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Key Responsibilities:
- Own technical discovery and solution design for customer engagements across evals and fine-tuning.
- Architect engagements leveraging Innodata's platforms and global SME workforce across 85+ languages.
- Feed customer signal back into R&D and product roadmap.
Requirements:
- 7+ years in applied ML, ML engineering, or technical solutions with 2+ years in LLM evaluation or post-training.
- Hands-on experience fine-tuning LLMs using SFT, RLHF, DPO, or KTO.
- Deep familiarity with LLM evaluation methodology and toolchains.
Culture and Growth:
- A senior individual-contributor role for those with credibility in sophisticated ML buyer environments.
- Opportunities to represent Innodata externally at conferences and in technical content.
- Stay current on state-of-the-art evals and post-training methods.
Innodata
Innodata is a global data engineering company focused on enabling responsible AI advancement through data, evaluation frameworks, and human expertise. With a 36+ year legacy, they provide high-quality data solutions to foundation model labs, hyperscalers, and enterprise AI teams.