Research Engineer, Post-Training (All Industry Levels)
About the role and team Joining us as a Research Engineer on the Post-Training team, you'll be diving into the exciting world of fine-tuning AI models, optimizing their performance, and ensuring they meet the highest standards of quality and efficiency. Your work will directly contribute to our groundbreaking advanceme
What this role actually needs.
About the role and team Joining us as a Research Engineer on the Post-Training team, you'll be diving into the exciting world of fine-tuning AI models, optimizing their performance, and ensuring they meet the highest standards of quality and efficiency. Your work will directly contribute to our groundbreaking advanceme Responsibilities: - Develop alignment algorithms and loss functions to improve data sample efficiency. - Write data pipelines to process diverse web data into a format models can ingest. - Identify quality signals to understand our model’s performance in the real world. - Design sampling algorithms to improve serving efficiency of large generative models. Requirements: - "All Industry Levels": have at least PhD (or equivalent) - Write clear and clean production-facing and training code - Experience working with GPUs (training, serving, debugging) - Experience with data pipelines and data infrastructure - Strong understanding of modern machine learning techniques (reinforcement learning, transformers, etc) - Track-record of exceptional research or creative applied ML projects 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, Post-Training (All Industry Levels) role.
- Develop alignment algorithms and loss functions to improve data sample efficiency.
- Write data pipelines to process diverse web data into a format models can ingest.
- Identify quality signals to understand our model’s performance in the real world.
- Design sampling algorithms to improve serving efficiency of large generative models.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- "All Industry Levels": have at least PhD (or equivalent)
- Write clear and clean production-facing and training code
- Experience working with GPUs (training, serving, debugging)
- Experience with data pipelines and data infrastructure
- Strong understanding of modern machine learning techniques (reinforcement learning, transformers, etc)
- Track-record of exceptional research or creative applied ML projects
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Research Engineer, Post-Training (All Industry Levels) is framed against UpJobz source checks, country scope, compensation visibility, and work-authorization signals so candidates can make a faster go/no-go decision.
United States tech market
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Compensation read
$225K - $400K is visible before the click, so candidates can compare the role against local market expectations before applying.
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Current extracted signal: Open to TN, H-1B, and OPT candidates already in the United States. UpJobz treats this as a search signal, not legal advice, and links visa-sensitive roles back to the relevant visa hub where possible.
Location read
Hybrid roles in Redwood City or New York City should be compared against commute, local salary bands, and nearby employer demand.
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Source: jobs.ashbyhq.com · Source ID: 449bf7c1-41d7-47a4-89a7-0ee0b31f5918 · Confidence: 92/100 · Last checked: May 7, 2026
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