AI Engineer

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 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

• 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
• You think in evals first. Before writing a prompt, you ask 'how will we know if this is better?' and you build the scaffolding to answer that question rigorously
• You’re comfortable with ambiguity at the model layer. You know that Claude, GPT, and Gemini make different tradeoffs, and you’ve developed intuition for when to switch, fine‑tune, or route between them
• You write solid Python and have enough backend instincts to own a service end‑to‑end: API design, data pipelines, async processing, and observability
• You can read a patent claim and not immediately give up. You’re curious about domain knowledge and willing to become a genuine expert in IP reasoning over time
• You have a strong technical background building ML & AI pipelines
• You have great understanding of ML methods and statistics, including ML project lifecycle and associated challenges at each stage of development
• Experience deploying, monitoring and maintaining data science products in cloud environments such as AWS and GCP.
• Solid understanding of data transformations and analytics functions using tools/languages like Pandas, Sklearn, SQL, Spark, etc.
• Familiarity with database modeling, data warehousing principles and SQL

• Comprehensive health coverage – Medical, dental,…