About Pleo
Messy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. At Pleo, we're changing that. We build spend solutions that make managing money seamless, empowering, and surprisingly effective for finance teams and employees alike - with a vision to help all businesses ‘go beyond’.
The word ‘Pleo’ actually means ‘more than you’d expect’, and living by that mantra has been the secret to our success over the last 10 years.
Now, we’re at a pivotal moment in our journey; every move we make has a direct impact on our 40,000+ customers, our business, and our collective success. We need people who take pride in uncovering customer needs, who turn complex problems into simple solutions, challenge the way things are done (respectfully), and always aim high. With great ambitions driving us forward, we can’t say we’ve got this whole thing figured out. And frankly, that’s half the fun! What we can say is that we’re a driven, progressive, and, importantly, a kind bunch of 850+ people from over 100 nationalities, all committed to delivering the future of business spending, together.
About the role
This is an exciting opportunity to help shape how Pleo builds AI-powered product features, working alongside software engineers, data engineers and data scientists to take ideas from prototype to production. Pleo has over 40,000 customers and a decade of unique spend data — an incredible foundation to build on. Your mission will be to harness this data to create real product value.
Who you’ll be working with and reporting to
You'll be one of the first hires in our Data & AI Products team but, rest assured, you will not be working solo! You will work very closely with other Engineers and Data Scientists, each bringing distinctive skills while you bring applied AI engineering and data context. Together, the team covers the full chain from data to shipped product.
Your core focus is on building and shipping AI-powered features, with a strong collaborative element across Product Engineering, AI Platform, and Data & ML Platform teams.
What you’ll be doing
As a Staff Applied AI Engineer you will:
- Build and ship multiple AI-powered product features, setting the bar for delivery at Pleo, across agentic workflows, spend intelligence, automated actions, and more.
- Bring deep applied AI expertise and strong judgment on trade-offs (quality vs latency/cost, build vs buy, agent patterns vs simpler approaches) and help teams avoid hype-driven decisions.
- Work directly with Product, Design, Engineering, and business stakeholders to advise and prioritise on what's actually worth building. You aren't just implementing specs, you're discovering and defining the product.
- Own the evaluation, monitoring, and operationalisation of AI features: setting up evals, tracking drift and performance, and managing prompt changes safely in production. You will help establish how Pleo does this at scale and own the development in production.
- Act as a design partner to the GenAI Core platform team: challenge decisions with evidence from real feature delivery, bring clear requirements, and validate platform choices in production.
- Establish and enforce practical standards for AI feature delivery in product squads (evaluation strategy, monitoring expectations, safe prompt/versioning practices, privacy & safety guardrails) using GenAI Platform tooling (not building the platform itself).
- Upskill the team through mentorship, reviews, pairing, and lightweight playbooks that make other engineers faster.
What you bring
You will thrive in this role if you have:
- Proven experience shipping multiple GenAI features into production, at scale in a customer-facing product. You've moved past prototype phases and ideally have experience shipping multi-step, tool-using agents in a user-facing product.
- The ability to translate complex business challenges and product visions into scalable AI solutions. In other words, you can autonomously scope, design and build.
- Deep applied AI judgment: you can reason about evaluation, retrieval quality, tool use, failure modes, and what is/isn’t worth building.
- Experience applying evaluation and observability to LLM systems (tests, golden sets, online metrics, monitoring) and using those signals to iterate.
- Strong understanding of privacy and security concerns when building LLM applications: prompt injection, handling PII and data leakage risk.
- Solid experience with the modern AI stack including Vector DBs, orchestration and durable execution frameworks, and LLM APIs.
- Experience building APIs, services and data retrieval pipelines (RAG, vector search etc) to feed data into LLMs.
- Deep proficiency with Python for both data and ML engineering, SQL and major cloud providers.
- An extensive background in traditional ML engineering and a deep understanding of how to architect and build data systems for reliable and scalable production-use.
- Ability to influence cross-functionally with Product/Design/Engineering and bring teams along on decisions.
To share extra context, our current tech stack includes GCP, BigQuery, Airflow, Python, SQL on the Data side and AWS, Kotlin, Javascript, Typescript on the Product side while our infrastructure is containerised with Kubernetes. Experience across those technologies or languages would be considered a bonus.
Why is this role a good fit for you
This role is a good fit for you if:
- You have strong product instincts and build with the user in mind. You understand that a model is only as good as the problem it solves.
- You have moved past prototyping and have a deep understanding of the realities of LLMOps, data retrieval, prompt and context engineering, as well as model evaluation in production.
- You don't just call APIs, you understand the data feeding the AI system and can reason about data quality, architecture, and retrieval without needing a dedicated data engineer beside you at all times.
