations, it seems like an excellent opportunity in a dynamic and innovative field.
    Adecco are operating as an Employment Agency. Adecco are an equal opportunities employer.

    Please be assured that your CV will be treated in the strictest confidence and we would always speak to you before discussing your CV with any potential employer

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.