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

Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: 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. What You’ll Own: 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. Additional responsibilities include (but are not limited to): 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, evaluation, and experiment tracking Implement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnesses Integrate human-in-the-loop and AI-augmented evaluation signals into model development workflows Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring Diagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation drift Collaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systems Work closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutions Contribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvements Contribute to best practices and standards for LLM training, evaluation, and quality assurance across projects Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team You’ll Thrive in This Role If You Have: BS/MS/PhD in Computer Science, Machine Learning, AI, Applied Mathematics, or a related quantitative technical field (MS/PhD preferred) 2-3 years of relevant industry or research engineering experience in ML/AI systems Hands-on experience with LLM training / fine-tuning / post-training, including at least one of: supervised fine-tuning (SFT) preference optimization (e.g., DPO or related methods) RLHF / RLAIF-style workflows task- or domain-adaptation of foundation models Strong programming skills in Python and experience building production-quality ML code Experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) and model libraries/tooling (e.g., Hugging Face ecosystem, vLLM, distributed training stacks) Experience designing and implementing evaluation pipelines for LLM/ML systems, including metrics computation, dataset handling, and experiment comparisons Strong understanding of data pipelines and ML systems engineering, including reproducibility, observability, and debugging Experience with large-scale distributed ML systems and performance optimization for training/evaluation workloads (GPU/accelerator environments preferred) Experience with large-scale data processing and workflow orchestration in support of model training/evaluation Ability to collaborate directly with technical stakeholders including research scientists, ML engineers, data engineers, and customer technical leads Strong written and verbal communication skills, including the ability to explain complex technical tradeoffs to both technical and non-technical audiences Technical Skills ML / LLM Engineering Experience training, fine-tuning, and evaluating transformer-based models Understanding of post-training workflows and model iteration loops Familiarity with inference-time considerations (latency, throughput, memory/performance tradeoffs) where relevant to evaluation or deployment Evaluation & Experimentation Experience implementing automated evaluation pipelines and test harnesses Experience with experiment tracking, versioning, and reproducibility practices Ability to assess metric quality and ensure consistency across model comparisons Software / Data Engineering Proficiency in Python and strong software engineering fundamentals Experience with data processing pipelines, storage formats, and scalable dataset workflows Familiarity with CI/CD, testing, and engineering quality practices for ML systems The expected salary range for this position is $80,000 – $175,000 USD per year, based on experience, skills, and qualifications. 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