AI Engineer
Atira · Munich
What this role requires
2 requirements, read out of the advert rather than guessed from the job title:
Also mentioned, not required: AI, agents, evals, runtimes, sandboxing. Worth having, but their absence is not what gets a CV filtered out.
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Job description
About Atira
Atira builds the commercial brain for industrials. Our agents 10x the sales engineers of today by offering the complete toolbox of tasks needed in complex environments. They handle everything from extracting and classifying requirements from incoming RFQs, orchestrating communication and information retrieval across multiple stakeholders and systems, while generating outcomes like documentation, production system inputs, or configuration recommendations. CRM. Atira. ERP.
We are one of the fastest growing teams in Munich and are looking for you to join us!
Why engineers are the foundation of everything Atira ships
As an AI engineer at Atira, you will work on frontier AI technology, such as agent harnesses, runtimes, evals and self-verification in complex engineering domains. You build the frameworks and scaffolding that enable agents to reliably understand multi-stakeholder, complex industrial processes, ranging from inbound processing, product configuration, pro-active information retrieval, complex technical documentation writing and seamless systems write-backs.
Misreading or wrongly interpreting a spec can cost a manufacturer six figures, so the challenges are real and the margin for error is thin.
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Where you trained matters less than what you've built and how honestly you can describe what broke. Atira is at a stage where you must own problems end to end, because what you ship today is what our customers use tomorrow.
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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