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Remote Internship Machine Learning Engineer Jobs in California

Machine Learning Engineer

San Francisco, CA · On-site +1

$164K - $266K/yr

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build ... Employee divides their time between in-office and remote work. Access to an office location is ...

Lead Machine Learning Engineer

San Francisco, CA · On-site +1

$120K - $159K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Lead Machine Learning Engineer

San Jose, CA · On-site +1

$120K - $158K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

Sr. Lead Machine Learning Engineer

San Jose, CA · On-site +1

$120K - $158K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple ...

... machine learning models with minimal supervision, applyingsound statistical and engineering ... Ability to work effectively in a remote environment using collaboration tools Preferred Experience ...

... machine learning models with minimal supervision, applyingsound statistical and engineering ... Ability to work effectively in a remote environment using collaboration tools Preferred Experience ...

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Remote Internship Machine Learning Engineer information

What is the difference between Remote Internship Machine Learning Engineer vs Remote Data Scientist Intern?

AspectRemote Internship Machine Learning EngineerRemote Data Scientist Intern
Required CredentialsBasic programming, machine learning fundamentals, coursework or certificationsStatistics, data analysis, programming skills, coursework or certifications
Work EnvironmentCollaborative team, remote, project-basedRemote, data analysis projects, team collaboration
Industry UsageTech, AI, software developmentTech, finance, healthcare, research

Both roles are internship positions focused on data and machine learning skills, often requiring similar educational backgrounds. The main difference lies in their focus: Machine Learning Engineers concentrate on developing and deploying ML models, while Data Science Interns focus on analyzing data and deriving insights. Both are common in tech industries and often share similar work environments and prerequisites.

What are popular job titles related to Remote Internship Machine Learning Engineer jobs in California? For Remote Internship Machine Learning Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Internship Machine Learning Engineer jobs in California look for? The top searched job categories for Remote Internship Machine Learning Engineer jobs in California are:
What cities in California are hiring for Remote Internship Machine Learning Engineer jobs? Cities in California with the most Remote Internship Machine Learning Engineer job openings:

Machine Learning Engineer (Remote)

Astrix Inc

South San Francisco, CA • On-site, Remote

$55 - $73/hr

Full-time

Re-posted yesterday


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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