This role is not a good fit for you if:
- You want to focus on research and algorithm development. We need someone who cares about shipping solutions to production right now, turning AI concepts into functional features that solve real financial problems today.
- You need a perfectly groomed backlog, structured tooling and pre-defined specs. We expect our Staff Engineers to be able to navigate ambiguity, to autonomously scope solutions and build any new tooling that might be required.
- You can't explain complex AI trade-offs to a CEO or a designer without losing them in the weeds. You work well with stakeholders who are very technically or commercially focused.
How you’ll develop in this role
In your first 6 months at Pleo, you’ll:
- Familiarise yourself with our codebase, tooling and roadmap.
- Partner with our Principal Engineer to define and own our approach to AI feature development.
- Contribute to shaping the roadmap for tooling and features developed by our GenAI Core team.
- Collaborate with Product and Data teams to ship a first feature to production
We’re committed to helping you develop your career, whether that means taking on bigger projects, stepping into leadership, or acquiring new skills!
The location
Please note: We can hire on a remote, hybrid or in-person set-up in any of the locations listed on the advert but you will need to be physically based in the country of your choice with a valid right to work.
We are unable to offer visa sponsorship for this role.
Show me the benefits!
- Your own Pleo card (no more out-of-pocket spending!)
- Lunch is on us for your work days - enjoy catered meals or receive a lunch allowance based on your local office
- Comprehensive private healthcare - depending on your location, coverage options include Vitality, Alan or Médis
- We offer 25 days of holiday + your public holidays
- For our Team, we offer both hybrid and fully remote working options
- We use MyndUp to give our employees access to free mental health and well-being support with great success so far
- Paid parental leave - we want to make sure that we're supportive of families and help you feel that you don't have to compromise your family due to work
The interview process
We want to ensure you are set-up for success and understand what will be expected of you. If your application is successful, our interview process is as follows:
- Intro call: A 30-minute chat with our Talent Partner to discuss the role and your background.
- Technical screening: A 15 to 30 minute call with our Talent Partner to check your knowledge of key technical topics
- System design interview: A 75-minute practical session with our engineers focusing on scoping and designing an AI feature
- Live coding interview: A 75-minute practical session with our engineers focusing on implementing your solution.
- Hiring Manager interview: A 60-minute conversation to deep dive into your knowledge and experience.
- Final interview: A leadership interview focusing on your behavioural, communication and collaboration skills.
Transparency is important to us so we also wanted to share some insights about what we’re looking for in applications to ensure you can set yourself up for success!
- CV writing and content: we receive a lot of CVs, and many of them are AI-generated. We love seeing people leverage AI—it’s a big focus for us internally too—but without human intervention, these CVs can sometimes become generic and fail to show a candidate in the best light. What we're really looking for is the specific details of real impact that only you know from your previous experience. A top tip from us is to use the “Achieved X, as measured by Y, by doing Z” formula (credit: Laszlo Bock, ~2014) to give a really clear picture of what you’ve worked on. A final note: including links to your previous companies' websites is a huge help and allows us to truly understand your background!
- Application care: every single application we receive is reviewed by a human (yes, hundreds of them) because we believe that candidates' efforts should be matched by an equal level of human care. This means that we expect a similar level of attention put into your application. Read and answer the application questions carefully, they make a huge difference in our decision-making process.
- Profile to role fit: this is neither a research-driven nor a traditional data or software engineering role. We're looking for someone with proven experience shipping customer-facing AI products who can also confidently operate at Staff level in a SaaS organisation. This means we'll be looking for evidence that you can own fairly complex initiatives that have organisation-wide impacts beyond just building and shipping.
About your application
- English first. Since it's our company language, please submit your application in English. You’ll be using it a lot if you join us.
- A fair look for everyone. Our talent team reads every single application to ensure the process is fair. To keep things running smoothly, we only accept applications through our system—our support team can’t pass on calls or emails.
- Diversity drives us. We can only reach our goals if our team reflects the world around us. That starts with you hitting apply, even if you don't tick every single box. We encourage people from all backgrounds and experiences to join us.
- Interview at your best. We want you to feel comfortable throughout the process. If you have any accessibility requirements or need a specific format, email belonging@pleo.io. We’ll design a process that works for you.
- Your data is safe. When you apply, we process your personal data as a data processor. For more information on how Pleo processes personal data, read our Privacy Policy here.
- Applying for multiple roles? Nothing is stopping you, and we assess every role independently. However, we do look for alignment, so make sure you can explain why your interest and experience are right for each specific role.
- Reapplying. If you’re applying for the same role again, please wait six months from your last decision before hitting submit.
