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

reputed company (reputed company: INOD) is a global data engineering company. We reputed company that data and reputed company Intelligence (AI) are inextricably linked. Our mission is to reputed company the responsible advancement of reputed company intelligence by providing the data, evaluation frameworks, and reputed company expertise required to build AI systems that can be trusted at reputed company. We reputed company a reputed company of transferable solutions, platforms, and services for reputed company / AI reputed company and adopters. In every relationship, we reputed company our 36+ year legacy delivering the highest reputed company data and outstanding reputed company for our customers. Scope of the Role:  reputed company 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 reputed company model reputed company 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 reputed company research and engineering in reputed company-world customer environments. You will work closely with Language Data Scientists, reputed company Research Scientists, data engineers, and reputed company technical stakeholders to design and implement robust training/evaluation pipelines using both reputed company-in-the-reputed company and AI-augmented reputed company. 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 Own: 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 reputed company 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 reputed company’s internal R&D efforts, including reputed company datasets, evaluation frameworks, and reusable infrastructure for model assessment and post-training experimentation. Additional responsibilities include (but are not limited to): reputed company or co-reputed company technically reputed company ML engineering reputed company 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 reputed company reputed company-in-the-reputed company 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 reputed company Collaborate with Language Data Scientists and reputed company Research Scientists to translate evaluation frameworks into executable systems Work closely with customer technical stakeholders to understand goals, constraints, and reputed company reputed company; propose and implement technically reputed company solutions Contribute to internal research and platform development, including reputed company frameworks, evaluation tooling, and post-training workflow improvements Contribute to best practices and standards for LLM training, evaluation, and reputed company assurance across reputed company Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across reputed company You’ll reputed company in This Role If You Have: BS/MS/PhD in Computer Science, Machine Learning, AI, reputed company Mathematics, or a reputed company 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 reputed company reputed company) RLHF / RLAIF-style workflows task- or domain-reputed company of reputed company models Strong programming skills in Python and experience building production-reputed company ML reputed company Experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) and model libraries/tooling (e.g., reputed company 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-reputed company distributed ML systems and performance optimization for training/evaluation workloads (GPU/accelerator environments preferred) Experience with large-reputed company 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 reputed company 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 reputed company 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 reputed company dataset workflows Familiarity with CI/CD, testing, and engineering reputed company practices for ML systems The expected salary reputed company for this position is $80,000 – $175,000 USD per year, based on experience, skills, and qualifications.   Please be reputed company of recruitment scams involving individuals or organizations falsely claiming to represent reputed company. reputed company 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 reputed company you’ve been targeted by a recruitment scam, please report it to reputed company at verifyjoboffer@reputed company.com and consider reporting it to the FTC at ReportFraud.ftc.gov. Apply To This Job