Staff Engineer - Platform & Tooling

Who is Atominvest Atominvest is the operating system for modern private markets asset managers. Our modular, flexible SaaS technology underpins key investor- and portfolio-management workflows. Our customers are sophisticated private equity, growth equity, and private credit firms managing billions in assets globally. We aim to deliver ongoing high-quality outcomes for them through a long-term partnership mindset. At Atominvest, you’ll be working on something very exciting: the future of work, investments and sustainability/ ethical investing for the world’s best investors and delivering a positive impact through this. Based on the growth we are experiencing, we think we’re on to something big. It won’t be easy. Joining a scale up business is an amazing and rewarding challenge, pushing well beyond the boundaries of natural growth cycles. You’ll move quicker than feels comfortable and be brilliant at continually re-prioritizing what must get done to keep Atominvest growing year over year at the pace of the best SaaS companies on the planet. We’ve gone from zero to ten, now we’re looking to go from ten to one hundred!

About the role

Hi, I'm Thomas, VP Engineering at Atominvest. This is the hire I'm most excited about, so I wanted to tell you about it myself. You'll own the platform our clients run on and build the AI development harness our engineers work in day to day. That work runs from infrastructure to application code, so we're looking for an experienced engineer with breadth across both, and depth in at least one. Together, it's what lets a small, lean team, working alongside AI agents, ship like a much bigger one. By platform, we mean everything our product and our engineers stand on: infrastructure, security, test automation, developer experience, and the agent pipelines that run through all of them. It's a broad, hands-on role. You'll write infrastructure as code for a new service one day and address network vulnerabilities the next, then enhance our AI development harness and release a new feature, all within the same month. You might be a platform engineer who loves shipping product, or a full-stack engineer who has run serious production infrastructure. Either works. As deployments grow more complex, the team needs your depth in infrastructure and distributed systems. You'll strengthen the architecture and make the design decisions the platform will rest on for years. Those decisions should leave room to grow, because future products may bring much larger volumes of data than we handle today. It has to stay dependable while we do it. Our software needs to be available around the clock, every day of the year, and data integrity is critical. Our clients use Atominvest to report to their own investors, so a wrong number matters as much as an outage. Security, reliability and audit-readiness matter too. If you enjoy getting those right without slowing everything else down, you'll find plenty of fun things to work on. The machine that builds the product We're becoming AI-native. Agents do more and more of the mechanical work of writing code, and engineers build the machine that builds the product: the architecture, the specs, the limits agents work within, and the evals that tell us whether their output is right. I think that moves an engineer's leverage upstream. When code is cheap, the scarce things are judgement and understanding. Which problem is worth solving? What does good look like for the person using it? Is the output actually right? Did what we shipped help? The best engineers I've worked with always solved the end problem, with code as the medium. Now that's most of the job. That's why product and user understanding sit at the centre of how we work. An engineer who knows our clients' workflows can point agents at the right problem and spot when the result is subtly wrong. Without that, agents just build the wrong thing faster. Your part is the AI development harness that makes this safe. That means sandboxed environments with synthetic data, scoped and audited access to logs and alerts, and evals and validation gates in CI. It also means firm lines, enforced by tooling, that keep production deploys and secrets in human hands, and client data out of reach of development agents. You'll also build the agent orchestration pipelines that run in three places: in how we build software, in AI workflows inside the product our clients use, and in the internal tools our implementation team uses to onboard and configure clients.

What you'll do

Build the machine that builds the product Most of your time goes here. Build our AI development harness. Self-service infrastructure, a reproducible development environment, and the sandboxes, guardrails and evals that let agents take on more of the work safely, with results we can measure. Automate quality. Build the QA automation pipeline: automated tests, validation gates and release checks that give us confidence in every change, including the ones agents write. Orchestrate agents across the business. Build pipelines that run agents reliably and safely in our product workflows and our internal implementation tools, not just in engineering. Own the platform Own our infrastructure. We're migrating to ECS. You'll strengthen our ECS architecture, see the move through in safe, reversible stages, bring every resource under infrastructure as code, and retire the old deployment model. Build security into the platform. Tenant-scoped credentials, per-tenant secrets, least-privilege access, and the audit trail that SOC 2 and our clients' security teams expect. Make production observable. Set up the monitoring, alerting and tracing we rely on, and build playbooks for on-call and incident response which is shared amongst the team. Work across the stack Make the application changes the harness needs, such as synthetic data, test hooks and the specs agents work from. Ship product. Pick up application work in TypeScript and React alongside the team. Over