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

Who we are:

Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are the AI technology solutions provider-of-choice to 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.

By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of clean and optimized digital data to all industries. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.

Our global workforce includes over 3,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years.

Position Summary:

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.

Who We’re Looking For:

You have at least 2-3 years of relevant experience in machine learning engineering, applied ML systems, or research engineering, with substantial hands-on work in LLMs and multimodal foundation models. You have built, adapted, or optimized model training and evaluation pipelines, and you understand the practical realities of experimentation at scale: reproducibility, debugging, metrics quality, and iteration speed.

You are comfortable operating in ambiguous, high-complexity environments and can move from problem framing to implementation. You can collaborate effectively with both researchers and engineers, and you are credible in technical conversations with sophisticated customer stakeholders (e.g., AI researchers, ML engineers, technical product leads).

You bring a builder mindset and strong engineering judgment, while also understanding that evaluation quality and data quality are central to model improvement. You are excited to partner with human evaluation experts and language data scientists to create integrated post-training and evaluation systems.

Tell Me More:

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.

Responsibilities:

• 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

• 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

Preferred Skills

• Experience with multimodal model training/evaluation (text + image/audio/video)
• Experience with long-context evaluation and/or model adaptation for long-context tasks
• Experience with agentic or multi-turn evaluation harnesses, tool-use simulation, or interactive environment testing
• Experience working in customer-facing technical consulting, solutions engineering, or applied research delivery
• Familiarity with LLM safety, alignment, robustness, or red-teaming evaluation approaches
• Contributions to open-source ML/LLM tooling or published technical work in relevant areas

How this role partners with the team

This role works closely with:

• Language Data Scientists, who lead human evaluation design, language/data process excellence, and annotation/synthetic workflows
• Applied Research Scientists, who lead evaluation methodology, benchmarking research, and experimental design
• Data Engineers / Platform Teams, who support scalable data and infrastructure foundations
• Customer Technical Stakeholders, who rely on Innodata for expert guidance and implementation support in advanced GenAI development

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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.

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