Our mission and why it matters

We are on a mission to make humans the strongest security layer.

Human risk remains one of the biggest vulnerabilities and traditional awareness training is not enough. We take a different approach by combining AI-driven personalization, real threat detection, and behavioral science to actively protect people and organizations.

We don't just simulate risks. We build the tools that detect and stop them.

Why this role matters

Our internal AI development team is a small, fast-moving task force and the go-to experts for ML across Hoxhunt's products. We build and maintain a shared LLM SDK that lets product teams ship AI-powered features without worrying about telemetry, cost tracking, or model management, and we own the most complex AI features ourselves, including the multimodal pipelines, multi-step agentic systems, and specialist ML models that go beyond what any single product team can handle alone.

We're growing because demand is outpacing our capacity. AI is now business-critical to everything we ship, and we need another strong engineer to help us keep up.

As a Machine Learning Engineer on the AI development team, you'll embed with product teams to tackle hard problems, ship solutions, and move on to the next challenge. One month you might be minimising unit costs on a pipeline serving millions of users; the next, you're building a threat detection model with its own training loop and data collection pipeline. The problems are technically demanding and the impact is real.

What you'll own and drive

Design, develop, and maintain AI features and ML models serving millions of end users

Build and maintain scalable APIs that integrate models directly into our products

Ensure the reliability, scalability, and observability of our AI systems

Collaborate cross-functionally with product teams and PMs to scope and execute complex projects

Take end-to-end ownership of initiatives from the initial idea all the way to execution

What success looks like

In your first 3 months you'll: Ramp up on our tech stack, begin shipping features, and actively contribute to project planning and execution.

By month 6 you'll: Drive end-to-end projects, contribute to architectural decisions, and operate as a trusted core team member.

What makes your thrive here

You have

At least 3 years of software engineering experience in production environments

Strong Python skills and solid software engineering fundamentals, including writing production-grade code

Hands-on experience with LLMs, generative AI systems, and ML feature development in production

Experience with at least one major cloud platform like GCP, AWS, or Azure

A builder mindset, meaning you love to ship fast, iterate, and move on without needing tightly defined problems

Bonus points if you also

Have experience building and deploying ML models at scale or working with agentic AI workflows

Bring a background from a SaaS product company

Possess knowledge of GCP (our preferred stack) and API design, observability, or scalability patterns

Have experience in TypeScript

Who you'll work with

You will join a fast-moving, low-bureaucracy team of four passionate, self-directed ML engineers. The team works in close collaboration with different product teams to solve complex problems, ship solutions rapidly, and move to the next challenge. You will collaborate closely with talented colleagues who thrive in a high-autonomy environment and share a strong enthusiasm for leveraging AI-native ways of working, without sacrificing quality, in daily engineering work. Join us in building great AI-powered products and having fun along the way!

What you can expect for us

Compensation: Monthly salary of €5000 - €6500 depending on your experience.

Working ways: We work in a flexible, but hybrid work setting. You are expected to visit the Helsinki office weekly.

High performance meets high humanity: We bring an incredibly driven, high-impact energy to our work, but we leave our egos at the door. You will be surrounded by wildly talented, dedicated colleagues in an environment built on extreme kindness, support, and psychological safety.