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Machine Learning Intern Remote Jobs in Stanford, CA

DataRobot

Agentic AI Intern

San Francisco, CA · Remote

This is a fully remote position. The program runs approximately 6 months. WHAT YOU'LL DO: * Build ... Foundational understanding of machine learning concepts - supervised learning, model evaluation ...

DataRobot

Agentic AI Intern

San Francisco, CA · Remote

This is a fully remote position. The program runs approximately 6 months. WHAT YOU'LL DO: * Build ... Foundational understanding of machine learning concepts - supervised learning, model evaluation ...

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Machine Learning Intern Remote information

See Stanford, CA salary details

$30K

$50K

$103.4K

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

As of Aug 9, 2026, the average yearly pay for machine learning intern remote in Stanford, CA is $50,034.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,200.00 and $54,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a machine learning intern?

To thrive as a Machine Learning Intern (Remote), a solid understanding of programming (especially Python), statistics, and foundational machine learning concepts—often supported by coursework or a relevant degree—is essential. Familiarity with tools like TensorFlow, PyTorch, Jupyter Notebooks, and version control systems (e.g., Git) is typically required, along with experience using data analysis libraries. Strong problem-solving skills, initiative, and clear communication are valuable soft skills for collaborating virtually and adapting to remote work environments. These skills and qualities enable effective contribution to projects, smooth team communication, and successful learning in a dynamic, distributed setting.

What types of projects can I expect to work on as a machine learning intern?

As a remote Machine Learning Intern, you can typically expect to contribute to projects such as data preprocessing, building and evaluating machine learning models, and assisting with the deployment of models into production environments. You may also help with tasks like feature engineering, exploratory data analysis, and preparing technical documentation. Collaboration is usually done through virtual meetings and code repositories, and you'll often work closely with data scientists, engineers, and mentors who provide guidance and feedback. This hands-on experience helps you gain exposure to industry-standard tools and workflows, preparing you for more advanced roles in the future.

What does a machine learning intern do when working remotely?

A remote Machine Learning Intern typically assists with data collection, cleaning, and analysis, helps develop and test machine learning models, and collaborates with team members through virtual meetings and code repositories. They may also research new algorithms, document their work, and present findings to their supervisors. The role provides hands-on experience in applying machine learning concepts to real-world problems while working from a remote location.
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Machine Learning Intern Remote jobs near you

Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home

Ginas Tech Jobs

San Francisco, CA • Remote

Full-time

Medical, Dental, Vision, PTO

Re-posted 1 hour ago

Job description

Job Description

Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home

As a Principal Machine Learning Engineer, you are a deep technical authority responsible for designing and evolving the most critical ML systems in the company.  The Principal Machine Learning Engineer will operate across training, inference, evaluation, and infrastructure, solving the hardest architectural and performance problems.  While Technical Leads may own execution at the team level, you set the technical standard and shape how ML systems are built across the organization.  This is a hands-on, high-impact role focused on depth.  This position is 100% Remote.

Principal Machine Learning Engineer Responsibilities:

- Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment.

- Design reproducible, high-performance training pipelines across GPU infrastructure.

- Architect inference systems that balance latency, throughput, cost, and reliability at scale.

- Design and maintain data systems for high-quality synthetic and real-world training data.

- Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.

- Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.

- Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.

- Make pragmatic trade-offs and ship improvements quickly, learning from real usage.

- Work under real production constraints: latency, cost, reliability, and safety

Principal Machine Learning Engineer Outcomes:

- ML systems (training, inference, evaluation) are reliable, scalable, and meet defined performance targets.

- Models deployed to production achieve measurable quality improvements and meet user-impact goals.

- Production issues are proactively monitored, debugged, and resolved with clear root-cause analysis.

- Team and cross-functional collaborators benefit from clear guidance, best practices, and scalable ML solutions.

- Research-to-production cycles are efficient, safe, and continuously improve the product experience.

Qualifications

Principal Machine Learning Engineer Qualifications:

- Strong background in deep learning and transformer-based architectures.

- Artificial Intelligence (AI) experience required.

- Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.

- Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.

- Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).

- Strong software engineering fundamentals; you write robust, maintainable, production-grade systems.

- Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.

- Comfort owning ambiguous, zero-to-one ML systems end-to-end.

- A bias toward shipping, learning fast, and improving systems through iteration.

- Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.

- Contributions to open-source ML or systems libraries.

- Background in scientific computing, compilers, or GPU kernels.

- Experience with RLHF pipelines (PPO, DPO, ORPO).

- Experience training or deploying multimodal or diffusion models.

- Experience with large-scale data processing (Apache Arrow, Spark, Ray).

Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.

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Additional Information

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