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Remote Machine Learning Jobs in California (NOW HIRING)

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

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 ...

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Remote Machine Learning information

See California salary details

$25.2K

$42K

$86.8K

How much do remote machine learning jobs pay per year?

As of May 30, 2026, the average yearly pay for remote machine learning in California is $42,026.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,100.00 and $45,400.00 per year, depending on experience, location, and employer.

What Are Remote Machine Learning Jobs?

Machine learning is a method of analyzing data via automating analytical model building. The premise is that systems can learn from data. Machine learning positions include machine learning engineer, computer vision engineer, and senior deep learning engineer. In a remote machine learning job, you work from home in a branch of artificial intelligence performing duties related to computational processing and data. Your goal is to design models that solve business problems, such as helping organizations avoid unknown risks or find profitable opportunities. Your responsibilities include maintaining data pipelines, performing model research and implementation, building machine learning systems, and onboarding new utilities.

What are the key skills and qualifications needed to thrive as a Remote Machine Learning Engineer, and why are they important?

To thrive as a Remote Machine Learning Engineer, you need a strong background in mathematics, statistics, programming (often Python), and experience with machine learning frameworks, typically supported by a relevant degree. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms (like AWS or GCP), and version control systems is crucial. Strong problem-solving abilities, self-management, and effective virtual communication distinguish top performers in remote settings. These competencies ensure the engineer can build effective models, collaborate across distributed teams, and deliver impactful solutions independently.

What are some effective strategies for collaborating with team members while working remotely as a Machine Learning Engineer?

Collaboration in a remote Machine Learning role often relies on clear communication through digital tools such as Slack, Zoom, and project management platforms like Jira or Asana. Regular check-ins and stand-up meetings help keep everyone aligned on project goals and timelines. Sharing code and models via version control systems (like Git) and using collaborative notebooks (such as JupyterHub or Google Colab) are also common practices. Building strong documentation habits and proactively seeking feedback can help ensure smooth teamwork and project success, even across different time zones.

What is a remote machine learning job?

A remote machine learning job involves working with algorithms, data, and models to develop predictive systems or automate tasks, all while working from a location outside of a traditional office setting. Professionals in this role use techniques from statistics and computer science to analyze data, train machine learning models, and deploy solutions for real-world applications. Remote machine learning jobs can span various industries, including technology, healthcare, finance, and e-commerce. These roles typically require strong programming skills, knowledge of machine learning frameworks, and the ability to communicate findings effectively with team members or stakeholders. Working remotely offers flexibility, but also requires discipline and self-motivation to succeed.

Is ML full of coding?

Machine Learning (ML) roles often involve significant coding, especially in programming languages like Python or R, to develop algorithms and models. However, some positions focus more on data analysis, feature engineering, or model evaluation, which may require less coding but still involve technical skills and understanding of ML concepts.

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

AspectRemote Machine LearningData Scientist
Required CredentialsBachelor's/Master's in CS, ML certificationsBachelor's/Master's in CS, Statistics, or related field
Work EnvironmentRemote, collaborative teams, tech companiesRemote or on-site, diverse industries, analytics focus
Industry UsageTech, AI startups, researchFinance, healthcare, e-commerce, tech
Search & Comparison IntentOften compared for technical roles in AI/MLBroader data analysis roles, but overlapping skills

Remote Machine Learning specialists focus on developing algorithms and models primarily in tech environments, often requiring advanced programming and ML knowledge. Data Scientists analyze data to extract insights, sometimes utilizing ML techniques. While both roles share skills and credentials, Remote Machine Learning emphasizes model development, whereas Data Scientists focus on data analysis and interpretation.

More about Remote Machine Learning jobs
What are the most commonly searched types of Machine Learning jobs in California? The most popular types of Machine Learning jobs in California are:
What job categories do people searching Remote Machine Learning jobs in California look for? The top searched job categories for Remote Machine Learning jobs in California are:
What cities in California are hiring for Remote Machine Learning jobs? Cities in California with the most Remote Machine Learning job openings:
Remote Machine Learning jobs near you
Infographic showing various Remote Machine Learning job openings in California as of May 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 100% Remote job distribution, with an average salary of $42,026 per year, or $20.2 per hour.
Machine Learning Engineer (Remote)

Machine Learning Engineer (Remote)

Astrix Inc

South San Francisco, CA • On-site, Remote

$55 - $73/hr

Full-time

Posted 15 hours 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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