The Role:
As an AI Engineer for Cryptographic ML Systems at Openchip, you will help design and implement the foundation of our privacy-preserving, verifiable AI execution stack. This role brings together applied cryptography, secure ML inference, and low-level systems development to ensure AI models deployed through AI OS can run safely, verifiably, and in privacy-respecting environments. You will work across FHE (Fully Homomorphic Encryption), ZKML (Zero-Knowledge Machine Learning), and circuit-based proof systems like zkSNARKs to help develop and optimize the attestation and security algorithms running inside of Openchip’s AI OS. This role requires hands-on implementation in Rust and Python, deep curiosity for math and cryptographic systems, and a collaborative mindset to work across research, engineering, and hardware teams. You will report directly to the Secure AI Optimization Lead and be a key driver in making secure AI practical, performant, and production ready.
Key Responsibilities:
- Design and implement FHE-based model inference workflows using Zama’s Concrete or similar libraries; evaluate performance and integration paths.
- Prototype and validate Zero-Knowledge Proof (ZKP) pipelines for verifying model execution using tools such as ezkl, Circom, Halo2, and zkVMs.
- Contribute to the architecture and development of the Openchip’s composable framework for private/verifiable AI.
- Develop libraries in Rust and Python for compiling, executing, and verifying models in secure environments.
- Build and test APIs and servers for secure model inference and proof generation; explore design of PoC endpoints for real-world integration.
- Integrate secure ML protocols with the broader Openchip AI OS runtime stack and interface with compiler/runtime teams as needed.
- Analyze performance implications and hardware-software co-design considerations when deploying cryptographically verified inference pipelines.
- Contribute to future proving scheme design tailored to Openchip’s custom AI hardware.
Qualifications:
- MSc or PhD in Computer Science, Cryptography, Applied Mathematics, or related field, or equivalent practical experience.
- More than 5 years of experience or proficient in Rust (preferred) and Python, with experience developing secure or performance-critical systems.
- Experience with at least one cryptographic proof system (zkSNARKs, Halo2, STARKs) and/or FHE frameworks (Concrete, SEAL, TFHE).
- Solid background in applied math or algorithm design related to cryptography, constraint systems, or polynomial arithmetic.
- Familiarity with secure inference and ML model verification concepts; interest in deploying models under trustless conditions.
- Comfortable with low-level debugging and optimizing for compute/memory performance.
Soft Skills:
- Passion for security, privacy, and trust in AI systems.
- Clear communicator who can work across research and engineering boundaries.
- Independent and exploratory, but thrives in a collaborative environment.
- Strong sense of code quality, modularity, and technical ownership.
- Curiosity-driven mindset, excited to push the frontier of verifiable AI infrastructure.
What We Offer:
· Join an innovative team and experience company growth.
· We believe in investing in our employees and providing them with opportunities for growth and career development.
· Work in a hybrid environment with flexible scheduling.
· We offer a remuneration package that values your experience. A chance to work on one of the most transformative AI and silicon engineering companies in Europe.
· The position will be preferably in Barcelona (Spain), but open to Gdansk (Poland), Rome (Italy) and Ghent (Belgium).
We are looking for outstanding people willing to join our mission to change the silicon industry and help build a better world. If you feel identified with Openchip, please contact us.
At Openchip & Software Technologies S.L., we believe a diverse and inclusive team is the key to groundbreaking ideas. We foster a work environment where everyone feels valued, respected, and empowered to reach their full potential—regardless of race, gender, ethnicity, sexual orientation, or gender identity.
