Machine Learning Scientist (contract)

About the Role:
The Machine Learning Scientist Contractor is a short term contract role focused on developing advanced deep learning models and scalable machine learning pipelines for complex biological and genomic datasets. This position works closely with computational scientists and data teams to design, train, and deploy production ready models that support next generation diagnostics, biomedical research, and large scale data analysis. The role emphasizes expertise in computer vision, deep learning architectures, and end to end machine learning development in production environments.

Responsibilities:

• Design and implement machine learning and deep learning models, including computer vision models for image enhancement, classification, and feature extraction from complex biological or genomic datasets.

• Develop scalable machine learning pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, validation, and deployment.

• Optimize model performance using modern deep learning architectures such as convolutional neural networks, vision transformers, generative adversarial networks, and variational autoencoders.

• Build production grade machine learning systems that can process large scale datasets in distributed or cloud based environments.

• Write clean, maintainable, and well documented Python code following software engineering best practices including version control, testing, and code review.

• Translate ideas from machine learning and artificial intelligence research papers into working prototypes and production ready implementations.

• Collaborate with cross functional teams including computational scientists, data engineers, and research stakeholders to refine model design and evaluate performance.

• Communicate technical findings, model tradeoffs, and experimental results clearly to both technical and non technical audiences.

Qualifications:

• Masters degree with at least 2 years of industry or research experience, or a PhD in Computer Science, Machine Learning, Artificial Intelligence, Robotics, Computational Biology, or a related quantitative field.

• Strong expertise in machine learning, deep learning, and artificial intelligence including experience with modern architectures such as CNNs, transformers, GANs, and representation learning models.

• Extensive hands on experience training, evaluating, and optimizing deep learning models using frameworks such as PyTorch, TensorFlow, or JAX.

• Proficiency in Python programming for machine learning development, data processing, and scientific computing.

• Experience designing machine learning experiments, performing model validation, and improving model accuracy through performance tuning and hyperparameter optimization.

• Knowledge of software development best practices including modular code design, automated testing, documentation, and Git based version control workflows.

• Experience working with cloud computing platforms such as AWS, Google Cloud Platform, or Microsoft Azure for model training and deployment.

• Strong analytical thinking, problem solving skills, and the ability to work independently in a fast paced research or engineering environment.

Desired Qualifications:

• Background in genomics, computational biology, bioinformatics, or biomedical data analysis.

• Experience applying machine learning or computer vision techniques to biological, medical, or scientific datasets.

• Familiarity with MLOps practices including experiment tracking, model registries, pipeline automation, and continuous integration and deployment for machine learning.

• Experience working with large scale datasets, distributed computing frameworks, or GPU accelerated training environments.

• Contributions to open source machine learning projects, published research, or a strong public code portfolio demonstrating applied machine learning work.