Research Engineer

What you'll do

• Develop neuro-symbolic AI systems, knowledge representations, and reasoning approaches.
• Design evaluation frameworks and benchmarks; run experiments, ablations, and failure analyses.
• Build post-training, data synthesis, and production pipelines, measuring correctness, coverage, latency, and cost.

What they ask for

• Strong software engineering fundamentals and hands-on experience building large-scale ML systems, research infrastructure, or high-performance training pipelines.
• Experience training or post-training large foundation models, including distributed training, fine-tuning, reinforcement learning, and large-scale experimentation.
• Deep proficiency in Python and modern ML frameworks or equivalent distributed training and inference infrastructure.
• Experience building agentic AI systems and designing benchmarks, ablation studies, and evaluations of model behavior.

Nice to have

• Experience building autoformalization, theorem-proving, or model-and-symbolic-tool systems.
• Experience with Lean, Coq, Isabelle, SMT solvers, compilers, or program analysis.

Tools

Python, PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, Lean 4, Coq

AI focus

Develop neuro-symbolic AI systems for knowledge formalization, reasoning, and verification, including foundation-model post-training, reinforcement learning, synthetic data generation, and model self-improvement.