AI/ML Research Engineer

About the role

AI/ML Research Engineer focused on post-evaluation and LLM training. This role involves research experience in vision-language models, computer vision, and multimodal AI.

Key responsibilities

• Train, fine-tune, and evaluate transformer-based models
• Implement and design evaluation pipelines for LLM/ML systems, including metrics computation, dataset handling, and experiment comparisons
• Work with LLM training, fine-tuning, and post-training workflows including supervised fine-tuning (SFT), preference optimization (e.g., DPO), and RLHF/RLAIF-style workflows
• Build and maintain production-quality ML code using modern ML frameworks
• Develop and manage data pipelines and ML systems engineering for reproducibility, observability, and debugging
• Optimize performance for large-scale distributed ML systems in GPU/accelerator environments
• Collaborate with research scientists, ML engineers, data engineers, and customer technical leads
• Explain complex technical tradeoffs to both technical and non-technical audiences

About you

• 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, or 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)
• 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
• Strong written and verbal communication skills