Principal Application Software Engineer - grads welcome

Cambridge
3 months ago
Applications closed

Related Jobs

View all jobs

Staff Machine Learning Engineer, Anomaly Detection United Kingdom, London

Senior Software Engineer (GO/PHP)

Senior Machine Learning Engineer

Principal Consultant, Advanced Analytics - Data Science (UK)

Principal Data Scientist

Principal Data Scientist

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

Education:
Degree educated with a 2.1 or higher in a relevant field (Computer Science, Physics, Natural Sciences, Engineering, etc.).
Mathematically inclined with strong problem-solving abilities.
Technical Expertise:
Hands-on experience with the following:
Node.js, Python, Java
PostgreSQL, Elasticsearch, Redis
General engineering mindset and problem-solving skills.
Professional Experience:
Several years of experience in a commercial setting managing complex technical projects.
Proven ability to lead a small team to success.
Relocation:
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.
Relocation support may be required.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

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.