AI/ML Scientist/Engineer(remote)

Role:AI / ML Scientist / Engineer

Location:remote

Duration: Long Term

Position Overview

We are seeking a highly motivated AI/ML Scientist/Engineer to work on advanced AI for Drug Discovery initiatives. This role focuses on transforming drug discovery through the design, development, training, optimization, and deployment of advanced deep learning models for lab-in-the-loop molecular design and optimization.

The work spans sequence, structure, conformational ensembles, molecular property prediction, and architectural innovation across natural language processing, computer vision, and robotics. The successful candidate will collaborate with ML Scientists, ML Engineers, and Drug Designers in a multidisciplinary research environment.

Key Responsibilities

• Model Development & Deployment: Design, optimize, evaluate, and deploy cutting-edge deep learning models, including Large Language Models (LLMs), multimodal transformers, and generative models, along with supporting data pipelines.
• Scaling & Infrastructure: Optimize and scale training and inference using multi-GPU and cloud infrastructure.
• MLOps & Maintenance: Implement and manage MLOps workflows, including model deployment, version control, and performance monitoring to ensure robustness and reproducibility.
• Multi-modal Data Integration: Develop algorithms to identify associations across diverse datasets, including omics (genomics, transcriptomics), imaging, and clinical data.
• Scientific Collaboration: Partner with cross-functional scientific and engineering teams to translate novel machine learning methods into applications for disease understanding and clinical decision-making.

Education & Experience

• B.S., Master's, or PhD in Computer Science, Machine Learning, Computational Biology, Data Science, Statistics, Mathematics, or a related quantitative discipline.
• 1–5 years of relevant industry experience, inclusive of postdoctoral work.

Technical Skills

• Strong foundations in data structures, algorithms, and software engineering principles.
• Expert-level proficiency in Python.
• Extensive hands-on experience with PyTorch, JAX, or TensorFlow.
• Strong understanding of debugging complex AI/ML systems beyond basic code-level issues.
• Experience with large-scale distributed model training, including technologies such as:
• DDP
• Ray
• FSDP
• DeepSpeed
• Experience with model deployment technologies such as Triton and ONNX.
• Experience optimizing multi-GPU training and inference.
• Strong understanding of MLOps, model deployment, monitoring, versioning, and reproducibility.

Preferred AI/ML Specialization

Hands-on experience in one or more of the following is highly preferred:

• Geometric Deep Learning
• Cofolding Models
• Neural Force Fields
• Molecular modeling
• Generative models
• Multimodal Transformers
• Molecular property prediction

Domain Experience

Prior experience in any of the following is a strong plus:

• Drug Discovery
• Computational Biology
• Genomics
• Transcriptomics
• Medical Imaging
• Clinical data
• Molecular datasets

Professional Attributes

• Strong ownership of complex technical challenges from start to finish.
• Excellent technical communication skills with both technical and non-technical stakeholders.
• Ability to work effectively in multidisciplinary research environments.
• Strong problem-solving and scientific collaboration capabilities.