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HRL Laboratories

LLM & Agentic AI Research Intern

Posted 2 Days Ago
Be an Early Applicant
Hybrid
Calabasas, CA
43-48 Hourly
Internship
Hybrid
Calabasas, CA
43-48 Hourly
Internship
Conduct research on LLMs and agentic AI systems, design prototypes, develop generation pipelines, and evaluate frameworks in a research-driven environment.
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Join Us for an Unforgettable Summer Internship Experience!
 
Looking to grow your skills, build your network, and have a little fun along the way? Our Summer Internship Program offers the perfect mix of hands-on experience and community-building activities designed to support both your personal and professional development.
 
Over the course of the summer, you’ll dive into meaningful work that will expand your knowledge and challenge you to think creatively. But it’s not all work—we believe in balance.
 
You’ll also get the chance to:
 
Connect with fellow interns and early-career professionals through engaging networking events
Unwind with movie nights, beach days, and celebrate National Intern Day
Explore our cutting-edge science during two half days of guided laboratory tours, where you’ll visit 8 of our top research facilities and see innovation in action
Whether you’re looking to kick-start your career or simply learn more about the exciting work we do, this is more than just an internship—it’s your first step into a vibrant, supportive, and forward-thinking community.

HRL Laboratories pioneers the next frontiers of physical and information science. Delivering transformative technologies in automotive, aerospace and defense, HRL advances the critical missions of its customers to help them remove limitations and create competitive advantage. HRL then transitions the work back to customers – ready for real-world application. For more than 70 years, HRL’s rich portfolio of scientific discoveries and engineering innovations continues to build on each other — often in unexpected, profound and far-reaching ways. As a private company owned jointly by Boeing and GM, HRL prioritizes purpose over profit, significantly advancing the state of the art.
HRL Laboratories develops robust intelligent systems that deliver adaptable, autonomous performance improvement solutions for complex missions. Our teams advance human-machine synergy, operationalized machine learning models and complex systems analytics and agents to create scalable, secure technologies. We design novel algorithms and mission-ready solutions that strengthen decision making for autonomous and human-guided systems across national security and commercial applications.

Essential Duties:
• Conduct advanced research in LLMs, Generative AI, and agentic AI systems, staying current with rapidly evolving techniques such as tool-augmented reasoning, planning agents, and multi-agent collaboration.
• Design and prototype LLM-based agents capable of reasoning, planning, tool invocation, memory management, and autonomous task execution.
• Develop and optimize retrieval-augmented and tool-augmented generation pipelines (RAG, Graph-RAG, hybrid search, vector & symbolic retrieval).
• Build and evaluate end-to-end agent workflows, including orchestration, tool APIs, state tracking, and long-horizon planning.
• Implement robust evaluation pipelines for LLMs and agents (offline evals, LLM-as-judge, human-in-the-loop evaluation, red-teaming, and safety validation).
• Experiment with open-source and frontier foundation models (e.g., Llama, Mistral, Claude, Gemini, GPT-class models) and efficient adaptation techniques.
• Contribute to scalable production systems, including model serving, orchestration frameworks, observability, guardrails, and lifecycle management.
• Participate in internal research reviews, technical deep dives, and code walkthroughs.

Required Qualifications:
• Currently pursuing M.S. or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Computational Linguistics, Applied Mathematics, or a related technical field.
• Strong foundation in machine learning and NLP, including transformers, attention mechanisms, and modern generative modeling.
• Proficiency in Python and modern ML frameworks such as PyTorch, JAX, or TensorFlow.
• Hands-on experience with LLM tooling and frameworks, such as: HuggingFace, LangChain, LlamaIndex, Haystack, or similar orchestration frameworks
• Familiarity with agentic AI patterns, including Tool use / function calling, Planning & reasoning (ReAct, Tree/Graph of Thoughts, etc.), Memory architectures (short-term, long-term, vector DBs)
• Experience with RAG pipelines, embeddings, and vector databases.
• Understanding of prompting strategies, fine-tuning, and alignment techniques (SFT, RLHF/DPO, PEFT/LoRA).
• Strong software engineering practices, including version control, testing, and documentation.
• Ability to communicate technical concepts clearly and collaborate in a research-engineering environment.

Preferred Qualifications:
• Experience building autonomous or semi-autonomous agent systems in real applications.
• Familiarity with multi-agent systems, coordination, and distributed reasoning.
• Knowledge of evaluation and benchmarking frameworks for LLMs and agents
• Experience with model optimization techniques: Quantization, distillation, speculative decoding, KV-cache optimization
• Familiarity with deployment stacks: Docker/Kubernetes, model serving, GPU/accelerator pipelines
• Exposure to knowledge graphs, symbolic reasoning, or hybrid neuro-symbolic AI systems.
• Contributions to open-source AI/ML projects or publications (ACL, NeurIPS, ICML, ICLR, etc.).

Special Requirements:
US Citizenship with the ability to obtain and maintain a US Government Security Clearance
• Must be enrolled in an educational program following the end of your intern assignment

Compensation and Benefits:
Pay Range: $43 - $48 an hour
The pay will be determined by your educational year.  ·        The pay will be determined on your educational year. The range displayed on each job posting reflects the minimum and maximum target for new hires for the position. Your recruiter can share more about the specific range during the hiring process.
 Benefits: 80 hours of Sick time

Assignment Length:
·        HRL Summer internship assignments typically span 8 to 12 weeks, with three available cohort start dates: May 26, June 1 and June 15.
 
Stipend: 
·        For students who will be relocating and renting in the Malibu, CA area for the summer we offer a 1 time stipend to help with moving costs. Specific information on pay out and requirements to be discussed with your recruiter. 

Non-Discrimination and Equal Employment Opportunities (U.S.)
Don’t meet every single requirement? Studies have shown that some people are less likely to apply to jobs unless they meet every single desired qualification. At HRL, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
We are proud to be an EEO/AA employer M/F/D/V. We maintain a drug-free workplace and perform pre-employment substance abuse testing. 

If you would like more information about Equal Employment Opportunity as an applicant under the law, please go to Employees & Job Applicants | U.S. Equal Employment Opportunity Commission
For our privacy policy please visit: www.hrl.com/privacy

 
 

Top Skills

Docker
Haystack
Huggingface
Jax
Kubernetes
Langchain
Llamaindex
Python
PyTorch
TensorFlow
HQ

HRL Laboratories Malibu, California, USA Office

We are nestled on the Malibu mountainside overlooking the Pacific!

HRL Laboratories Calabasas, California, USA Office

26800 Agoura Rd, Calabasas, CA, United States, 91301

HRL Laboratories Camarillo, California, USA Office

741 Calle Plano, Camarillo, CA, United States, 93012

HRL Laboratories Santa Monica, California, USA Office

1545 26th Street, Santa Monica, Ca, United States, 90404

HRL Laboratories Thousand Oaks, California, USA Office

3500 Willow Lane, Thousand Oaks, Ca, United States, 91361

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