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Internship Machine Learning Quant Jobs in Berkeley, CA

Adobe

Senior Machine Learning Engineer

San Francisco, CA · On-site

$172K - $306K/yr

... related quantitative field. Join us at Adobe and help compose the future of creative content ... machine learning. Together, we'll develop groundbreaking solutions that empower creatives around ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... • Strong background in machine learning engineering with a focus on model optimization ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... • Strong background in machine learning engineering with a focus on model optimization ...

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All JobsMachine Learning Quant JobsInternship Machine Learning Quant JobsInternship Machine Learning Quant Jobs in Berkeley, CA

Internship Machine Learning Quant information

See Berkeley, CA salary details

$31.2K

$52.1K

$107.8K

How much do internship machine learning quant jobs pay per year?

As of Aug 7, 2026, the average yearly pay for internship machine learning quant in Berkeley, CA is $52,141.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,800.00 and $56,300.00 per year, depending on experience, location, and employer.

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

AspectInternship Machine Learning QuantData Scientist Intern
Required CredentialsStrong programming skills, basic finance knowledge, coursework in machine learningStatistics, programming, domain knowledge, coursework in data analysis
Work EnvironmentFinancial firms, hedge funds, quantitative trading teamsTech companies, startups, research labs
Industry UsageFinance, trading, quantitative researchTechnology, marketing, healthcare analytics
Common Search IntentInternship roles in finance with machine learning focusInternship roles in data science across industries

Internship Machine Learning Quant roles typically focus on applying machine learning techniques to financial data within trading and investment firms. Data Scientist Intern positions are broader, spanning various industries like tech and healthcare, emphasizing data analysis and modeling. While both require programming and analytical skills, the finance-specific knowledge is more critical for Machine Learning Quant internships.

What job categories do people searching Internship Machine Learning Quant jobs in Berkeley, CA look for? The top searched job categories for Internship Machine Learning Quant jobs in Berkeley, CA are:
What cities near Berkeley, CA are hiring for Internship Machine Learning Quant jobs? Cities near Berkeley, CA with the most Internship Machine Learning Quant job openings:
Internship Machine Learning Quant jobs near you
Infographic showing various Internship Machine Learning Quant job openings in Berkeley, CA as of July 2026, with employment types broken down into 1% As Needed, 57% Full Time, 40% Part Time, 1% Temporary, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $52,141 per year, or $25.1 per hour.

Director Machine Learning, Drug Discovery Analytics

Revolution Medicines

Redwood City, CA • Hybrid

Other

Posted 3 days ago

New

Job description

The Opportunity:

We are seeking a Director Machine Learning to lead the development of advanced machine learning approaches that accelerate small-molecule drug discovery. This role sits at the intersection of data science, chemistry, and biology, transforming complex scientific datasets into predictive models that guide target discovery, compound design, and translational hypotheses.

Working closely with experimental scientists, the Director ML will develop cutting-edge modeling approaches that integrate chemical, biological, and phenotypic data with their team. The successful candidate will play a key role in advancing a data-driven discovery strategy by designing predictive models, deploying innovative algorithms, and translating insights into actionable decisions that improve the speed and success of the discovery of medicines for patients with RAS-driven cancers.

Key responsibilities include:

Scientific Leadership:

  • Provide hands-on scientific leadership in drug discovery analytics spanning Identify opportunities where AI and advanced analytics can meaningfully improve scientific decision-making

  • Managing, coaching and mentoring scientists across the function in order to develop their skills and build RevMed's organizational capabilities

  • Define and lead machine learning strategies that accelerate early-stage drug discovery.

Model Development:

  • Develop predictive models for:

    • Compound activity, selectivity, ADME/Tox, and developability properties

    • Target engagement, mechanism-of-action, and phenotypic datasets

Cross-Functional Collaboration:

  • Work with biologists to interpret complex experimental datasets and generate mechanistic hypotheses.

  • Collaborate with data scientists and engineers and ML engineers to deploy models into scalable discovery workflows.

Required Skills, Experience and Education:

  • PhD in machine learning, computational chemistry, computational biology, computer science, or a related quantitative discipline.

  • 8+ years experience applying machine learning or advanced analytics to scientific problems.

  • Demonstrated experience working with chemical or biological datasets in drug discovery or related domains.

  • Strong expertise in:

    • Python-based ML ecosystems (PyTorch, TensorFlow, scikit-learn)

    • Data analysis and scientific computing (NumPy, Pandas)

    • Deep learning and representation learning techniques

  • Evidence of successful coaching, mentorship and development of both individuals and teams in order to build long-term organizational capability

  • Passion for scientific innovation and a relentless commitment to improving patient outcomes.

Preferred Skills:

  • Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.

  • Experience with cheminformatics or bioinformatics toolkits is highly desirable.

  • Familiarity with cloud computing and scalable ML workflows is a plus

  • Ability to work at the interface of computational and experimental science. 

    #LI-Hybrid  #LI-LN1

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