Machine Learning Researcher

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.