Machine Learning Engineer (Remote)

Our client is a leader in healthcare innovation, seamlessly integrating pharmaceutical development, diagnostic solutions, and advanced technology and data capabilities.

Title: Machine Learning Engineer (Contract)
Pay rate: $55-73/hr+ (Depends on experience)
Location: Remote in the US or Canada, or onsite in SSF. Must be available during PST hours.
Duration: Through Dec. 2026 (Likely to get extended)

Overview:
Seeking a Machine Learning Bioinformatics Engineer to develop and deploy advanced ML solutions supporting pharmaceutical R&D. This role focuses on analyzing large-scale, multimodal clinicogenomic datasets (genomic, transcriptomic, clinical, and real-world data) to drive insights into disease biology, patient stratification, and treatment response. Ideal candidates are strong in both machine learning and bioinformatics, with a passion for translating complex data into impactful discoveries.
Key Responsibilities:

• Build and deploy scalable, production-ready machine learning models
• Process and analyze genomic and transcriptomic data using bioinformatics pipelines
• Prepare high-quality, normalized biological datasets for downstream analysis
• Train large-scale models using frameworks like PyTorch Lightning and Hugging Face
• Develop cloud-based ML solutions (AWS/Google Cloud Platform) with a focus on scalability and reproducibility
• Collaborate with cross-functional teams to uncover biomarkers and therapeutic targets
• Provide technical input and guidance on ML system design and implementation

Qualifications:

• PhD with 0-2 years of relevant work experience, or MS with 3-5 years of relevant work experience, or BS with 4-7 years of relevant work experience.
• Proficient programming skills: Strong Python programming skills with extensive experience in ML and data libraries (e.g., NumPy, pandas, PyTorch).
• Deep ML expertise: Excellent knowledge of modern machine learning methods and development best practices, including training strategies, model validation, performance visualization, and experimental design.
• Deep bioinformatic expertise: Proficient knowledge of bioinformatic processing pipelines for genomic and transcriptomic variables.
• Strong knowledge of computational oncology, cancer genomics and analysis of clinicogenomics datasets.

• Must be authorized to work in the United States

INDBH
#LI-MG1