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Remote Machine Learning Finance Jobs in California

Freenome

Senior Machine Learning Scientist

Brisbane, CA · On-site +1

$110.10K - $150.40K/yr

... remote. What you'll do: * Independently pursue cutting edge research in AI applied to biological ... You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial ...

Freenome

Staff Machine Learning Scientist

Brisbane, CA · On-site +1

$199.68K - $283.50K/yr

... remote. What you'll do: * Independently pursue cutting edge research in AI applied to biological ... You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial ...

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

To excel in Remote Machine Learning Finance, strong analytical skills, a solid background in statistics or mathematics, and experience with financial data are essential, often supported by a degree in computer science, finance, or a related field. Familiarity with programming languages like Python or R, experience with machine learning frameworks (such as TensorFlow or Scikit-learn), and knowledge of financial modeling tools are typically required. Excellent problem-solving, communication, and the ability to work independently are standout soft skills in this remote environment. These abilities are crucial for developing effective financial models, interpreting complex data, and collaborating with distributed teams to drive business value.

How do remote machine learning professionals in finance typically collaborate with cross-functional teams?

Remote machine learning professionals in finance often work closely with data analysts, financial experts, and software engineers to develop and deploy predictive models. Collaboration is typically facilitated through virtual meetings, shared documentation, and project management tools. Clear communication and regular check-ins are crucial for aligning goals and ensuring that machine learning solutions address real business needs. Many organizations also encourage participation in virtual workshops and code reviews to maintain a strong sense of teamwork despite the remote setting.

What is a Remote Machine Learning Finance job?

A Remote Machine Learning Finance job involves applying machine learning techniques and algorithms to financial data and problems, often from a remote location. Professionals in this field develop models to predict market trends, assess risks, automate trading, or detect fraud using large datasets. Remote roles allow employees to work from anywhere, collaborating with teams virtually and using cloud-based tools to analyze data. These positions typically require strong programming skills, knowledge of finance, and experience with machine learning frameworks.
What are the most commonly searched types of Machine Learning Finance jobs in California? The most popular types of Machine Learning Finance jobs in California are:
What are popular job titles related to Remote Machine Learning Finance jobs in California? For Remote Machine Learning Finance jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Machine Learning Finance jobs in California look for? The top searched job categories for Remote Machine Learning Finance jobs in California are:
What cities in California are hiring for Remote Machine Learning Finance jobs? Cities in California with the most Remote Machine Learning Finance job openings:
Remote Machine Learning Finance jobs near you
Machine Learning Engineer (Remote)

Machine Learning Engineer (Remote)

Astrix Inc

South San Francisco, CA • On-site, Remote

$55 - $73/hr

Full-time

Posted 29 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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