> Markdown version of [/jobs/ext/2504538-artificial-intelligence-engineer](https://www.wearedevelopers.com/jobs/ext/2504538-artificial-intelligence-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence Engineer - **Company:** Innovaccer, Inc. - **Location:** San Francisco, CA, United States - **Contract:** Internship / Graduate position - **Skills:** Artificial Intelligence, Software Applications, Python (Programming Language), Machine Learning, Open Source Technology, Pytorch, Large Language Models, Information Technology, HuggingFace - **Published:** August 9, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=1dffe65ffab26fc9 ## About the Role * MS or PhD in Computer Science, Machine Learning, or a related quantitative field. Exceptional BS candidates with substantial research or open-source work will be considered. * Depth beyond coursework: first-author publications at NeurIPS, ICML, ICLR, ACL, EMNLP, or similar; meaningful open-source ML contributions; a research internship at an AI lab; or models you trained and shipped that people actually used. * You have fine-tuned an open-weight model yourself, understand the difference between parameter-efficient and full fine-tuning, and can explain why you chose one. * Strong Python and PyTorch. Familiarity with the current training and serving stack (HuggingFace, FSDP or DeepSpeed, vLLM or SGLang, or equivalents). * Some exposure to multi-GPU training, even at lab scale. You should know what a sharding strategy is and why it matters. * Evidence you can finish things. ## Description We are looking for an AI Engineer to join our growing AI team and help build intelligent, production-grade AI systems that solve complex problems at scale. In this role, you will work closely with product, engineering, and data teams to design, develop, and deploy AI-powered applications, including LLM-based solutions, AI agents, retrieval-augmented generation (RAG), and intelligent automation workflows. The ideal candidate is hands-on, highly curious, and comfortable working across the full AI development lifecycle-from experimentation and prototyping to production deployment and optimization. A Day in the Life * You take an idea from paper to prototype to production. If you have only ever done one of those three, this role will stretch you, and we are fine with that if the rest is strong. * You can build the model layer of a real product, not just a model. That means choosing model sizes, composing several models into a working system, and holding a product-level accuracy bar. * You write real code. Python fluently, PyTorch fluently, and enough systems sense to know why your training run is slow. * You design experiments. You state the hypothesis, run the ablation, and report the result that disagrees with you. * You measure things. You are suspicious of results that look good, and you build the eval before you build the model. * You read current research and can tell the difference between a technique that will hold up and one that will not. * You explain your work to people who are not AI engineers, including clinicians and operators who will tell you when your output is wrong. ## Related Videos - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [How to start an AI project for a good cause and boost your career](https://www.wearedevelopers.com/magazine/15-how-to-start-an-ai-project-for-a-good-cause-and-boost-your-career) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)