Research Engineer, ML Systems (All Industry Levels)
About the role and team Joining us as a Research Engineer on the ML Systems team, you’ll be working on cutting-edge ML training and inference systems, optimizing the performance and efficiency of our GPU clusters, and developing new technologies that fine-tune leading consumer AI models with a data flywheel, and serve
What this role actually needs.
About the role and team Joining us as a Research Engineer on the ML Systems team, you’ll be working on cutting-edge ML training and inference systems, optimizing the performance and efficiency of our GPU clusters, and developing new technologies that fine-tune leading consumer AI models with a data flywheel, and serve Responsibilities: - Write efficient Triton kernels and tune them for our specific models and hardware - Develop prefix-aware routing algorithms to improve serving cache hit rate - Train and distill LLMs to improve latency while preserving accuracy and engagements - Build an efficient and scalable distributed RLHF stack powering the model innovations - Develop systems for efficient multimodal (image gen/video gen) model training & inference Requirements: - "All Industry Levels": at least PhD (or equivalent) research experience - Write clear and clean production system code - Strong understanding of modern machine learning techniques (reinforcement learning, transformers, etc) - Track record of exceptional research or creative ML systems projects - Comfortable writing model development code (PyTorch) for either training or inference Company context: Character.AI builds personalized conversational AI agents and the infrastructure that powers them at scale.
Day-to-day expectations
Character.AI lists these responsibilities for the Research Engineer, ML Systems (All Industry Levels) role.
- Write efficient Triton kernels and tune them for our specific models and hardware
- Develop prefix-aware routing algorithms to improve serving cache hit rate
- Train and distill LLMs to improve latency while preserving accuracy and engagements
- Build an efficient and scalable distributed RLHF stack powering the model innovations
- Develop systems for efficient multimodal (image gen/video gen) model training & inference
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- "All Industry Levels": at least PhD (or equivalent) research experience
- Write clear and clean production system code
- Strong understanding of modern machine learning techniques (reinforcement learning, transformers, etc)
- Track record of exceptional research or creative ML systems projects
- Comfortable writing model development code (PyTorch) for either training or inference
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Source: jobs.ashbyhq.com · Source ID: 3ab40c3d-63bd-4634-a126-5a3d25d3263b · Confidence: 92/100 · Last checked: May 7, 2026
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