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

Innodata Inc.

2d•$80,000 - $175,000

About The Position

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.

Requirements

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
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
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
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

Responsibilities

Design and implement the pipelines and tooling that connect data, evaluation, and post-training.
Help customers and internal teams move from evaluation findings to measurable model improvements.
Build fine-tuning workflows (e.g., supervised fine-tuning and preference-based optimization).
Integrate evaluation harnesses into model development loops.
Improve experiment reliability and throughput.
Support advanced evaluation scenarios such as long-context, cross-modal, and dynamic multi-turn interactions.
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, 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.