Description:
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.
Responsibilities
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