Foundation Model Researcher, Molecular Science

CyRAD Talent Solutions; New York (NY); Hybrid; USD 300,000 - 800,000; Full time

Job summary

CyRAD Talent Solutions is seeking a top-tier LLM Research Engineer to advance large language and multimodal models for molecular science and drug discovery. The role blends foundational AI research with system design and scalable training on HPC resources, with collaboration across computational and molecular scientists in a hybrid New York environment.

Ideal candidates are strong in ML theory and engineering, able to ship production-grade systems, and comfortable working in a multi-disciplinary

Qualifications

• Strong background in ML, CS, or quantitative field.
• Experience with large-scale ML systems and training infrastructure.
• Solid Python programming and ML engineering skills.
• Ability to conduct rigorous research and ship production-grade systems.

Responsibilities

• Research, develop, and scale large language and multimodal models for complex scientific problems.
• Design pre-training pipelines and distributed training systems for large models.
• Explore post-training methods including reinforcement learning, contrastive learning, and instruction tuning.
• Build multimodal models spanning text, molecular graphs, 3D structures, time-series data.
• Optimize large-scale training and inference on high-performance computing infrastructure.
• Collaborate with ML researchers, computational scientists, and domain experts to translate model advances into capabilities.

Skills

Python

ML engineering

Distributed training

Model scaling

HPC

Pre/post training

Research systems

Job description

LLM Research Engineer - Scientific Discovery

• New York, New York, United States

Location: New York, NY
Work Model: Hybrid
Compensation: $300K to $800K base + substantial variable compensation and additional incentives

Overview

This role sits inside a well-resourced, interdisciplinary research organization applying frontier large language models and machine learning to molecular science and drug discovery. The environment combines foundational AI research, large-scale model development, and direct collaboration with computational and molecular scientists, with computational infrastructure built specifically for research at this scale.

The position owns the research and engineering behind large language and multimodal models for scientific problems: architecture, pre-training, post-training, scaling, and the systems that make all of it run efficiently on high-performance compute. The mandate is not to integrate existing tools but to extend what large-scale models can do in science.

Candidates who thrive here combine rigorous research instincts with the ability to ship real, working systems. Molecular science or drug discovery background is not required; depth and versatility in machine learning matter more.

What You’ll Do

• Research, develop, and scale large language and multimodal models for complex scientific problems
• Design pre-training pipelines and distributed or parallel training systems for large models
• Explore advanced post-training methods including reinforcement learning, contrastive learning, and instruction tuning
• Build multimodal models spanning text, molecular graphs, 3D structures, time-series data, and other scientific modalities
• Optimize large-scale training and inference across high-performance computing infrastructure
• Partner with ML researchers, computational scientists, and domain experts to turn model advances into new capabilities for molecular science and drug discovery

What We’re Looking For

• Exceptional background in machine learning, computer science, mathematics, or a related quantitative field
• Deep expertise in large-scale ML systems, LLM architecture and training, and/or multimodal learning
• Strong Python programming and hands-on ML engineering ability
• Experience with distributed training, model scaling, training infrastructure, or high-performance computing
• Strong command of modern pre-training and/or post-training methods
• Demonstrated ability to conduct rigorous research and also build production-quality systems
• Exceptional record of academic, research, technical, or professional achievement
• Ability to work in the New York City office three days per week

Preferred

• Exposure to molecular science, structural biology, or drug discovery problems (helpful, not required)
• Experience building models over non-text scientific modalities such as graphs, 3D structures, or time series

Compensation

$300K to $800K base salary, plus substantial variable compensation and additional incentives. Hybrid schedule with three days per week in the New York City office.