Innodata Inc.

Technical Solutions Architect, Evals & Fine-Tuning

Remote - United States

Sponsorship not specified$140k-$160kDetected 15 days ago

PythonMachine LearningPyTorchData EngineeringLLMsProduct ManagementProduct StrategyResearch

About the role

As a Technical Solutions Architect for Evals & Fine-Tuning, you are the technical face of Innodata to our most demanding customers.
You sit at the intersection of client AI/ML teams, our research scientists and ML engineers, our subject-matter expert workforce, and our platform teams.

built fine-tuning pipelines, designed eval harnesses, argued with stakeholders about benchmark validity, and earned credibility with sophisticated ML buyers.
Run technical workshops, POCs, and pilot designs that de-risk larger programs and prove value quickly.
Innodata partners with leading foundation model labs, hyperscalers, and enterprise AI teams to build the data, evaluation, and post-training systems that make modern LLMs trustworthy and production-ready.

7+ years of experience in applied ML, ML engineering, ML research, or technical solutions roles, with at least 2+ years focused specifically on LLM evaluation and/or post-training.

Hands-on experience fine-tuning LLMs (SFT at minimum
preference optimization methods like RLHF, DPO, or KTO strongly preferred) and designing the data pipelines that feed them.

The expected salary range for this position is $140,000 – $160,000 USD per year, based on experience, skills, and qualifications.

Bachelor's or advanced degree in computer science, machine learning, computational linguistics, or related field - or equivalent demonstrated experience.

Lead technical discovery with prospective and existing customers - foundation model labs, frontier AI teams, and large enterprises - to understand model objectives, gaps, and constraints.
Feed customer signal back into Innodata's R&D and product roadmap - what benchmarks customers actually want, where eval methodology is breaking, what new fine-tuning paradigms are gaining traction.

This listing is sourced directly from Innodata Inc.'s careers page and normalized into a canonical job model.