Technical Lead - Software Engineer (Full Stack) Bristol

Bristol
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Databricks Tech Lead

Lead Data Engineer

Machine Learning and AI Engineering Lead

Data Engineer

Principal Software Engineer

Job Title: Technical Lead (Software Engineer) (Full stack)- Python, PySpark, HPC
Salary: £100,000 - £130,000 | Location: Bristol, UK (Hybrid)

About the Company

My client is a pioneering InsurTech specialising in cyber reinsurance, delivering advanced analytic's and underwriting solutions that transform cyber risk management. Their skilled, collaborative team thrives on data science and engineering innovation.

The Role: Technical Lead

We're seeking a hands-on Technical Lead to drive platform development, build and lead a high-performing engineering team, and integrate advanced risk modelling into their cyber reinsurance platform. This role requires 10+ years' experience in software engineering, including 5+ years in leadership, preferably in insurance or financial services.

Key Responsibilities

Platform Development: Architect and develop acyber reinsurance platform, incorporating:
Reinsurance submission ingestion, policy administration, cyber risk modelling, portfolio optimisation, and advanced reporting.
Team Leadership: Build and manage a high-performance engineering team across HPC, data engineering, and web development.
Collaboration: Work closely with data science and modelling teams to integrate analytical models.
Scaling Strategy: Expand the platform across new business lines.
Hands-On Contribution: Remain actively involved in the codebase, solving technical challenges and mentoring the team.

Qualifications & Skills

10+ years in software engineering, you must be experienced across the Full stack both Front and Back End with 5+ years in leadership (preferably in insurance).
Strong Python and PySpark skills, plus HPC, large-scale data engineering, and full-stack development.
Experience with machine learning, cloud platforms (AWS, GCP, Azure), DevOps tools (Docker, Terraform, Kubernetes), and data lakehouses (Databricks).
Proven success in building and scaling engineering teams and aligning initiatives with business goals.

Why Join?

Lead a cutting-edge team in cyber reinsurance.
Shape the future of risk management with advanced analytics.
Work in a highly collaborative, innovative environment.

How to Apply

Send your CV to to explore this opportunity

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.

Jobs

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!

Jobs Careers

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.

Jobs

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.