TECHNICAL PRODUCT LEAD

TECHNICAL PRODUCT LEAD Remote Full-time
Patent Domain Requirements
What you must know cold
Claim structure
Claim charts
Prior art
Office actions
Invalidity contentions
Portfolio strategy
Patent systems
Claim structure: independent vs dependent claims, claim scope, means-plus-function limitations, preamble interpretation, claim differentiation doctrine
Claim charts: limitation-by-limitation mapping for infringement and invalidity; understanding of reads on analysis; EOUs, IOUs, and claim construction approaches (plain meaning vs PHOSITA vs prosecution history)
Prior art: 102 anticipation analysis, 103 obviousness (motivation to combine, PHOSITA level, secondary considerations), temporal relevance relative to priority date
Office actions: reading and categorizing examiner rejections ( 101, 102, 103, 112); claim amendment strategy; prosecution history and its estoppel implications; file wrapper analysis
Invalidity contentions: structure and strategy of invalidity contentions in district court litigation; anticipation vs obviousness charts; motivation to combine arguments; 112 written description and enablement
Portfolio strategy: forward/backward citation analysis; continuation strategy; patent family grouping; FTO analysis methodology; portfolio pruning criteria
Patent systems: USPTO PAIR/Patent Center; EPO OPS; WIPO PCT; CPC and IPC classification systems; Derwent/Orbit for prior art search; global filing procedures
Credentials that signal domain depth
USPTO registered patent agent (registration number required) OR JD from accredited law school with substantial patent prosecution or litigation experience
Minimum 3 years hands-on experience creating claim charts, invalidity contentions, freedom-to- operate analyses, or office action responses in a professional context
Demonstrable familiarity with at least one patent analytics platform (Patlytics, Derwent Innovation, Orbit Intelligence, PatSnap, Google Patents Advanced)
Technical undergraduate degree preferred (engineering, chemistry, computer science, biology) the ability to read a technical patent in semiconductors, software, biotech, or mechanical engineering is required
Experience working directly with patent attorneys as a client, colleague, or counterpart understanding of how Am Law firms and in-house IP teams work is essential
AI & LLM Requirements
What you must know cold
LLM architecture: context windows, token limits, system/user/assistant patterns, temperature, sampling and how each affects patent analysis output quality and consistency
Prompt engineering: chain-of-thought prompting, few-shot examples with patent claim examples, structured JSON output schemas, negative constraints to prevent hallucination in legal contexts
Tool use / function calling: designing tool definitions for patent database queries, prior art retrieval, citation lookup, USPTO PAIR API calls the core of agentic patent workflows
Multi-step agents: LangGraph or equivalent for stateful pipelines where prior art search feeds into claim chart generation feeds into citation verification you can specify this architecture, not just describe it vaguely
Evaluation: LLM-as-judge patterns, golden dataset design, eval metrics for legal accuracy (citation correctness, claim scope fidelity, argument soundness), Braintrust or equivalent eval tooling
Hallucination in legal contexts: citation fabrication, claim scope inflation, temporal relevance errors in prior art, jurisdiction-specific legal standard drift and how to mitigate each in prompt design
Cost and latency: Anthropic prompt caching, token attribution per workflow, batching strategies for high-volume claim charting you understand the economics of LLM features at enterprise scale
AI tools you will use daily
Anthropic Claude API primary LLM for all patent workflows; you understand model strengths, rate limits, caching, and the difference between Claude models
LangSmith for reviewing eval run results, tracking prompt quality over time, and making go/no-go decisions based on eval data
LangGraph or equivalent for specifying multi-step agent architectures to the engineering team (you spec, they implement)
GitHub, JIRA and other productivity tools async-first delivery management, spec writing, stakeholder communication
Product Leadership Requirements
5+ years in B2B SaaS product management, with at least 2 years at a company competing in a domain with significant legal or regulatory accuracy requirements
Proven track record shipping AI-powered features to enterprise legal or IP clients (Am Law firms, Fortune 500 in-house teams, or equivalent)
Experience running a product without a Scrum Master or dedicated QA team async-first, output- oriented delivery with direct engineering accountability
Demonstrated ability to write LLM workflow specifications precise enough for engineers to implement without clarification sessions
Strong enterprise client relationship skills: you can demo a half-built product, field hard questions from a or IP litigation partner, and translate that feedback into a specific prompt change
Competitive analysis fluency: you actively use generally known products in this space regularly you know where their claim charts fail and where their UX creates friction
Ability to make technical tradeoff decisions alongside engineering: latency vs accuracy, prompt caching vs freshness, context window vs cost without deferring to engineering to decide for you
Track record of prioritizing with extreme discipline in resource-constrained environments you understand that 10 engineers competing with a 100-person org means every feature must be 10the value, not 10 the scope