Senior Machine Learning Engineer, Data and ML Innovation

What you'll do

• Design and implement large-scale LLM pretraining, supervised fine-tuning, and preference optimization pipelines.
• Drive model hillclimbing through systematic experimentation including dataset curation, hyperparameter tuning, and ablation studies.
• Develop evaluation frameworks for offline benchmarks and online metrics covering reasoning and tool use.
• Design and maintain verifiers and rubric-based reward systems for agentic tasks and model alignment.
• Build data pipelines for automated data generation, filtering, labeling, and replay buffers.
• Build and maintain agent training environments including tool APIs, simulators, and sandboxed runtimes.
• Implement environment abstractions to support reinforcement learning and large-scale agent evaluation.
• Develop scalable training workflows using distributed frameworks and containerized RL infrastructure.

What we're looking for

• Bachelor's, Master's, or PhD in Computer Science, Machine Learning, or a related technical field (or equivalent practical experience).
• 5+ years of hands-on machine learning engineering experience.
• At least 1 year of experience working directly on large language models or generative AI.
• Hands-on experience with LLM training workflows including pretraining, SFT, and preference optimization like RLHF, DPO, or PPO.
• Strong software engineering fundamentals including debugging, testing, code reviews, and production reliability.
• Demonstrated publication records in relevant conferences such as NeurIPS, ICML, or ICLR.
• Experience with agentic systems, tool use, environment design, or reinforcement learning (preferred).
• Experience building training environments, simulators, or model hillclimbing workflows (preferred).