Machine Learning Engineer

About the Role

About the Role: The Machine Learning Engineer, Contractor is a 3 to 6 months contract role focused on designing, developing, and deploying production quality deep learning models for large scale genomic and biomedical data. This role works closely with computational biology and data science teams to build robust machine learning pipelines that support next generation diagnostic and research applications. The position is ideal for an experienced ML engineer who thrives in applied research environments and can translate cutting edge methods into reliable production systems.

• Design, implement, and optimize deep learning models for image enhancement, classification, and analysis of genomic and biomedical data.

• Develop scalable machine learning solutions capable of operating on large, complex datasets in production environments.

• Build and maintain end to end ML pipelines including data preprocessing, model training, hyperparameter tuning, validation, and deployment.

• Implement model evaluation, monitoring, versioning, and reproducibility best practices.

• Write clean, well tested, and well documented Python code and reusable software packages.

• Translate ideas from technical research papers into working prototypes and production ready models.

• Collaborate closely with computational biology and cross functional teams to align technical solutions with scientific objectives.

• Communicate model performance, design decisions, and trade offs clearly to technical and non technical stakeholders.

• Masters degree with two or more years of relevant industry or research experience, or a PhD in Computer Science, Machine Learning, Computational Biology, Robotics, or a related quantitative field.

• Strong background in machine learning and deep learning, including hands on experience with CNNs, vision transformers, GANs, VAEs, and foundation models.

• Extensive experience training, tuning, validating, and optimizing deep learning models.

• Proficiency in Python and deep learning frameworks such as PyTorch, with Tensor Flow or JAX experience a plus.

• Proven ability to implement research concepts into production quality code.

• Strong software engineering practices including testing, code review, version control, and documentation.

• Experience working with cloud platforms such as AWS, GCP, or Azure.

• Excellent written and verbal communication skills.

• Authorized to work in the United States.

• Background or coursework in biology, genomics, or computational biology.

• Prior experience working with genomic, biomedical, or imaging datasets.

• Familiarity with MLOps tools, experiment tracking, model registries, and CI/CD workflows for machine learning.