Jobs at Replicant

Own website growth, traffic quality, conversion optimization, attribution, and funnel performance for a B2B SaaS company. Manage Webflow, GA4, tracking, integrations, SEO, AEO, GEO, AI-powered personalization, interactive web tools, and continuous A/B testing. Partner with advertising, demand generation, product, sales, and RevOps teams to connect web activity to pipeline and revenue.

Leads corporate FP&A, forecasting, long-range modeling, capital strategy, fundraising, board and investor reporting, IPO readiness, and finance team development. Partners with Accounting on metrics and public-company reporting standards while supporting hiring plans, M&A, and scenario analysis. Requires extensive corporate finance experience, hands-on IPO or public-company experience, SaaS metrics expertise, advanced financial modeling, and executive-level communication.

Lead exploration and application of large language models and generative AI for enterprise contact-center automation. Translate research into high-performing, deployable ML systems; contribute across experimentation, implementation, and deployment; collaborate with engineering and product teams; optimize performance; and help shape team strategy, innovation, and professional growth.

Build and improve Replicant’s AI-native platform infrastructure, including site reliability, CI/CD, developer tooling, observability, incident management, and agent harness systems. Own production infrastructure patterns, deployment workflows, and developer self-service across Kubernetes and cloud environments. Participate in on-call and incident response while improving scalability, availability, and operational efficiency for real-time conversational AI traffic.

Design, build, and operate customer-specific AI agents end-to-end: map workflows, write prompts and conversation flows, integrate with customer APIs, create test sets, monitor live conversations, debug production issues, and iterate on agent quality with product feedback.

The Lead Quality Engineer will set quality standards, mentor a QA team, improve testing strategies, and enhance automation in AI-driven workflows.

Build full-stack features for AI voice and chat products using TypeScript, Node.js, React, and Python. Integrate ASR/TTS and LLM improvements, optimize production systems for latency and cost, participate in on-call rotations, and collaborate with ML and product teams to scale enterprise AI conversations.

Lead a Product Engineering squad (8–15 engineers), prioritize people development, hiring, process, and predictable delivery. Drive AI-native practices, technical credibility, customer-focused outcomes, testing/validation, and cross-functional partnerships while owning team budget and headcount.