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Entry Level Machine Learning Researcher Jobs (NOW HIRING)

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Entry Level Machine Learning Researcher information

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$30K

$113.1K

$164.5K

How much do entry level machine learning researcher jobs pay per year?

As of Aug 20, 2026, the average yearly pay for entry level machine learning researcher in the United States is $113,102.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,000.00 and $154,000.00 per year, depending on experience, location, and employer.

What is the difference between Entry Level Machine Learning Researcher vs Data Scientist?

AspectEntry Level Machine Learning ResearcherData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some research experienceBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentResearch labs, academic institutions, R&D departmentsBusiness analytics teams, tech companies, consulting firms
Employer & Industry UsageResearch-focused roles in academia, tech, or research institutionsData-driven decision making in various industries like finance, healthcare, tech

While both roles involve working with data and algorithms, an Entry Level Machine Learning Researcher primarily focuses on developing new algorithms and advancing machine learning techniques in research settings. In contrast, a Data Scientist applies existing models to solve business problems, interpret data insights, and support decision-making across industries.

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What are the most commonly searched types of Machine Learning Researcher jobs?

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Infographic showing various Entry Level Machine Learning Researcher job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $113,102 per year, or $54.4 per hour.

Machine Learning Researcher

Fluency Digital, Inc.

San Francisco, CA • On-site

$100 - $130/hr

Other

Posted 2 days ago

New


Job description

Early-stage AI lab building intelligence that learns causality through physics.

You are a researcher who believes true intelligence emerges from understanding physical laws rather than statistical patterns in language. Your days will be spent architecting novel PyTorch models that infer causal structures from raw data, moving beyond correlation to grasp the mechanics of how the world works. We offer the autonomy to pursue deep, fundamental questions in a focused, on-site environment in San Francisco where every line of code advances the frontier of causal reasoning.

What we're looking for:
  • 1+ years of hands-on experience building and training models in PyTorch
  • Strong foundation in Python and scientific computing libraries
  • Deep interest in causal inference, physics-based modeling, or representation learning
  • Proven ability to translate complex research concepts into robust engineering implementations
  • Preference for deep focus and collaborative problem-solving over corporate process
  • Must be based in the United States.
Tech stack:

Python, PyTorch

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