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Internship Machine Learning Robotics Jobs (NOW HIRING)

FieldAI

3D Machine Learning Engineer

Irvine, CA

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field ... What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field ... What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ...

Brachium

Robotics Vision Engineer

San Ramon, CA · On-site

Robotics Vision Engineer Title Robotics Vision Engineer Company Description We are a 3-year-old ... Port, implement, and optimize analytics and machine learning algorithms using special purpose ...

Avride

Machine Learning Engineer Internship

Austin, TX · On-site

We develop and operate both autonomous cars and delivery robots that share technologies and ... Machine Learning / Math Foundation: Strong understanding of deep learning, reinforcement learning ...

DiDi Labs

Machine Learning Engineer, VLA

San Jose, CA · On-site

$129.19K - $247.04K/yr

Strong background in machine learning, deep learning, or robotics * Experience with PyTorch / JAX / TensorFlow * Solid understanding of modern neural architectures (transformers, diffusion, auto ...

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Internship Machine Learning Robotics information

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$25.5K

$42.6K

$88K

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

As of Jun 3, 2026, the average yearly pay for internship machine learning robotics 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 is the difference between Internship Machine Learning Robotics vs Internship Data Science?

AspectInternship Machine Learning RoboticsInternship Data Science
Required SkillsProgramming (Python, C++), Robotics, Machine LearningStatistics, Programming (Python, R), Data Analysis
Work EnvironmentRobotics labs, manufacturing, research facilitiesData centers, corporate offices, research institutions
Industry UsageRobotics companies, automation, AI hardwareFinance, healthcare, marketing, tech firms

Internship Machine Learning Robotics focuses on developing AI algorithms for robotic systems, combining hardware and software skills. In contrast, Internship Data Science emphasizes analyzing data to extract insights, often in business or research settings. Both internships require programming skills, but their applications and environments differ significantly.

What cities are hiring for Internship Machine Learning Robotics jobs? Cities with the most Internship Machine Learning Robotics job openings:
What are the most commonly searched types of Machine Learning Robotics jobs? The most popular types of Machine Learning Robotics jobs are:
What states have the most Internship Machine Learning Robotics jobs? States with the most job openings for Internship Machine Learning Robotics jobs include:
Internship Machine Learning Robotics jobs near you
Infographic showing various Internship Machine Learning Robotics job openings in the United States as of May 2026, with employment types broken down into 18% As Needed, 70% Full Time, 5% Part Time, 2% Temporary, 2% Contract, and 3% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Engineer, Foundation Model

DiDi Labs

San Jose, CA • On-site

$129.19K - $247.04K/yr

Other

Posted 19 days ago

Job description

About The Company

DiDi's autonomous driving unit was established in 2016 with the mission of developing Level 4 autonomous driving (AD) technology to make transportation safer and more efficient. In August 2019, the unit became an independent company, DiDi Autonomous Driving, dedicated to advanced AD R&D, product application, and business expansion. We believe integrating AD technology into a shared-mobility fleet will generate immense social value. By leveraging DiDi's specialized technology, operational expertise, and integrated ecosystem, we are positioned to build and operate a highly efficient, user-oriented autonomous fleet.

About The Role

The Foundation Model Team focuses on building large-scale foundation models for multi-agent behavior prediction and autonomous vehicle planning. By leveraging DiDi Voyager's unparalleled driving data, we train highly robust and generalizable deep learning systems that enable safe and intelligent autonomous driving at scale.

Our models serve as the core intelligence of the autonomous driving stack, enabling vehicles to understand complex traffic scenarios, anticipate agent behavior, and make safe and efficient driving decisions.

We operate at the intersection of large-scale machine learning, autonomous driving, and foundation model research, pushing the frontier of multi-agent prediction and planning.

Responsibilities

As a member of the Foundation Model Team, you will:

  • Design and train large-scale deep learning models for:

    • Multi-agent trajectory prediction

    • Behavior and intent prediction

    • Planning and decision-making

  • Build foundation model architectures (Transformers, Diffusion, Flow-based models, Decision models, VLM/VLA)

  • Develop scalable training pipelines across hundreds to thousands of GPUs

  • Work with massive real-world datasets and build high-quality data pipelines

  • Optimize models for latency, reliability, and on-vehicle deployment

  • Collaborate closely with perception, mapping, simulation, and systems teams

  • Drive research ideas into production systems used by real autonomous vehicles

Qualifications

  • Strong background in machine learning, deep learning, or robotics

  • Experience with PyTorch / JAX / TensorFlow

  • Solid understanding of modern neural architectures (transformers, diffusion, auto-regressive)

  • Strong coding skills in Python and C++

  • Passion for building real-world, safety-critical AI systems

Preferred Qualifications 

  • BS, MS or PhD in Computer Science, Machine Learning, Robotics, or a related field

  • Experience in autonomous driving, robotics, or embodied AI

  • Experience training large models on distributed GPU clusters

  • Experience with trajectory prediction, planning, or decision-making systems

  • Publications in top ML / robotics conferences (NeurIPS, ICML, ICLR, CVPR, RSS, CoRL, etc.)

The base salary range for this position is $129,189-$247,038 annually in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

I acknowledge that prior to submitting this application, I have read and accepted the Privacy Notice for California Residents which is available on https://v.didi.cn/AQnxlBa

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