About the role
About Resolve AI Software maintenance and production troubleshooting have become a massive tax of engineering velocity. Resolve AI is solving this by building a transformative, truly autonomous AI Production Engineer that investigates and fixes complex system issues end-to-end. Our founders ( Spiros Xanthos and Mayank Agarwal ) are the core creators of OpenTelemetry and led Splunk Observability. They have had 2 successful exits to Splunk and VMware. We’ve raised over $150M from top-tier investors including Lightspeed, Greylock, Unusual Ventures, and individual backers such as Jeff Dean (Chief Scientist, Google DeepMind), Thomas Dohmke (CEO, GitHub), Matt Garman (CEO, AWS), Reid Hoffman (Founder, LinkedIn), and Fei-Fei Li (Professor, Stanford). Joining Resolve AI at this stage of our journey is a once-in-a-lifetime opportunity. You’ve already decided that you want to work at an AI-native company that’s pushing the limits of how engineers work, and now you’re looking for the right one. What You’ll Do Develop AI-powered workflows end-to-end, balancing research and engineering to create production-ready AI models Build and optimize data pipelines to process high-volume structured and unstructured data for training and evaluation Design and execute benchmarks to evaluate AI models, improve performance, and reduce compute costs Fine-tune and train Large Language Models (LLMs) for production engineering and reliability use cases Research and experiment with novel techniques, including reinforcement learning, retrieval-augmented generation, and autonomous reasoning Work in a collaborative, high-impact team, translating research into scalable AI-powered solutions What We’re Looking For Deep experience with LLMs: Experience working with foundation models (e.g., GPT-4, Llama, Gemini), including fine-tuning, prompt engineering, and optimization Strong software engineering skills: Proficiency in Python, Go, or C++ with experience in distributed systems and cloud infrastructure Data engineering expertise: Experience working with large-scale data pipelines, embeddings, vector databases, or search Machine learning fundamentals: Understanding of transformers, deep learning, and self-supervised learning