Innodata Inc.

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

Remote - United States

Sponsorship not specified$80k-$175kDetected 15 days ago

PythonCI/CDMachine LearningTensorFlowPyTorchData EngineeringNLPLLMsAI OrchestrationA/B TestingSystems EngineeringResearchCommunication

About the role

Innodata is expanding its team of technical experts in LLM training, post-training, and evaluation systems.
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.

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
Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring
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
Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team

supervised fine-tuning (SFT)
preference optimization (e.g., DPO or related methods)
RLHF / RLAIF-style workflows
task- or domain-adaptation of foundation models
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 training, fine-tuning, and evaluating transformer-based models
Understanding of post-training workflows and model iteration loops
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
ML / LLM Engineering
Evaluation & Experimentation
Software / Data Engineering
The expected salary range for this position is $80,000 - $175,000 USD per year, based on experience, skills, and qualifications.

The expected salary range for this position is $80,000 – $175,000 USD per year, based on experience, skills, and qualifications.

BS/MS/PhD in Computer Science, Machine Learning, AI, Applied Mathematics, or a related quantitative technical field (MS/PhD preferred)

This listing is sourced directly from Innodata Inc.'s careers page and normalized into a canonical job model.