Llm Engineer Jobs
Llm Engineer jobs are open across AI research labs, enterprise software, fintech, and healthtech, from junior to staff and principal levels, with specializations in retrieval-augmented generation, fine-tuning, and prompt engineering. Find a role that fits from the openings below and apply directly.
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Minimum qualifications:
- Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field or equivalent practical experience.
- 5 years of experience in performance modeling, computer architecture, or hardware/software co-design.
- Experience with programming in C++ or Python.
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
- Deep understanding of distributed ML training methodologies (data, tensor, and pipeline parallelism).
About the job
Google Cloud’s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren’t just building technology; you’re shaping the frontier of enterprise and driving the evolution of advanced models.
The TPU Chip Architecture and Performance Co-design team is at the forefront of optimizing Google's custom AI silicon for next-generation machine learning models. As a Senior Performance Engineer, you will specialize in LLM training studies to shape the hardware and software architectures that will train the world's most capable AI models. You will analyze training workloads for first-party (1P) and third-party (3P) models to drive the next evolution of Google's custom ML accelerators.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Lead hardware/software co-design and performance modeling for LLM training workloads across current and future generation silicon.
- Analyze distributed training bottlenecks (compute, memory, networking) for massively scaled 1P and 3P models.
- Build and enhance modeling infrastructure, simulators, and performance tooling tailored to distributed ML training.
- Drive data-backed decisions that influence the roadmap for future TPU/Cloud Silicon architectures.
- Work closely with ML research and compiler teams to optimize training algorithms and influence future hardware design.
Minimum qualifications:
- Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field or equivalent practical experience.
- 5 years of experience in performance modeling, computer architecture, or hardware/software co-design.
- Experience with programming in C++ or Python.
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
- Deep understanding of distributed ML training methodologies (data, tensor, and pipeline parallelism).
About the job
Google Cloud’s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren’t just building technology; you’re shaping the frontier of enterprise and driving the evolution of advanced models.
The TPU Chip Architecture and Performance Co-design team is at the forefront of optimizing Google's custom AI silicon for next-generation machine learning models. As a Senior Performance Engineer, you will specialize in LLM training studies to shape the hardware and software architectures that will train the world's most capable AI models. You will analyze training workloads for first-party (1P) and third-party (3P) models to drive the next evolution of Google's custom ML accelerators.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Lead hardware/software co-design and performance modeling for LLM training workloads across current and future generation silicon.
- Analyze distributed training bottlenecks (compute, memory, networking) for massively scaled 1P and 3P models.
- Build and enhance modeling infrastructure, simulators, and performance tooling tailored to distributed ML training.
- Drive data-backed decisions that influence the roadmap for future TPU/Cloud Silicon architectures.
- Work closely with ML research and compiler teams to optimize training algorithms and influence future hardware design.
Llm Engineer Jobs by Experience Level
See All 76 Llm Engineer Jobs
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Find Llm Engineer JobsLlm Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software12
- Banking & Financial Services8
- Electronics & Hardware5
- Media & Entertainment3
- Automotive2
What Employers Look For
The qualifications that appear most often in llm engineer jobs.
- Proficiency in Python and experience with PyTorch or JAX for model development and fine-tuning
- Hands-on experience with large language models such as GPT, LLaMA, Mistral, or Gemini families
- Familiarity with retrieval-augmented generation pipelines and vector databases like Pinecone or Weaviate
- Experience deploying and optimizing models for production inference using vLLM, TensorRT, or similar tools
- Strong understanding of prompt engineering, context window management, and evaluation frameworks
- Bachelor's or master's degree in computer science, machine learning, or a closely related technical field
Tips for Your Llm Engineer Job Search
Tailor your resume to model infrastructure
Recruiters scanning llm engineer resumes look for specifics: which foundation models you've worked with, whether you've handled inference optimization, and what serving frameworks you've used. Generic 'AI experience' won't stand out. Name the models, the frameworks, and the production scale.
Show evaluation results, not just experiments
Your portfolio should include benchmark results or qualitative eval outputs, not just notebooks. Teams want evidence you can assess model behavior systematically. Even a side project with a documented eval harness signals maturity that most candidates skip.
Filter openings by stack, not just title
LLM engineer job titles vary wildly. Search for 'applied scientist,' 'AI engineer,' and 'foundation model engineer' in addition to the exact title. Then filter by the stack listed in the description to find roles that match your strengths in tools like LangChain, vLLM, or Hugging Face.
Apply early to roles that fit
Migrate Mate lists llm engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare for system-design questions on inference
Interviews for llm engineer roles frequently include a system-design round focused on latency, cost, and context management, not just model selection. Practice designing a retrieval-augmented pipeline end-to-end, including chunking strategy, vector store choice, and re-ranking logic.
Negotiate around compute access, not just salary
When you reach the offer stage, GPU access, cloud credits, and research time are negotiable at many AI teams. Ask specifically what compute budget the team works with and whether engineers have discretionary access. These resources affect what you can actually build.
Llm Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most llm engineers?
The companies hiring the most llm engineers right now include Capital One, Information Technology Senior Management Forum, and TikTok, with the largest share of openings in California, New York, and Virginia, based on current listings on Migrate Mate as of August 2026. Demand is concentrated at AI-native startups and large technology companies building internal generative AI products.
How many llm engineer jobs are remote?
About 59% of llm engineer openings are fully remote or hybrid as of August 2026, making it one of the more remote-accessible engineering disciplines. Roles focused on prompt engineering, evaluation, and RAG pipeline development tend to be the most remote-friendly, while positions involving on-premises GPU infrastructure or close cross-functional collaboration often require some on-site presence.
How do you become a llm engineer?
Start by building a strong foundation in Python and deep learning fundamentals, then work through the Hugging Face course and experiment with open-source models locally. Build at least one end-to-end project that includes fine-tuning or retrieval-augmented generation, document your evaluation process, and publish the code. Contributing to open-source LLM tooling and writing about your experiments publicly accelerates hiring significantly.
Can you get hired as a llm engineer without direct experience?
Yes, particularly at companies still building their AI teams from scratch. The most effective approach is demonstrating hands-on competence through a public portfolio: a fine-tuned model on Hugging Face, a documented RAG system on GitHub, or a technical write-up of an eval you designed. Hiring managers in this space weight demonstrated ability over credentials because the field is too new for long resumes to exist.
What does the llm engineer interview process look like?
Most processes include a recruiter screen, a technical phone interview covering Python and ML fundamentals, and a take-home or live coding round focused on a practical task like building a retrieval pipeline or evaluating model outputs. Final rounds typically include a system-design interview on inference architecture and a team-fit conversation. Some companies add a research presentation if the role involves novel modeling work.
Where can I find and apply to llm engineer jobs?
You can find and apply to llm engineer jobs on Migrate Mate, which lists current openings from employers across the United States. Search the listings to find roles that match your experience and stack, then apply directly to each one that fits. Openings are updated regularly, so checking back frequently gives you access to newly posted roles before they fill.
See All 76 Llm Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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