AI/ML Research Engineer, LLM Training & Evaluation

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