2026 Machine Learning Research Intern at Lambda

San Francisco, California, United States

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Not SpecifiedCompensation

InternshipExperience Level

InternshipJob Type

UnknownVisa

Artificial Intelligence, Machine LearningIndustries

Requirements

BS, MS, or Ph.D. student in Computer Science or related field, focusing on Machine Learning
Demonstrated project experience or publications in relevant areas
Proficient in PyTorch or similar frameworks
Strong communication and collaboration skills

Responsibilities

Conduct research in foundation models for language, vision, life sciences, and robotics (Track 1)
Perform multi-modal research, including building efficient data and evaluation toolkits (Track 1)
Publish findings in top-tier ML research conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV/ECCV, ACL, SIGGRAPH), and/or as technical blogs, public datasets, benchmarks, and open-source tools (Track 1)
Maximize training and inference performance for large-scale AI systems (Track 2)
Conduct systematic model and agent evaluation (Track 2)
Publish findings in top-tier ML systems conferences (e.g., MLSys, OSDI, SOSP, NSDI), and/or as technical blogs, public datasets, benchmarks, and open-source tools (Track 2)
Work on foundation models, multi-modal, agents, system benchmarking, and performance optimization

Skills

Key technologies and capabilities for this role

Machine LearningPyTorchFoundation ModelsMulti-ModalGenerative AISystem BenchmarkingPerformance OptimizationAgentsResearchPublications

Questions & Answers

Common questions about this position

What is the work arrangement for this internship?

This is a hybrid position requiring presence in the San Francisco office 4 days per week, with Tuesday designated as the work-from-home day.

What is the compensation for this Machine Learning Research Intern role?

This is an hourly role eligible for overtime, with the rate set based on market data and other factors; a higher or lower rate may apply depending on qualifications.

What skills are required for this internship?

Candidates must be a BS, MS, or Ph.D. student in Computer Science or related field focusing on Machine Learning, with demonstrated project experience or publications, proficiency in PyTorch or similar frameworks, and strong communication and collaboration skills.

What is Lambda's company culture like?

Lambda is a fast-growing company founded in 2012 with ~400 employees (2025), offering generous cash & equity compensation, and focused on building the world's best deep learning cloud with a mission to make compute ubiquitous.

What makes a strong candidate for this internship?

Strong candidates have demonstrated project experience or publications in ML, proficiency in PyTorch, and nice-to-haves like open-source contributions, experience with foundation models or multi-modal systems, dataset creation, or optimizing ML workloads.

Lambda

Cloud-based GPU services for AI training

About Lambda

Lambda Labs provides cloud-based services for artificial intelligence (AI) training and inference, focusing on large language models and generative AI. Their main product, the AI Developer Cloud, utilizes NVIDIA's GH200 Grace Hopper™ Superchip to deliver efficient and cost-effective GPU resources. Customers can access on-demand and reserved cloud GPUs, which are essential for processing large datasets quickly, with pricing starting at $1.99 per hour for NVIDIA H100 instances. Lambda Labs serves AI developers and companies needing extensive GPU deployments, offering competitive pricing and infrastructure ownership options through their Lambda Echelon service. Additionally, they provide Lambda Stack, a software solution that simplifies the installation and management of AI-related tools for over 50,000 machine learning teams. The goal of Lambda Labs is to support AI development by providing accessible and efficient cloud GPU services.

San Jose, CaliforniaHeadquarters

2012Year Founded

$372.6MTotal Funding

DEBTCompany Stage

AI & Machine LearningIndustries

201-500Employees

Risks

Nebius' holistic cloud platform challenges Lambda's market share in AI infrastructure.

AWS's 896-core instance may draw customers seeking high-performance cloud solutions.

Existential crisis in Hermes 3 model raises concerns about Lambda's AI model reliability.

Differentiation

Lambda offers cost-effective Inference API for AI model deployment without infrastructure maintenance.

Nvidia HGX H100 and Quantum-2 InfiniBand Clusters enhance Lambda's AI model training capabilities.

Lambda's Hermes 3 collaboration showcases advanced AI model development expertise.

Upsides

Inference API launch attracts enterprises seeking low-cost AI deployment solutions.

Nvidia HGX H100 clusters provide competitive edge in high-performance AI computing.

Strong AI cloud service growth indicates rising demand for Lambda's GPU offerings.

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