This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Join the AI Research & Solutions Group. We're looking for an engineer who builds the models, not just uses them. Someone who has trained neural networks from scratch, constructed datasets that actually work, and implemented papers before the libraries caught up. You understand AI deeply because you've debugged gradient flows at 2am and know why your loss function is misbehaving. As a Lead AI Engineer in the AI Research & Solutions group, you'll work hands-on at least 3 days a week in the office in Menlo Park, leading the development of ML systems that power enterprise AI agents—systems that reason, act, and recover across long-horizon workflows. This is not a research scientist role. This is for the engineer who wrote the code that made the paper possible—the one who turned theory into working systems that ship.
Job Responsibility:
Build & Train Models: Own the full pipeline: architecture design → dataset engineering → distributed training → production deployment
Implement state-of-the-art techniques from papers—often before official implementations exist—translating math into working PyTorch code
Build experimentation infrastructure that enables rapid iteration on architectures, training regimes, and evaluation methods
Build Agent Systems: Create orchestration loops that enable AI to reason across multi-file changes, invoke tools (compilers, test runners, debuggers), and recover from failures
Implement RL for code generation: execution-based rewards (RLVR), process supervision, reward models, and execution semantics alignment
Ship Production AI: Optimize for inference: quantization, distillation, pruning, and serving infrastructure—understanding quality/latency/cost tradeoffs
Design evaluation pipelines that catch regressions and measure what actually matters