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Own and improve Wispr’s developer experience systems, including CI/CD pipelines, reproducible testing environments, local development workflows, debugging tools, and agentic coding infrastructure. The role focuses on accelerating feedback, improving reliability, reducing engineering friction, and partnering with engineers to drive adoption and ongoing operational excellence across client and backend codebases.

Build and operate Wispr’s GTM infrastructure across company and people data, account signals, outbound and inbound workflows, lead routing, and AI agents. Develop production systems integrating Salesforce, ClickHouse, enrichment providers, and sales tools. Create monitored, evaluated, and reliable LLM workflows for research, meeting preparation, data hygiene, and deal processing. Partner with sales, marketing, and operations to improve pipeline generation, speed-to-lead, conversion, and adoption.

Design and build shared platform primitives for agentic AI systems, including memory, context management, tools, triggers, model coordination, and durable execution. Create infrastructure for model prototyping and evaluation, establish reference architectures, and evolve systems into reliable production platforms. Responsibilities include observability, recovery, security, latency and cost controls, cross-functional architectural alignment, and defining reusable abstractions adopted by multiple teams.

Develop and evaluate new AI and HCI product concepts through hypotheses, prototypes, experiments, and user testing. Build model harnesses, improve memory and context retrieval, and collaborate with ML researchers to connect product and model feedback. Determine which ideas should advance into products, document unsuccessful experiments, and own implementation and direction for successful product areas.

Lead the quality practice for an AI voice interface product by developing automated tests across desktop, mobile, backend services, and integrations. Establish continuous regression testing and CI/CD release gates, investigate real-world voice workflows and failures, prioritize risk-based testing, and provide release recommendations. Partner cross-functionally throughout development, improve coverage from incidents and defects, and coach QA teammates while remaining hands-on.

Lead Wispr Flow’s security assurance and GRC programs across SOC 2, ISO 27001, privacy, risk management, and enterprise customer requirements. Own customer security reviews, questionnaires, escalations, Trust Center content, audit evidence, controls, remediation, and compliance tooling. Partner with Engineering, Product, Legal, Sales, and Customer Success to address security blockers, improve automation, communicate AI security and privacy practices, and translate customer feedback into product requirements.

Build and lead foundational revenue operations across product-led and sales-led motions. Own forecasting, pipeline, lead routing, deal desk, quote-to-cash, GTM data modeling, funnel reporting, rep enablement, and customer journey processes. Develop AI-native agentic workflows using code, CRM, warehouse data, and modern GTM tools to automate enrichment, hygiene, research, and deal processing while driving measurable revenue outcomes.

Own and build complex detection, inference, state, and delivery systems across AI products. Instrument ambiguous quality problems, establish correctness without reliable ground truth, define meaningful quality metrics, and make tradeoffs across imperfect signals and user impact. Partner with product and platform teams to place systems appropriately and turn persistent quality issues into reusable, measurable solutions.

Owns product growth across onboarding, first dictation, and habit formation. The role ships rapid experiments, evaluates funnel metrics, improves experimentation reliability through randomization and instrumentation, and partners across teams to close product funnel gaps. Success requires data-driven decision-making, production-quality execution, and ownership of ambiguous growth problems.

Build verification and evaluation systems that allow AI agents to determine when work is complete. Develop testing environments, convert failures and human feedback into evaluation signals, improve agent context and memory delivery, and operate the agent fleet as a production system. The role spans application code, infrastructure, testing, measurement, and ambiguous problem-solving. Prior voice, model training, or product-domain experience is not required.