Principal Software Developer

OpenText
UK
1 year ago
Applications closed

Related Jobs

View all jobs

Principal Data Engineer

Principal Configuration and Data Engineer

Principal Configuration and Data Engineer

Principal/Senior Data Scientist

Principal/Senior Data Scientist

Principal/Senior Data Scientist

OPENTEXT OpenText is a global leader in information management, where innovation, creativity, and collaboration are the key components of our corporate culture. As a member of our team, you will have the opportunity to partner with the most highly regarded companies in the world, tackle complex issues, and contribute to projects that shape the future of digital transformation. Your Impact At OpenText, everything we do is based on a simple idea: The fastest way to get results is to build on what you have. Our software solutions enable organizations to do just that. Secure and scalable, with analytics built-in, they bridge the gap between existing and emerging IT—fast-tracking digital transformations across DevOps, Hybrid IT, Security, and Predictive Analytics. In the race to innovate, OpenText customers have a clear advantage. Our portfolio spans the following areas: DevOps | IT Operations| Cloud | Security | Info Governance | Big Data, Machine Learning, & Analytics. About our product: Fortify is the industry-leading provider of Application Security solutions that empower organizations to develop secure software. Fortify offers a comprehensive portfolio of application security solutions with the flexibility of testing on-premise and on-demand to cover the entire software development lifecycle. Over 80% of security breaches exploit application vulnerabilities, and at Fortify, you will be at the forefront of one of the fastest-growing segments in the security market. You will work with bright, motivated teammates to implement solutions to some of the toughest code analysis problems in the industry. We develop complex algorithms to scan the code of over 20 different programming languages. Development and testing are done using agile methodologies and techniques. Learn more about Fortify Static Code Analyzer (SAST): Security from the Inside Out:https://www.youtube.com/watch?vDGZrTtx7rLoFortify Static Code Analyzer:https://software.microfocus.com/en-us/products/static-code-analysis-sast/overviewJoin our experts and help us expand our security team A typical day in your life in this role What the role offers: Design and implement static analysis algorithms based on recent relevant computer science research and literature. Develop new analysis features and add support for new languages and language features. Apply recent research developments from computer science literature where we can benefit from upgrading our algorithms and program representations. Write specifications for features as they are implemented. Analyse the quality of security finding results and product performance characteristics. Maintain the Fortify Static Code Analyzer code base using good software engineering practices. Collaborate with a project team of other software engineers, security researchers, and quality engineers, to develop reliable, cost-effective, and high-quality solutions. Education and Experience Required What you need to succeed: Master's degree or Ph.D. (preferred) in Computer Science or equivalent, with emphasis on programming languages, static program analysis, compilers, or software security. 12 years of solid enterprise Java backend engineering skills Excellent written and verbal communication skills; Ability to effectively communicate design proposals and specifications. Qualified candidates have prior expertise in or knowledge of one or more of the following areas: Background in knowledge of compiler internals, static code analysis algorithms (abstract interpretation, dataflow, higher order analysis, buffer analysis, shape analysis, separation logic, context-insensitive incremental analysis). Background in Compiler construction (frontends, IR, type inference, program transformations) in one or more programming languages Background in the software security domain Desirable skills: Programming skills in additional coding languages and frameworks and desire to learn new programming languages Compiler tools (LLVM, MLIR, Rust HIR/MIR, Eclipse JDT, etc.) Experience with software systems running on multiple platform types. Strong analytical and problem-solving skills. Familiarity with agile development methodologies One last thing: You are persistent and inquisitive. You have to understand why things are happening the way they are. You are determined to understand cyber attack techniques at a very detailed level. You are a self-starter who is able to work with minimal management, however, have strong collaboration and interpersonal skills to work together with several other professionals from other information security fields. You’re a creative thinker who wants to answer the question, “Why?” Your workstation is a pyramid of monitors that you can't take your eyes off of at the risk of missing something. You have a desire to learn new technologies. Your sense of humour, passion and enthusiasm shines through in everything you do.

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.