What you will do

• Build the agent runtime and harnesses. You design and improve the core loops that let Atira's agents reason in complex engineering environments across the entire sales-engineering lifecycle. This includes the SDK, execution harnesses, and the orchestration layer that ties it together
• Build out our AI moat. You are in charge of quality & evals, customising tools & skills, improving the ontology layer that unlocks compounding process knowledge and fusing deterministic guardrails with flexible harnesses to make our agents actionable at enterprise level complexity
• Unlock industrial scale. 1000s page inbounds, excel pricing lists, technical drawings, CAD files, ERP tables, configurator rules - industrials live in multi-modal, data heavy environments where reliability is crucial. You will be responsible to continuously scale our AI systems and infrastructure: Millions of concurrent LLM requests, safely isolated sandboxes and the corresponding observability stack - no silent failures, cost blowouts, or latency explosions
• Advance our internal AI tooling. FDEs, platform engineers and the GTM team depend on tooling you build to improve Atiras internal context layer, the models we use and skills we provide them with
• Work with FDEs when customer problems hit the platform. When an FDE hits a problem that traces back to the runtime, the pipeline, or the AI infrastructure, you're the person they pull in. At a team of 12, the boundary between AI, platform and customer is thin, and you'll cross it regularly

What we are looking for

Ownership, depth, and the instinct to take problems personally, go deep until you understand the system, and own the outcome including the parts that broke.

Your profile

• Agent fluency sits on top of real engineering fundamentals. Strong skills in Python, TypeScript, or a comparable language. Paired with hands-on experience with agents, evals, harnesses, runtimes, sandboxing and tools
• You've gone further than most with AI-assisted development. Whether it's Claude Code, Cursor, Codex, or your own setup, you've invested seriously in building the skills, context, and workflows to get maximum leverage from coding agents. This isn't a nice-to-have. We think engineers who have mastered this are fundamentally more productive, and we hire accordingly
• You are a strong technical decision maker that knows how to scale systems into millions of calls per day, while being able to explain the pros and cons of every solution. Yes, every solution: there is no free lunch
• You’ve demonstrated ownership and initiative: a company you started, a serious open-source contribution, a side project that demand drove to production scale, a role where you owned a technical outcome others would have delegated. We want to see evidence that you build things without being told to
• Degree in Computer Science, Information Systems, AI, or a related technical field from a strong technical university
• Full working proficiency in English. German is not required

What you'll get

• Real ownership: you'll be one of the early team members and shape both product and architecture as well as work closely with customers
• Strong peers: work with people who have built and shipped AI systems in industrial environments at scale before, and the chance to learn directly from them
• Impactful work: your code is deployed all across Europe’s and US industrial backbone, affecting how complex products and services are sold and configured worldwide
• Competitive salary & equity package and additional benefits including Wellpass, JobRad and company dinners
• Most importantly: Collaborate & thrive in a high-performing but caring culture. We will own the industrial sales function but will do so with modesty and integrity

What success looks like after a few months

• You've shipped meaningful improvements to the agent runtime, our AI pipelines and related systems that are running in production across multiple customer deployments
• You own core parts of our AI stack (evals, tooling, context-layer, infrastructure, a piece of the SDK) and the team trusts your judgment on how it should evolve
• When something agent-related breaks in production, you're one of the people who can trace it end to end, from agent behaviour back to the agent infrastructure layer, and fix it
• You've improved our internal AI tooling to significantly accelerate engineering output at high quality
• Your design decisions have held up under real load and you can explain the tradeoffs you made and what you'd do differently

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