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Machine Learning Research Intern Jobs (NOW HIRING)

Samaya AI

ML Research Intern

Mountain View, CA · On-site

Role We are seeking a highly motivated Machine Learning Research Intern to work on cutting-edge research in the fields of Natural Language Processing (NLP) and Machine Learning (ML). At Samaya, our ...

Labelbox

Applied Research Intern

San Francisco, CA · On-site

Role Overview As an Applied Research intern at Labelbox, you will design, build, and productionize ... A strong foundation in AI and machine learning, backed by a Ph.D. or Master's degree in Computer ...

Booz Allen Hamilton

Machine Learning Research Engineer

$209K/yr

Booz Allen Hamilton is seeking a Machine Learning Research Engineer to support the creation of physics-aware foundational models for remote sensing applications. The role involves training, testing ...

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$25.5K

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How much do machine learning research intern jobs pay per year?

As of Jun 6, 2026, the average yearly pay for machine learning research intern in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Machine Learning Research Intern, you need a strong foundation in mathematics, statistics, programming (especially Python), and an understanding of machine learning algorithms, typically supported by ongoing or completed studies in computer science or related fields. Familiarity with technical tools such as TensorFlow, PyTorch, scikit-learn, and experience with data analysis libraries are commonly required. Curiosity, problem-solving ability, and effective communication skills help interns stand out by enabling them to collaborate, share insights, and adapt to new research challenges. These skills ensure interns can contribute meaningfully to research projects, quickly learn new techniques, and effectively communicate their findings.

What are some typical challenges faced by Machine Learning Research Interns during their projects?

Machine Learning Research Interns often encounter challenges such as dealing with limited or messy datasets, tuning complex model architectures, and balancing innovative research with practical implementation. Additionally, they may need to quickly familiarize themselves with unfamiliar frameworks or tools and effectively communicate technical findings to both technical and non-technical team members. Successfully navigating these challenges can provide valuable learning experiences and help interns build strong problem-solving skills for future roles.

What does a Machine Learning Research Intern do?

A Machine Learning Research Intern assists in the development, implementation, and evaluation of machine learning models and algorithms under the supervision of experienced researchers. They often preprocess data, run experiments, analyze results, and contribute to research papers or technical reports. Interns also stay up to date with the latest advancements in machine learning, participate in team meetings, and sometimes help in coding or optimizing existing models. This role provides hands-on experience in applying theoretical knowledge to real-world problems and prepares interns for careers in AI research or development.
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Machine Learning Research Intern jobs near you
Research Intern - Reinforcement Learning (RL) - Onsite

Research Intern - Reinforcement Learning (RL) - Onsite

Level AI

San Francisco, CA • On-site

$17.75 - $23.50/hr

Internship

This job post has expired today. Applications are no longer accepted.

Job description

Research Intern (Reinforcement Learning)

Build the next generation of Agentic AI with us. Our platform combines conversation intelligence, multimodal understanding, and agentic AI systems to power both human agents and autonomous AI agents across the entire customer experience lifecycle.

A core part of this vision is our investment in custom Small Language Models (SLMs) —purpose-built for CX workflows—paired with reinforcement learning systems that continuously improve decision-making in real-world environments.

We're looking for a Research Intern (Reinforcement Learning) to join us in shaping this future.

What You'll Do

  • Design and build reinforcement learning environments that model real-world customer interaction workflows.
  • Design RL agents that learn from these environments using real-world interaction data, rewards, and feedback loops
  • Define reward models and feedback loops using real-world signals (outcomes and human feedback)
  • Enable learning from production data by structuring interaction traces into training-ready datasets for offline and online learning
  • Experiment with multi-agent systems and simulation frameworks for complex coordination and decision-making
  • Collaborate with engineering and product teams to deploy, evaluate, and iterate on learning systems in production at scale.

What We're Looking For

  • Currently pursuing (or recently completed) a degree in Computer Science, AI, Machine Learning, or related field
  • Strong understanding of reinforcement learning fundamentals
  • Familiarity with RL environments and training libraries such as Verl and Tinker
  • Strong foundation in probability, math, and optimization
  • Passion for building real-world AI systems

Nice To Have

  • Experience with RLHF, LLM/SLM fine-tuning, or model alignment
  • Exposure to agent-based systems or multi-agent RL
  • Prior research, projects, or publications in RL or applied ML
  • Experience working with large-scale or production datasets

Why Level AI

  • Work on production-grade Agentic AI systems used by leading enterprises
  • Build alongside a team with deep expertise from Amazon, Google, and Meta
  • Be part of a fast-growing Series C AI company
  • Direct exposure to 0→1 AI innovation in CX and decisioning systems

Apply