Applied Research Scientist, LLM Evaluation & Post-Training

Who we are:

Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2000 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 worlds 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 were 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 3000 employees in the United States Canada United Kingdom the Philippines India Sri Lanka Israel and Germany. Were poised for a period of explosive growth over the next few years.

Position Summary:

Innodata is expanding its GenAI research capability to advance state-of-the-art evaluation and post-training methods for LLM and multimodal systems. As an Applied Research Scientist LLM Evaluation & Post-Training you will lead research and experimentation on how evaluation design measurement strategies and feedback signals influence model improvement.

This role is ideal for a technically rigorous researcher who is deeply fluent in modern LLM evaluation and post-training and who can turn research insight into practical methods for customer solutions and internal platform innovation. You will work across human-in-the-loop and AI-augmented workflows partnering with Language Data Scientists and AI/ML Research Engineers to design and validate evaluation frameworks that drive measurable model gains.

The ideal candidate combines strong experimental and statistical judgment with hands-on technical ability and can engage as a peer with research and engineering stakeholders at leading AI companies.

Who Were Looking For:

You have at least 5 years of relevant experience (including graduate research) in applied ML research research science or advanced ML experimentation with significant experience in LLM evaluation benchmarking alignment or post-training. You have a track record of designing high-quality experiments interpreting results rigorously and translating findings into practical improvements.

You are comfortable working across research and product/customer contexts. You can identify important methodological questions build a research agenda and collaborate with engineers and data experts to execute. You understand that evaluation is not only about metrics but about measurement validity robustness stress testing and alignment to real-world usage.

You are excited by frontier challenges including long-context cross-modal and dynamic multi-turn evaluations and by the opportunity to build new benchmark datasets and evaluation frameworks that become strategic assets for Innodata and its customers.

You bring an implementation-minded approach to experimentation and are comfortable collaborating closely with engineers to productionize methods and research outputs when appropriate.

Tell Me More:

As an Applied Research Scientist LLM Evaluation & Post-Training you will help define the next generation of evaluation-driven model improvement workflows. You will study how different evaluation approaches (human automated hybrid) shape model selection and post-training outcomes and you will design experiments that produce credible actionable conclusions.

Your work may include designing benchmark datasets developing evaluation taxonomies and protocols defining metrics and scoring methodologies analyzing failure modes and testing how changes in evaluation setup affect downstream fine-tuning results. You will also support customer engagements by bringing scientific rigor to evaluation strategy methodology review and technical recommendations.

This is a highly collaborative role that sits at the intersection of research engineering and language/data operations.

Responsibilities:

• Define and execute a research agenda focused on LLM evaluation and post-training especially evaluation-driven model improvement

• Design rigorous experiments to study how evaluation methodologies impact fine-tuning and post-training outcomes

• Develop and validate evaluation frameworks for LLM and multimodal systems including:

  • benchmark/task design

  • scoring methods

  • judge/model-assisted evaluation

  • human evaluation protocols

  • robustness/stress testing

• Lead research on advanced evaluation domains including long-context cross-modal and dynamic multi-turn evaluations

• Study the effectiveness and limitations of existing evaluation techniques and propose improved methodologies with clear validity and scalability tradeoffs

• Analyze model behavior and failure patterns; generate actionable recommendations for model improvement and evaluation redesign

• Collaborate with AI/ML Research Engineers to translate research methods into scalable evaluation and post-training pipelines

• Collaborate with Language Data Scientists to integrate human-in-the-loop and synthetic data/evaluation strategies into research programs

• Engage with customer technical stakeholders to understand evaluation goals review methodologies and provide expert recommendations

• Contribute to internal benchmark datasets evaluation frameworks and reusable research assets

• Produce high-quality technical documentation internal research reports and client-facing materials explaining methods results assumptions and limitations

• Contribute to thought leadership and best practices in LLM evaluation post-training and GenAI quality measurement

• MS/PhD in Computer Science Machine Learning Statistics Applied Mathematics AI or a related quantitative scientific field (PhD strongly preferred)

• 5 years of relevant experience in applied research / research science in ML/AI with substantial work in LLMs or foundation models

• Demonstrated experience with LLM evaluation benchmarking alignment post-training or model quality research

• Strong foundation in experimental design statistical analysis and scientific reasoning for ML systems

• Strong coding skills in Python for research experimentation and analysis (e.g. data processing evaluation pipelines statistical analysis visualization)

• Experience working with modern ML tooling/frameworks (e.g. PyTorch Hugging Face JAX/TensorFlow as applicable) sufficient to design and execute model/evaluation experiments

• Ability to evaluate and compare human and automated evaluation methods including tradeoffs in cost reliability validity and scalability

• Experience designing evaluation studies and protocols that are reproducible across datasets model versions and evaluation runs

• Ability to collaborate directly with technical stakeholders including research scientists ML engineers data scientists and customer technical counterparts

• Strong communication skills and ability to present nuanced technical conclusions assumptions and limitations clearly

Technical Skills

Evaluation Science & Benchmarking

• Experience designing benchmark datasets test suites or evaluation frameworks for language or multimodal models

• Deep understanding of metric design scoring reliability and measurement validity

• Experience with human evaluation methods and quality assurance considerations (e.g. rubric design inter-rater reliability adjudication frameworks)

LLM / Post-Training

• Understanding of post-training methods and how training objectives interact with evaluation outcomes

• Ability to reason about model behavior failure modes and tradeoffs across tasks/domains

• Familiarity with alignment and robustness considerations in model evaluation

Quantitative Analysis

• Strong statistical analysis skills (sampling uncertainty significance testing where appropriate error analysis metric interpretation)

• Ability to synthesize complex experimental findings into actionable recommendations

Preferred Skills

• Hands-on experience running or supporting fine-tuning/post-training experiments (SFT preference optimization RLHF/RLAIF-style workflows)

• Experience with multimodal evaluation (e.g. text-image audio video)

• Experience with long-context benchmarking/evaluation and real-world context management challenges

• Experience designing multi-turn interactive or agentic evaluation protocols

• Published research and/or open-source benchmark contributions in LLM evaluation post-training alignment or related areas

• Experience in customer-facing applied research technical consulting or cross-functional product/research collaborations

• Familiarity with safety trustworthiness and governance considerations in GenAI evaluation

How this role partners with the team

This role works closely with:

• Language Data Scientists who bring deep expertise in language data human evaluation workflows multilingual/multimodal process design and data quality operations

• AI/ML Research Engineers who implement scalable training/evaluation systems and connect research methods to production-grade pipelines

• Business and Customer Teams who rely on Innodata for expert consultation and credible technically rigorous GenAI solutions

• Internal R&D and Platform Teams to transform research outputs into reusable frameworks benchmarks and differentiated offerings

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