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Machine Learning Engineer (Remote)Astrix Inc • United States
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Machine Learning Engineer (Remote)

Astrix Inc
  • US
    United States
  • US
    United States
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À propos

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/GCP) 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
  • United States

Compétences linguistiques

  • English
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