Position Details
About this role
This role involves leading the development of scalable ML infrastructure for LLM post-training, evaluation, and deployment, with a focus on feedback systems and distributed workflows.
Key Responsibilities
- Build ML evaluation and deployment pipelines
- Manage feedback and reward systems
- Optimize distributed training workflows
- Ensure reproducibility and operational excellence
- Collaborate with research and engineering teams
Technical Overview
The environment includes Python, ML infrastructure, distributed systems, and pipelines for training, evaluation, and feedback management of large-scale models.
Ideal Candidate
The ideal candidate is a lead ML engineer with over 5 years of experience in ML infrastructure, specializing in scalable evaluation, deployment pipelines, and feedback systems for LLMs. They possess strong leadership skills and a deep understanding of distributed systems and operational best practices.
Must-Have Skills
Nice-to-Have Skills
Tools & Platforms
Required Skills
Hard Skills
Soft Skills
Industry & Role
Keywords for Your Resume
Deal Breakers
Less than 5 years of experience in ML infrastructure, Lack of experience with scalable evaluation pipelines, No background in distributed systems or feedback systems
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