• System Ownership: Take ownership of the core dialog & policy engine. Define and implement the state machine for agent state representation, the decision-making logic, and the mechanisms for enforcing complex safety policies and guardrails at the execution layer of a workflow
• Distributed Context & Memory: Design, implement, and maintain the high-performance context and memory systems. Focus on low-latency, reliable access to conversational and user history, including the tight integration and optimization of RAG and vector retrieval pipelines for production use
• Agentic Orchestration Frameworks: Define, architect, and deliver robust agentic orchestration patterns, including battle-tested planner–executor schemes, ReAct-style reasoning and acting loops, and resilient, multi-step workflows that programmatically combine tools, LLMs, and stateful memory
• Internal SDK/Framework Development: Build and evolve the internal, production-grade equivalent of frameworks like LangChain/LlamaIndex. Design composable graphs and execution chains with clear APIs and type safety that product engineering teams and low-code builders can safely reuse, extend, and deploy at scale
• Voice Runtime Infrastructure: Own and optimize the voice runtime components for streaming audio, low-latency barge-in detection, and reliable turn-taking protocols. This requires deep collaboration with Application and ML Platform teams to meet tight latency, jitter, and quality of service (QoS) constraints
• Tooling & Integration Architecture: Architect a robust, secure tooling and integration framework (MCP/A2A). This includes building the underlying infrastructure for tool registration, handling complex authentication/authorization, implementing rate limiting/circuit breaking, managing retries, and ensuring typed, validated I/O between agents and external microservices
• Platform Observability & Reliability: Define, instrument, and monitor rigorous SLIs/SLOs for the Agent Platform. Lead engineering efforts to continuously improve reliability, enhance system debuggability (rich, step-level traces and structured logging), and drive core performance optimizations over time
• API & Abstraction Design: Ensure the platform's public-facing APIs and internal abstractions are clear, well-documented, and fundamentally sound, enabling junior and senior engineers alike to compose sophisticated agent behavior without introducing systemic invariants or breaking changes
• Advanced Capabilities R&D: Explore and prototype future capabilities, focusing on the engineering challenges of on-device personalization, implementing privacy-preserving federated learning signals, or integrating novel policy adaptation techniques that influence agent behavior in production

Qualifications

• Deep Experience: 3+ years of hands-on experience in backend engineering and distributed systems, with a track record of building and owning core platforms or frameworks used successfully by other engineering teams
• Agentic Systems Expertise: Demonstrated, hands-on experience architecting, building, or operating production-grade agentic systems: orchestrating LLM calls, managing complex tool interactions, and defining stateful workflows—moving beyond simple single prompt/response API integrations
• Orchestration & Design Patterns: Strong working knowledge of engineering orchestration frameworks (e.g., LangChain, LlamaIndex, or internal equivalents) and a deep understanding of core design patterns like RAG, ReAct, and multi-step planning
• Systems Engineering Mastery: Deep and practical understanding of distributed system design, concurrent programming, and building for reliability in multi-tenant cloud environments with strictly defined latency and cost envelopes
• Framework Evangelism: Proven experience designing, implementing, and rolling out successful frameworks or libraries that other internal engineering teams enthusiastically adopt and productively build upon
• Security Focus: Comfort and prior experience working on security-sensitive systems, including implementing authz/authn schemes, isolation boundaries, data protection protocols, and integrating with centralized policy/safety infrastructure
• Technical Leadership: Strong technical communication skills and the ability to lead complex, cross-functional technical initiatives, driving consensus and influencing architectural decisions across partner teams

Bonus point

• Experience developing and operating conversational AI platforms, agent frameworks, or high-throughput, complex workflow engines in a production setting
• Engineering background in real-time media (audio/video) systems or low-level signaling protocols where extreme low-latency and jitter management are critical performance factors
• Prior experience building high-stakes enterprise platforms (e.g., payments, identity, core data services) where correctness, auditability, and absolute reliability are non-negotiable requirements
• Exposure to emerging systems and engineering techniques, such as integrating federated learning models, enabling on-device personalization, or implementing bandit-style adaptive policy systems

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Total compensations includes base pay, equity, and benefits. We have a 401k plan, HSA, FSA, free food.