Senior Product Engineer (Backend)

Sequence
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Product Manager (Deploy)

Senior Product Manager - AI, ML & Data Science

Senior Data Scientist London, England

Senior Data Engineering Manager

Senior Data Scientist

Senior Engineer, Data Engineering

About Sequence

Backed by a16z and Salesforce Ventures, Sequence is reinventing the accounts receivable category, building a flexible toolkit to help B2B finance teams to scale their revenue collection infrastructure.

The team behind Sequence has decades of experience building and operating category-defining marketplace, machine learning, fintech, and enterprise software companies. We are no ordinary start-up; the maturity of our leadership and technology means we are operating at a lightning fast pace. This is a fantastic opportunity to be a part of the next wave of innovation for the CFO office, doing your best work with talented, ambitious and creative teammates.

Sequence is the ultimate billing and revenue stack for B2B companies. We help our customers design and iterate on their pricing and revenue flows, so they can stay completely focused on their mission without worrying about billing.

At the end of your career, we want you to look back at your time with Sequence and say it was the best job you ever had.

The role

We’re looking for senior product engineers with strong backend skills to help us build financial tools for modern, fast scaling technology companies.

What you'll do:

  1. Create a world class product experience. You'll help make the experience of implementing and working with the Sequence product second to none. We believe our billing engine is the most flexible and comprehensive out there; however, complex financial processes can be challenging to model and expose in an approachable and intuitive way. You'll help us figure out the best ways to go about this, building a backend for our own product, alongside a first-class API experience for our customers.
  2. Guide product direction: We believe that all engineering decisions are product decisions, and vice versa. You'll collaborate with data, product, and design to choose what we work on and why, and help build Sequence to be the best product and business it can be.
  3. Shape our backend architecture. You'll work on some of our most complex technical problems and help others do the same. You'll help choose how we build things and what we prioritize as a product and engineering team.
  4. Help our small team have outsized impact. We want to build an early engineering culture that values collaboration, learning, and teamwork. You'll play an integral role in shaping our product, culture, and ways of working to help us achieve this.

This is a great fit if you...

  1. Enjoy being hands-on with a focus on writing code and shipping things. We have a long list of things we want to build.
  2. Are happy working day-to-day in a backend-focused role using a strongly typed language. Our services are built in Kotlin; though you don't need experience in that language to join us, many of our engineers have done the same and enjoyed the challenge of learning a new set of skills.
  3. Want to work as part of a small, multi-disciplinary team and collaborate closely with others.
  4. Want to work on something new. The biggest product and company decisions still lie ahead of us.
  5. Enjoy the uncertainty and unpredictability that comes with an early stage company.
  6. Are happy to learn deeply about our customers, the problems they face, and work with them to figure out solutions.

This may not be the right role if you…

  1. Enjoy structure and only staying within your area of expertise. We’re a small team early on the journey, and things change quickly as we learn more.
  2. Want a traditional product team set up, with a predictable roadmap, clearly scoped out tickets provided for you, and so on.
  3. You prefer a slower pace. We're tackling real problems for our customers today, so we need to move quickly.
  4. Want all of the benefits that come with a larger, established tech company.

We're fully remote within +/- 5 hours of GMT, or you can work from our Hub in central London—whatever you prefer.

#J-18808-Ljbffr

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.