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.
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