AI​/ML Research Engineer, LLM Post-Training & Evaluation

Who we are:

Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are the AI technology solutions provider-of-choice to 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.

By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of clean and optimized digital data to all industries. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.

Our global workforce includes over 3,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years.

Innodata is expanding its team of technical experts in LLM training, post-training, and evaluation systems. As an AI/ML Research Engineer, LLM Training & Evaluation, you will build and optimize the technical foundations that power model improvement for foundation model builders and leading labs.

This role is ideal for someone who has hands-on experience fine-tuning and evaluating large language models (and ideally multimodal models), and who can bridge research and engineering in real-world customer environments. You will work closely with Language Data Scientists, Applied Research Scientists, data engineers, and client technical stakeholders to design and implement robust training/evaluation pipelines using both human-in-the-loop and AI-augmented methods.

The ideal candidate brings a strong computer science / machine learning engineering background, experience with modern LLM post-training workflows, and the ability to engage credibly with technical counterparts at leading AI organizations.

You have at least 2-3 years of relevant experience in machine learning engineering, applied ML systems, or research engineering, with substantial hands‑on work in LLMs and multimodal foundation models. You have built, adapted, or optimized model training and evaluation pipelines, and you understand the practical realities of experimentation at scale: reproducibility, debugging, metrics quality, and iteration speed.

You are comfortable operating in ambiguous, high‑complexity environments and can move from problem framing to implementation. You can collaborate effectively with both researchers and engineers, and you are credible in technical conversations with sophisticated customer stakeholders (e.g., AI researchers, ML engineers, technical product leads).

You bring a builder mindset and strong engineering judgment, while also understanding that evaluation quality and data quality are central to model improvement. You are excited to partner with human evaluation experts and language data scientists to create integrated post‑training and evaluation systems.

As an AI/ML Research Engineer, LLM Training & Evaluation, you will design and implement the pipelines and tooling that connect data, evaluation, and post‑training. You will help customers and internal teams move from evaluation findings to measurable model improvements.

Your work may include building fine‑tuning workflows (e.g., supervised fine‑tuning and preference‑based optimization), integrating evaluation harnesses into model development loops, improving experiment reliability and throughput, and supporting advanced evaluation scenarios such as long‑context, cross‑modal, and dynamic multi‑turn interactions.

You will also contribute to Innodata’s internal R&D efforts, including benchmark datasets, evaluation frameworks, and reusable infrastructure for model assessment and post‑training experimentation.

• Lead or co‑lead technically complex ML engineering projects from initial customer discussions through implementation and delivery
• Design, build, and improve LLM training and post‑training pipelines, including data ingestion, preprocessing, fine‑tuning,…