AI Engineer London
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Job Summary
About Sequence
If you join Sequence youll help build the platform that lets companies get paid - were already processing $1bn in annual invoice volume and growing fast.
Were building the AI-powered revenue platform for modern finance teams replacing fragile spreadsheets and legacy systems with software their teams love using.
Cognition MoonPay and 100 other high-growth companies trust us to handle their entire revenue cycle across quoting billing and revenue recognition.
Founded by repeat entrepreneurs we hit 10x ARR growth last year and just closed a $20M Series A led by 645 Ventures alongside a16z and exceptional founders and CFOs from companies like Decagon Klaviyo and Wise.
Whats it like to work at Sequence
Small team big opportunity real ownership. Youll do work that matters have direct access to customers and grow alongside the company.
If you want to do the best work of your career at a company thats scaling fast wed love to meet you.
What youll be doing
We care deeply about the quality of the product were building. Were looking for engineers who are ambitious and take ownership end-to-end: from identifying what to build through shipping to making sure it works for real customers.
Heres what you might work on:
Our AI infrastructure. We process over a billion dollars a year through Sequence so correctness is table-stakes. Youll build and own the platform that allows us to build reliable software on top of non-deterministic models including agentic workflows prompt iteration tools and evals.
AI-powered approval workflows. Enterprise deals need approval chains but rigid workflows break when every company has different rules. Youll build flexible routing that customers configure in natural language - require VP approval for deals over $100k with annual terms - and turn that into deterministic auditable business logic.
An intelligent collections agent. Chasing late payments is tedious manual work. Were building an agent you instruct in natural language (send a reminder at 7 days overdue; escalate at 14 days) which then runs the entire workflow and collects the relevant context autonomously.
Were a lean team growing to 20 engineers. Youll have real influence on what we build and how we build it. Early enough to shape fundamental architecture. Late enough that customers depend on what you ship.
Were looking for product engineers who live in this space. You have informed views on which tool to reach for and when. Youve built side projects to see whats possible. You take AI security seriously - youve scrutinized the threat vectors and you treat models as untrusted by default.
See what weve shipped in our public changelog.
You should apply if
Youre a builder who wants to solve problems end-to-end:
Youve shipped LLM-based systems to real customers and have first-hand experience with how they fail in production
Youve designed agentic systems and have informed views on the building blocks (like embeddings memory tool use long-running state recovery from partial failures) and when to reach for each
Youve built evals infrastructure (like datasets LLM as judge prompt regression tests monitoring) and have a clear view of what good looks like
You respect the weight of building business-critical systems on top of non-deterministic models and are comfortable writing resilient software
You have informed opinions about the tooling for building evaluating and securely running multi-provider AI systems in production
Youve shipped production backend systems and care about the difference between it works and its reliable
You care about customers - you want to understand why youre building something not just what
Youre comfortable with ambiguity. You thrive in early ideation stages share work-in-progress to gather feedback and adapt easily based on input
You communicate clearly. Thoughtful communication is a superpower that sharpens how we collaborate and build
This might not be right if
Were a small team moving fast on hard problems. That might not be a fit if you:
Prefer a slower pace. Customers are depending on what we ship
Enjoy larger organisation structures and staying only within your area of expertise. Taking ownership here means doing whatever the problem needs
Want a traditional engineering team set up with a predictable roadmap and clearly scoped out tickets provided for you
Arent comfortable with production responsibility. Were revenue-critical infrastructure - on-call matters here
Tech stack
We hire for ability not a specific tech stack. Most of the team learned Kotlin here:
Backend: Kotlin (modular monolith using Http4k Spring Boot Exposed Result4k)
Storage: Postgres BigQuery
AI Platform: Vertex AI Langsmith more to come here
Async messaging: Google Cloud Pub/Sub
Infrastructure: Google Cloud Terraform
Frontend: TypeScript React
Monitoring: Google Cloud Monitoring Sentry
Tools: GitHub Slack Notion Linear
Read our engineering principles
How we work
Were based in London and New York and love spending as much time with our colleagues as possible. We spend three days together in the office with team lunch on Wednesdays.
Youll join a cross-functional product team working directly with design and product. We get together twice a year for company offsites.
What we offer
Salary: 115000 - 130000
Equity: Meaningful share options
25 days holiday bank holidays
Modern flexible pension via Penfold with salary sacrifice available
Visa sponsorship available
Interview process
We move fast - typically 2 weeks start to finish. Weve written a detailed blog post about the process with tips for how to prepare.
Public resources on Sequence:
Podcasts:
Riding Unicorns: S6E5 (written recap): M&A founding story enterprise product views.
Transcript of the Wharton FinTech episode if you want to skim rather than listen.
Written:
USXP: From Oxford to a16z: building a US-first mindset at Sequence: go-to-market and US expansion.
Sequence blog: Introducing Sequence 2.0: her launch post for the AI-native rebuild.
Required Experience:
IC
About Company
Reduce billing errors, slipped revenue and recognition errors from custom deal terms, complex pricing, manual quoting and poor integration across sales to finance.
