AI Research Scientist

What you'll do

• Conduct research in generative AI to build specialized foundation models and trained agents for observability.
• Train multimodal models on large-scale telemetry data including metrics, logs, traces, topology, and events.
• Design and build simulated environments and RL training loops for on-policy agent training and evaluation.
• Translate research advances into product features by collaborating with cross-functional engineering and product teams.
• Stay at the forefront of foundation models, world models, and reinforcement learning research.
• Publish research findings in top-tier conferences and open-source key model artifacts and benchmarks.

What we're looking for

• Hold a PhD in Computer Science, Machine Learning, or a related field with equivalent experience.
• Possess deep expertise in generative modeling, world models, AI agents, reinforcement learning, or multimodal learning.
• Have extensive experience designing and implementing deep learning models and agents.
• Demonstrate proficiency with distributed training frameworks like DeepSpeed or Megatron-LM and the PyTorch library.
• Maintain a track record of impactful publications at top-tier venues such as NeurIPS, ICLR, ICML, or TMLR.
• Be familiar with efficient training, post-training, and inference techniques for large foundation models.
• Ability to communicate complex research findings to both technical and non-technical audiences.
• Experience in GPU programming, CUDA optimization, or building production data pipelines is preferred.