About Patlytics

Patlytics is the fastest-growing AI-native patent intelligence platform, transforming how IP is protected and monetized at scale. Our advanced LLMs and generative AI engine—custom-built for intellectual property—power every phase of the patent workflow from invention disclosure through litigation, delivering citation-backed insights with unprecedented accuracy and speed.

Fresh off our $40M Series B — led by SignalFire, with participation from N47, Myriad Venture Partners, Liquid 2 Ventures, BAM Corner Point, Alumni Ventures, Antiportfolio Ventures and Relativity- allows us to triple down on the product, accelerate growth globally, hire future mission-aligned colleagues, and to continue redefining what is possible for our customers. — we're scaling fast across product, engineering, and go-to-market. If you want to build at the inflection point, this is it.

With teams across the globe we're building a global company fueled by exceptional talent and bold ideas. Our rapid expansion is driven by people who bring entrepreneurial energy, intellectual curiosity, and a shared commitment to setting the global standard for AI-powered IP intelligence. We're creating a culture where diverse perspectives shape breakthrough innovation and every voice contributes to transforming how the world protects ideas.

We hire for trajectory, not just credentials. If you're 70% of this list and hungry to close the gap, apply.

As an AI Engineer, you’ll

Develop and deploy robust, scalable AI/ML algorithms for cutting-edge IP applications

Design, implement, and iterate on multi-step LLM pipelines for patent claim analysis, infringement detection, and prior art search, including the model selection, prompt architecture, and structured output contracts that define product quality

Build and maintain a rigorous evals framework: define success metrics per pipeline stage, curate golden datasets from real IP cases, and run continuous regression testing to catch degradation before users do

Develop retrieval systems (vector search, BM25 hybrids, re-ranking) optimized for patent corpus characteristics, long documents, technical claim language, biological sequence identifiers

Architect and ship agentic workflows within our Agent layer, coordinating tool use, memory, and multi-turn reasoning over complex IP research tasks

Develop data classification techniques and post-training on the latest LLMs

Collaborate with patent attorneys and domain experts to encode expert judgment into prompts, rubrics, and fine-tuning datasets, then measure whether it worked Own cost, latency, and quality tradeoffs for production inference: prompt caching strategies, context window management, batching, and model migration planning on AWS, GCP and other LLM providers.

Partner with a highly talented cross functional group of researchers, applied scientists, engineers, and product managers to build and evolve the (your company’s platform, product, solution, etc.)

Work with large, complex data sets, solve difficult, non-routine analysis problems, and apply advanced analytical methods as needed

Partner with the Leadership Team to align development strategies with key product requirements and long-term technical roadmap

Build tools to monitor data pipeline performance, data quality and models in production

Perform unit testing, profiling, and parameter tuning

Collaborate with data engineers & platform team to implement data pipelines and robust production real-time and batch decisioning solutions

Lead ongoing R&D of new technologies, data sources and data science & optimization tools

Improve existing machine learning methodologies by developing new sources and testing enhancements, running computational experiments, and fine-tuning parameters

That said, these responsibilities are just the beginning! As we continue to grow, we encourage you to contribute wherever you observe opportunities in the business that align with your interests.

About you

You've shipped LLM-powered features that real users depend on, not just demos or side projects. You have a track record of turning prototype prompts into production pipelines with monitoring and fallbacks