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 RoleInnodata 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 OwnAs 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.ResponsibilitiesYou 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 deliveryDesign, build, and improve LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, evaluation, and experiment trackingImplement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnessesIntegrate human-in-the-loop and AI-augmented evaluation signals into model development workflowsBuild robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoringDiagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation driftCollaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systemsWork closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutionsContribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvementsContribute to best practices and standards for LLM training, evaluation, and quality assurance across projectsMentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the teamYou’ll Thrive In This Role If You HaveBS/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 systemsHands-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 workflowstask- or domain-adaptation of foundation modelsStrong programming skills in Python and experience building production-quality ML codeExperience 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 comparisonsStrong understanding of data pipelines and ML systems engineering, including reproducibility, observability, and debuggingExperience 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/evaluationAbility to collaborate directly with technical stakeholders including research scientists, ML engineers, data engineers, and customer technical leadsStrong written and verbal communication skills, including the ability to explain complex technical tradeoffs to both technical and non-technical audiencesTechnical SkillsML / LLM EngineeringExperience training, fine-tuning, and evaluating transformer-based modelsUnderstanding of post-training workflows and model iteration loopsFamiliarity with inference-time considerations (latency, throughput, memory/performance tradeoffs) where relevant to evaluation or deploymentEvaluation & ExperimentationExperience implementing automated evaluation pipelines and test harnessesExperience with experiment tracking, versioning, and reproducibility practicesAbility to assess metric quality and ensure consistency across model comparisonsSoftware / Data EngineeringProficiency in Python and strong software engineering fundamentalsExperience with data processing pipelines, storage formats, and scalable dataset workflowsFamiliarity with CI/CD, testing, and engineering quality practices for ML systemsThe expected salary range for this position is $110,000 – $240,000 CAD per year, based on experience, skills, and qualifications.Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams.If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.