Research Engineer

• Train and post-train LLMs for safety and moderation tasks: SFT, RLHF, DPO, and related alignment methods
• Build and train reward models from human and synthetic preference data
• Design and run high-throughput data pipelines: collection, synthetic generation, filtering, deduplication, and quality control at very large scale
• Run distributed training on multi-GPU clusters and debug what goes wrong when it does
• Build evaluation systems and benchmarks that actually measure model behavior, and use them to drive training decisions
• Optimize models for production inference: quantization, speculative decoding, serving with vLLM/TensorRT or similar
• Move fast from experiment to production – your models ship, and you see their effect on real traffic

ML Research Engineer

• Own and maintain our internal benchmark suite, covering single/multi-turn content guardrails and agentic safety.
• Build benchmarks that distinguish specific model capabilities.
• Work with the product team to build evals covering core functionality of our flagship models.
• Build benchmarks for new features coming out of the research team.
• Adapt and extend evals to new verticals and changing product data.
• Work on research projects that study and quantify realistic agentic and LLM failure modes in the wild.

Research Engineer, Evals

• Internal bot detection red team: Challenge our bot detection mechanisms to ensure we properly detect bots. Create tools to automate this process.
• Help detect bypasses proactively by analyzing different modules as well as potential misconfigurations on our customer websites.
• Improve client-side bot detection: Add new signals to our client-side scripts to collect browser fingerprints capable of detecting advanced bots.
• Improve security and resilience of our JavaScript code by implementing obfuscation techniques to prevent reverse engineering.
• Collaborate with mobile team to collect new signals in our SDK to improve mobile bot detection.
• Research: Collaborate with other member of the threat research teams as well as member of other teams to implement new approaches to improve bot detection.
• Communication: Share your results externally using appropriate communication medium, such as meetups, conference talks, academic papers, blog posts.

Threat Research Engineer – Client Side

• Design, develop, and evaluate robust multi-sensor fusion algorithms for navigation, perception, and state estimation.
• Work on topics such as: INS/GNSS integration; vision-aided navigation; sensor fault detection; and robustness to degraded sensors.
• Contribute to research activities, from theoretical formulation to experimental validation.
• Implement and test algorithms in simulation and on embedded platforms.
• Collaborate with experts in navigation, perception, control, embedded software, and systems integration.
• Participate in system integration, performance analysis, and field or flight testing.
• Produce high-quality technical documentation and reports, and, where relevant, scientific publications.

Research Engineer jobs in France

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ML Research Engineer

Research Engineer, Evals

Threat Research Engineer – Client Side

Navigation Research Engineer