Job Description

Machine Learning Research Engineer

Location: Onsite

My client, a stealth-mode AI company is seeking a Machine Learning Research Engineer to help build the next generation of foundation models and multi modal systems. This is a high impact role for someone who thrives at the across research and engineering, and who is passionate about scaling cutting-edge AI systems in production environments.

Key Responsibilities

• Train foundation models from scratch across multiple modalities (e.g., text, vision, structured data).
• Fine-tune large language models (LLMs) and multi-modal models (MMMs) to solve diverse real-world problems.
• Design models capable of constraint-based reasoning (discrete and continuous) and graph-related tasks.
• Generate and curate high-quality training datasets, both synthetically and via human interaction.
• Build and optimize both open-source and proprietary models with performance and efficiency in mind.
• Translate research papers into production-grade code.
• Develop and optimize advanced retrieval and inference-time search algorithms.
• Operate across distributed infrastructure and contribute to HPC performance tuning.

Ideal Candidate Profile

• Strong programming and systems knowledge, especially in Python , PyTorch , CUDA , and ideally Triton .
• Extensive experience with generative models, sequence modeling, and model architecture design.
• Hands-on background in training and fine-tuning large-scale multi-modal models.
• Publication record in top-tier AI venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR).
• Strong ability to productionize ML research in distributed systems.
• Up to date on modern techniques like prompting, model compression, and inference optimization.
• Experience with large-scale training on high-performance computing (HPC) clusters.

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