Axiomatic_AI

Axiomatic AI is pioneering a new class of AI called Axiomatic Intelligence (AxI) that integrates formal verification with deep learning to produce verifiable, interpretable, and logically rigorous AI…

AI-driven DesignEngineering InnovationFormal VerificationPhotonic HardwarePhysics-based ModelsScientific InnovationSemiconductor HardwareVerifiable AIaxiomatic-ai.com

Axiomatic_AI

AI-driven DesignEngineering InnovationFormal VerificationPhotonic HardwarePhysics-based ModelsScientific InnovationSemiconductor HardwareVerifiable AIaxiomatic-ai.com

HQBoston, US

Team Size33

Open Jobs11

Total Funding-

Latest FundraiseUnknown

Join the Team

Applied AI Engineer

HybridGreater Barcelona Metropolitan Area, ES

Hybrid • Greater Barcelona Metropolitan Area, ES

About Us Axiomatic AI is building a new class of AI systems designed to reason with the rigor of the scientific method. By combining deep learning with formal logic and physics-based modeling, we create verifiable, interpretable AI systems that collaborate with and support human researchers in high-stakes scientific and engineering workflows.

Our mission, 30×30 , is to deliver a 30× improvement in the speed, accessibility, and cost of semiconductor and photonic hardware development by 2030.

We aim to revolutionize hardware design and simulation in these industries and are building a team of highly motivated professionals to bring these innovations from research into commercial products.

Position Overview As an Applied AI Engineer, you will be the bridge between the R&D team and the product, turning research prototypes into robust, production-ready AI capabilities. You will work hands-on across the stack to integrate LLM- and agent-based systems into real workflows, ensuring they are reliable, reproducible, and maintainable in production environments.

This role requires strong engineering skills to implement, test, deploy, and operate AI-driven features in production, working closely with researchers and software engineers to meet real-world constraints such as quality, latency, cost, privacy, and reliability. You will also bring a solid understanding of modern AI models, including LLMs and agentic architectures, to make sound technical choices and anticipate failure modes.

Your Mission

Applied AI Product Development: Build and ship applied AI features with the team, contributing from early design through production rollout and ongoing improvements.
Requirements & Delivery: Translate user feedback and research prototypes into clear technical requirements and pragmatic delivery plans.
LLM & Agent Workflows: Build LLM-powered workflows such as tool-calling agents, structured output pipelines, retrieval/tool integrations, and safe prompting strategies.

Key Requirements

1+ years of industry experience in software engineering (Python preferred), or relevant internship experience combined with strong open-source or project contributions.

Nice To Have

Familiarity with agent frameworks such as LangChain, PydanticAI, or similar tools.
Knowledge of prompt engineering and basic evaluation techniques for LLM systems.
Understanding of RAG architectures and retrieval-based systems.
Understanding of Knowledge Graph concepts and how they can support AI systems.
Experience with real-time inference patterns, including streaming responses.
Experience with model fine-tuning or training workflows.
Background in ML engineering or data engineering.
Contributions to open-source AI/ML projects.

What We Offer

Competitive compensation
Stock Options Plan: Empowering you to share in our success and growth.
Cutting-Edge Tools: Access to state-of-the-art tools and collaborative opportunities with leading experts in artificial intelligence, physics, hardware and electronic design automation.
Professional Growth: Opportunities to attend industry conferences, present research findings, and engage with the global AI research community.
Impact-Driven Culture: Join a passionate team focused on solving some of the most challenging problems at the intersection of AI and hardware.

Work model & location expectations:

Team work model: Hybrid
On-site expectations: 2/3 days per week
Primary location: Carrer d'Esteve Terradas 1, Castelldefels, Barcelona, Spain

Why join us? At Axiomatic_AI, you will be working on technology that drives innovation in AI for scientific and engineering applications in line with our 30 x 30 mission. This is your opportunity to contribute to the development of new AI architectures that can reason coherently and produce interpretable and verifiable solutions. Consequently, see those ideas commercialized into products that will shape the future of hardware and computing, while collaborating with a global team of engineers and AI specialists. We believe in pushing the boundaries of what is possible and continuously seek to redefine the intersection of AI, with focus on formal consistency. If you're ready to take your expertise in artificial intelligence and physics to the next level, we want to hear from you!

Worried about not meeting every qualification? Studies show that women and people of color are less likely to apply for jobs unless they meet every listed requirement. At Axiomatic-AI, we are dedicated to creating a diverse, inclusive, and authentic workplace. If this role excites you but your background doesn’t perfectly match every qualification, we still encourage you to apply. You could be the perfect fit for this position or another opportunity with us.

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Familiarity with cloud environments (GCP or AWS) and containerization tools such as Docker.

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Exposure to CI/CD and software delivery workflows (e.g., GitHub Actions or similar) is a plus.

Interest in production reliability, including logging/monitoring, safe rollout practices, and responsible handling of failures.

Problem-solving mindset, with the ability to learn quickly and debug issues in real systems.

Exposure to deploying and operating AI/LLM systems in production or enterprise-like environments.

Clear communication skills and a collaborative approach, including the ability to understand user needs and discuss trade-offs with the team.