Job Specifications
AI Engineer – Paris – Hybrid
Our client is seeking an AI Engineer to join a newly established tech startup with multi-million funding, dedicated to shaping the global AI ecosystem. The organisation prioritises outcomes that genuinely matter to people and communities, with transparency, equity, and public benefit at the core of everything it builds.
This is a rare opportunity to become the first technical hire and technical lead, taking ownership of the organisation’s technical stack from the ground up. The role is suited to someone who wants to build foundational AI infrastructure that others depend on, rather than short-lived prototypes. You will design, build, and deploy applied AI tools and systems across focus areas such as health and cultural preservation, working end-to-end from data pipelines and experimentation through to production deployment and monitoring.
Our client is looking for an individual with significant experience as either an AI Engineer or Machine Learning Engineer with clear evidence of delivering real-world systems.
To be suitable for this role you will require the below:
Key Requirements
Bachelor’s degree in Computer Science, Machine Learning, or a related field
Excellent experience as a Software, ML, or AI Engineer, with evidence of shipping production systems
Strong Python skills and experience with modern ML frameworks (PyTorch, JAX, TensorFlow)
Experience working with modern ML models, including foundation or multimodal models, and LLM fine-tuning methods
Solid understanding of data pipelines, evaluation, deployment, and production environments
Experience contributing to or maintaining open-source software
Strong problem-solving skills and ability to communicate technical trade-offs clearly
Collaborative mindset with experience working across technical and non-technical teams
Professional fluency in English (French a plus)
Clear commitment to building AI in the public interest
Nice to haves:
Experience in health AI, cultural or linguistic preservation, AI auditing, trust & safety, or open infrastructure
Experience working with low-resource or messy datasets.
Familiarity with responsible AI, interpretability, or robustness methods.
Experience working with governments, NGOs, or research institutions.
Contributions to technical standards, benchmarks, or public datasets.
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