[Remote] AI/ML Scientist/Engineer(remote)
Note: The job is a remote job and is open to candidates in USA. Programming.com is seeking an AI/ML Scientist/Engineer to advance AI for drug discovery. The role involves designing, training, optimizing, and deploying deep learning models for molecular design and optimization, scaling ML infrastructure, managing MLOps workflows, integrating multimodal scientific data, and collaborating with scientific and engineering teams.
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
Skills
- • 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
- • 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
- • 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
- • Geometric Deep Learning
- • Cofolding Models
- • Neural Force Fields
- • Molecular modeling
- • Generative models
- • Multimodal Transformers
- • Molecular property prediction
- • Drug Discovery
- • Computational Biology
- • Genomics
- • Transcriptomics
- • Medical Imaging
- • Clinical data
- • Molecular datasets
Benefits
- Remote work arrangement
Company Overview