The future of AI computing is light, not electrons.
As Lab Software Automation Engineer, you will bring strong software engineering expertise directly into our lab environment. You will bridge the gap between R&D bringup and operations, equipping our teams with the software tools, routines, and automation needed to efficiently perform complex measurements and hardware testing. By embedding a "data-first" and software-driven mindset into the lab, you will ensure high-quality deliverables and enable the seamless transition of our photonic NPUs from R&D to full production.
At Q.ANT, we are building photonic processing systems that compute with light—delivering a scalable, energy-efficient alternative to transistor-based architectures for next-generation AI and high-performance computing.
Your Impact & Responsibilities
Drive Lab Automation: Define measurement strategies, develop robust measurement routines, and automate software tasks across bringup and operations to streamline production workflows.-
Bridge Bringup & Operations: Serve as the central technical connection between R&D systems engineering, bringup teams, and operations, ensuring high dependability and on-time NPU deliveries (OTIF).
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Enable Data Analytics: Lead data-gathering efforts and manage long-term database storage to provide actionable insights for R&D and key stakeholders.
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Optimize Software Protocols: Establish clear software protocols, best practices, and documentation for lab tasks, while consulting on and developing internal lab software tools.
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Solve Complex Lab Challenges: Work autonomously to investigate and resolve complex measurement or hardware bringup issues using structured software engineering principles.
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Scale Internal Capabilities: Over time, expand automation systems to connect with backend operational databases and explore cross-reusability of software tools within R&D.
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Education: B.Sc. or M.Sc. in Computer Science, Electrical Engineering, Physics
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Software Core: Solid foundation in software engineering principles and hands-on proficiency in Python.
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Lab & Automation Focus: Strong interest or experience in lab automation, IT automation, or signal processing.
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Data & Tooling: Familiarity with modern software workflows (e.g., GitHub Flow) and interest/experience with databases and data visualization tools.
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Mindset: Highly analytical, solution-oriented, with strong ownership and self-leadership skills to drive processes forward independently.
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Experience Level: Open to both promising entry-level candidates with strong relevant foundations and experienced specialists/experts. Prior lab experience and exposure to lab automation frameworks (e.g., LabVIEW or similar tools) is a plus!
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Work on the leading edge: Photonic AI acceleration technologies that will define the next decade of computing - already validated in production HPC environments.
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Make impact at scale: Help solve one of computing's biggest challenges—making growing demand in compute and sustainability go hand in hand.
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Own your work from day one: Broad autonomy and decision authority with direct impact on product success, production efficiency, and overall NPU quality.
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Fast-track your growth: Work on challenges with few industry precedents—every problem you solve becomes new institutional knowledge and potentially industry-defining IP.
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World-class team: Collaborate alongside a passionate, international, cross-functional team of experts in photonics, processor design, and AI systems.
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Direct access to leadership: Work closely with the company's leaders, including VP SW and founders, within a transparent and fast-moving structure.
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Collaborative culture: Innovative work environment that values technical excellence, open communication, and pragmatic problem-solving.
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Be part of history: Join at the inflection point where photonic computing transitions from research to mainstream—your contributions will shape this transformation.
Q.ANT is a deep-tech scale-up developing photonic processing solutions that compute natively with light and deliver a scalable alternative to transistor-based systems. The analog co-processors are optimised for complex computations and enable energy-efficient performance for next-generation AI and HPC applications. In collaboration with the Institute for Microelectronics Stuttgart IMS CHIPS, Q.ANT operates its own pilot line for photonic chips, based on the material system Thin-Film Lithium Niobate TFLN. Q.ANT was founded by Michael Förtsch in 2018 and is headquartered in Stuttgart, Germany.
