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Remote R D Technician Jobs in California (NOW HIRING)

Hitachi

Financial Planner & Analyst Manager

Santa Clara, CA · On-site +1

$114K - $143K/yr

Finance Job Schedule: Full time Remote: No Title: Financial Planner & Analyst Manager Company ... In this role, you will contribute to effectively managing the finances of R&D. You'll get a chance ...

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Remote R D Technician information

What is the difference between Remote R D Technician vs Remote Quality Control Technician?

AspectRemote R D TechnicianRemote Quality Control Technician
Required CredentialsAssociate's or Bachelor's in Science or EngineeringAssociate's or Bachelor's in Science or related field
Work EnvironmentLaboratory, research facilities, or remote testing setupsManufacturing sites, labs, or remote inspection roles
Industry UsageResearch and development departments in biotech, pharma, or techManufacturing, biotech, or food industries
Common Search/ComparisonYesYes

The main difference between a Remote R D Technician and a Remote Quality Control Technician lies in their focus areas. R D Technicians primarily support research and development activities, working on new product development and testing. Quality Control Technicians focus on ensuring products meet quality standards, often involved in inspection and testing processes. Both roles require similar educational backgrounds and may operate remotely, but their core responsibilities differ based on the product lifecycle stage.

What are the most commonly searched types of R D Technician jobs in California? The most popular types of R D Technician jobs in California are:
What cities in California are hiring for Remote R D Technician jobs? Cities in California with the most Remote R D Technician job openings:
Remote R D Technician jobs near you

Machine Learning Engineer (Remote)

Astrix Inc

South San Francisco, CA • On-site, Remote

$55 - $73/hr

Full-time

Re-posted 12 days ago

Job description

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

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