Backend Software Engineer

CATCHES
Liverpool
3 weeks ago
Applications closed

Related Jobs

View all jobs

Senior Backend Engineer - Data Engineer

Software Engineer

Staff Engineer (ML-Native / Software Engineering)

Senior Software Engineer - AI & Machine Learning

Senior Software Engineer - AI & Machine Learning

Senior Software Engineer - AI & Machine Learning

Location:Fully remote with the opportunity of working in a co-working space local to you


About:

CATCHES are a SaaS start-up backed by some of the most influential names in luxury fashion globally. We've partnered with the global leaders in cloud computing and AI to integrate advanced 3D rendering, Artificial Intelligence (AI) and Visual Effects (VFX) techniques to create unparalleled shopping experiences for luxury fashion and exclusive events.


Role:

We are seeking a highly skilled Backend Software Engineer to join our team. The ideal candidate will have experience building APIs and backend services, ideally in C#.NET.

In this role, you’ll build robust, scalable, and secure backend systems powering our SaaS platform. You will collaborate closely with the frontend team, data engineers, and other stakeholders to deliver high-quality software solutions that meet our product's needs.

You’ll have input into technical direction and contribute to shaping backend architecture as we scale.


Responsibilities:

  • Design, develop, and maintain APIs and services primarily usingC#.NET.
  • Build scalable, fault-tolerant systems for a cloud-native environment (primarilyGCP).
  • Implement event-driven workflows usingRabbitMQ.
  • Collaborate with product, design, data, and frontend teams to ship end-to-end features.
  • Own your code in production, participate in code reviews, and improve system observability.
  • Champion clean code, security best practices, and scalable architecture.


Requirements:

  • 4+ years experience building backend systems, ideally in C#.NET.
  • Solid grasp ofPostgreSQLor equivalent relational databases.
  • Cloud deployment experience (GCP preferred, but AWS/Azure welcome).
  • Comfort withevent-driven architecturesandmessage queues.
  • Experience shipping production-grade systems with performance, security, and observability in mind.
  • Ability to work independently in a fast-moving, startup environment.
  • Strong communication skills and a collaborative mindset.
  • Experience delivering pragmatic solutions and implementing iterative design approaches.
  • Strong understanding of engineering fundamentals, including design patterns, SOLID principles, and clean code.


Nice to Have:

  • NoSQL Database experience.
  • Experience withKubernetesor other orchestration systems.
  • Exposure tobare metaldeployments or hybrid cloud environments.
  • DevOps practices: Infrastructure as Code, monitoring, and alerting.
  • Some experience with frontend development or WebGL/3D rendering pipelines.


What Working with Catches Looks Like:

  • Workfully remotewith optional coworking access.
  • Be part of asmall, experienced teamthat values shipping, experimentation, and autonomy.
  • Contribute early to product and architecture decisions.
  • Use cutting-edge tech to shape the future of immersive eCommerce.
  • Enjoy startup pace without burnout: async-first, high ownership, minimal meetings.


Tech Stack:

  • Languages: C#.NET (primary), Go, Python.
  • Databases: Postgres, Redis.
  • Messaging: RabbitMQ.
  • Infra: Docker, Kubernetes, GCP (primary), AWS, Azure & bare-metal.
  • CI/CD: GitHub Actions.

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.

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.

Machine Learning Jobs in the Public Sector: Opportunities Across GDS, NHS, MOD, and More

Machine learning (ML) has rapidly moved from academic research labs to the heart of industrial and governmental operations. Its ability to uncover patterns, predict outcomes, and automate complex tasks has revolutionised industries ranging from finance to retail. Now, the public sector—encompassing government departments, healthcare systems, and defence agencies—has become an increasingly fertile ground for machine learning jobs. Why? Because government bodies oversee vast datasets, manage critical services for millions of citizens, and must operate efficiently under tight resource constraints. From using ML algorithms to improve patient outcomes in the NHS, to enhancing cybersecurity within the Ministry of Defence (MOD), there’s a growing demand for skilled ML professionals in UK public sector roles. If you’re passionate about harnessing data-driven insights to solve large-scale problems and contribute to societal well-being, machine learning jobs in the public sector offer an unparalleled blend of challenge and impact. In this article, we’ll explore the key reasons behind the public sector’s investment in ML, highlight the leading organisations, outline common job roles, and provide practical guidance on securing a machine learning position that helps shape the future of government services.

Contract vs Permanent Machine Learning Jobs: Which Pays Better in 2025?

Machine learning (ML) has swiftly become one of the most transformative forces in the UK technology landscape. From conversational AI and autonomous vehicles to fraud detection and personalised recommendations, ML algorithms are reshaping how organisations operate and how consumers experience products and services. In response, job opportunities in machine learning—including roles in data science, MLOps, natural language processing (NLP), computer vision, and more—have risen dramatically. Yet, as the demand for ML expertise booms, professionals face a pivotal choice about how they want to work. Some choose day‑rate contracting, leveraging short-term projects for potentially higher immediate pay. Others embrace fixed-term contract (FTC) roles for mid-range stability, or permanent positions for comprehensive benefits and a well-defined career path. In this article, we will explore these different employment models, highlighting the pros and cons of each, offering sample take‑home pay scenarios, and providing insights into which path might pay better in 2025. Whether you’re a new graduate with a machine learning degree or an experienced practitioner pivoting into an ML-heavy role, understanding these options is key to making informed career decisions.