Machine Learning Engineer

About the role At Copoly.ai, we are a dynamic biotech and AI company driving innovation by working on our own proprietary products and developing specialized solutions for our clients in pharma, biotech, and beyond. We are transforming the future of early cancer detection through AI-powered diagnostic solutions. Our flagship product, OncoSage, leverages RNA sequencing and proprietary machine learning algorithms to deliver accurate, blood-based cancer detection. We are committed to advancing the field of oncology through cutting-edge technology, improving patient outcomes, and detecting cancer at its earliest stages. Join us in our mission to make revolutionary strides in healthcare technology. About the Job We are looking for a highly motivated and skilled AI/ML Scientist to join our project team at Copoly. This role involves working with one of our pharma clients and is dedicated to transforming drug discovery by designing, developing, training, and deploying new engines for lab-in-the-loop molecular design and optimization. This work spans sequence, structure, conformational ensembles, and molecular property prediction, and architectural innovation from natural language, computer vision, and robotics. The successful candidate will work in a multidisciplinary environment alongside ML scientists, ML engineers, and drug designers at the research frontier. Key Responsibilities Model Development & Deployment: Design, optimize, evaluate, and deploy cutting-edge deep learning models (e.g., large language models, multi-modal transformers, generative models) and data pipelines. Scaling & Infrastructure: Optimize and scale training and inference for performance and accuracy 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 understand associations between diverse datasets, including omics (genomics, transcriptomics), imaging, and clinical data. Scientific Collaboration: Collaborate with cross-functional teams to translate novel machine learning methods into impactful applications for disease understanding and clinical decision-making. What we are looking for Education: B.S., Master’s or PhD in Computer Science, Machine Learning, Computational Biology, Data Science, Statistics, Mathematics, or a related quantitative field. Industry Experience: 1–5 years of relevant experience (inclusive of post-doctoral work) Technical Skills Software Engineering: Strong foundations in data structures, algorithms, and software engineering principles. Programming: Expert-level proficiency in Python and extensive experience with deep learning frameworks such as PyTorch (preferred), JAX, or TensorFlow. An idea of how to debug that goes beyond pointing a coding agent at the problem. Modeling & Infrastructure: Experience with large-scale distributed training (e.g., DDP, Ray, FSDP, DeepSpeed) and model deployment tools (e.g., Triton, ONNX). AI/ML Specialization: Hands-on experience with geometric deep learning, cofolding models, or neural force fields is highly preferred. Soft Skills & Professional Attributes Problem Solving: Proven ability to take full ownership of technical challenges and proactively drive solutions from start to finish. Communication: Strong technical communication skills with the ability to articulate complex concepts to both technical and non-technical audiences. Domain Knowledge: Prior experience in drug discovery, genomics, or medical imaging is a plus but not required.

The role involves designing, training, and deploying deep learning models for lab-in-the-loop molecular design and optimization. Key duties include managing MLOps workflows and integrating multi-modal datasets such as omics and clinical data.

Candidates need a degree in a quantitative field and 1-5 years of experience in AI/ML, with expert-level Python and PyTorch skills. Experience with large-scale distributed training and geometric deep learning is highly preferred.

• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Python
• PyTorch
• Deep Learning
• MLOps
• Distributed Training
• Geometric Deep Learning
• Large Language Models
• Multi-modal Transformers
• Generative Models
• Data Structures
• Algorithms
• Cloud Infrastructure
• Drug Discovery
• Genomics
• Medical Imaging
• Software Engineering

• Science & Research
• Technology
• Healthcare
• Engineering
• Data & Analytics

Minimum education

Bachelor’s degree

Minimum experience

2+ years

Posting language

English

Working hours

40 hours per week

Seniority

Entry level