---
title: 'Member of Technical Staff - Agent Platform (Agent OS) at Boson AI'
canonical: 'https://feeny.ai/job/member-of-technical-staff-agent-platform-agent-os-boson-ai-santa-clara-95vzvadf5n6c'
type: 'job'
last_seen: '2026-09-11'
---

# Member of Technical Staff - Agent Platform (Agent OS) at Boson AI

- **Company:** Boson AI
- **Location:** Santa Clara, CA
- **Compensation:** $150k–$400k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-06-06
- **Last confirmed live:** 2026-09-11
- **Apply:** https://jobs.lever.co/bosonai/35858631-de70-4ddf-b310-ea9417af3b29

## Job description

About Boson AI: At Boson AI, we are not just building AI solutions; we are pioneering the future of enterprise AI. Driven by a passion for cutting-edge AI research, particularly in the transformative areas of large language models and agentic systems, our mission is to tackle the most complex real-world problems for businesses and unlock significant value. We are a dynamic and collaborative team of researchers and engineers who thrive on pushing the boundaries of what's possible, dedicated to delivering high-quality, reliable products that seamlessly integrate into the fabric of enterprise workflows and set new industry standards.

About the Role: Engineer and evolve the core Agent OS—a high-performance, resilient platform encompassing the dialog & policy engine, distributed context & memory, execution runtime, security isolation, voice runtime, and complex agentic orchestration frameworks. This system underpins all Boson agents, from low-code configuration flows in Workspace to advanced, production-grade systems leveraging RAG, ReAct, robust tool calling, and multi-step execution.

## Responsibilities

- 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.

## About Boson AI

## Company Overview
- **One-liner**: Boson AI builds real-time, audio-native AI models and agentic systems for natural, conversational voice interactions with machines.
- **Entity Type**: Private (funding not disclosed)
- **Headquarters**: Santa Clara, California, United States (also Toronto, Canada office)
- **Founded**: 2023
- **Founders**: Dr. Alex Smola (Co-Founder & CEO/CSO), Dr. Mu Li (Co-Founder & CEO/CTO), Yi Zhu (Co-Founder, departed May 2026), Yizhi Liu (Co-Founder)

## Core Business
- **Primary industries**: Conversational AI, Voice AI, Multimodal AI, Enterprise AI infrastructure
- **Target customers**: B2B – enterprises deploying voice agents, real-time customer service, and AI-powered communication workflows
- **Mission**: “Make communication with machines as easy, natural and fun as talking to a human.”

## Products & Services
- **Higgs Realtime**: An end-to-end, audio-native real-time speech-to-speech model for enterprise voice agents. Supports interruptions, code-switching (100+ languages), ~700ms speech-in to speech-out latency, and API compatibility with OpenAI Realtime (change three lines of code). Pricing: $0.0023/min audio in, $0.014/min audio out. [LinkedIn post](https://linkedin.com/company/boson-ai)
- **Higgs Avatar v1**: Real-time conversational avatar foundation model that generates 480p video at 16 FPS from a single image and streaming audio. Designed for real-time digital presence in voice agents. Private preview announced May 2026. [LinkedIn post](https://linkedin.com/company/boson-ai)
- **Agent Platform (Agent OS)**: An internal platform for building, deploying, and orchestrating AI agents (referenced in job postings as “Member of Technical Staff - Agent Platform”).

## Market Standing
- **Valuation**: Not publicly available
- **Key Metric**: Total funding not disclosed; headcount of 32 employees (as of mid-2026)
- **Notable Investors/Partners**: Not publicly listed. Talent sources include Amazon Web Services (AWS), Google, University of Toronto, Vector Institute, indicating strong ties to top AI research and engineering communities.
- **Growth Signals**: Launch of Higgs Realtime (August 2026) and Higgs Avatar v1 (May 2026); active hiring for senior engineering and ML roles; 10,000+ LinkedIn followers with +2.5% monthly growth; technical team comprises 68% of workforce.

## Competitive Advantages
- **Full-stack AI ownership**: Builds own foundation models (audio, avatar, agent orchestration) rather than stitching external components, enabling deep co-design for low latency and natural interaction.
- **Real-time expertise**: Achieves ~700ms speech-in to speech-out and ~125ms barge-in yield, with top benchmarks in tool-calling and interruption recovery.
- **Cost efficiency**: Higgs Realtime pricing ($0.0023/min audio in) is significantly lower than many competitors, targeting enterprise-scale deployment.
- **Research pedigree**: Founders have “almost a century of expertise in AI” (Alex Smola and Mu Li are well-known ML researchers; Smola co-authored seminal work on kernel methods and scalable ML).

## Strategic Focus
- **Real-time conversational AI for enterprise**: Prioritizing production-ready voice agents that can handle interruptions, code-switching, and emotional alignment.
- **Multimodal expansion**: Adding visual presence (avatars) to voice agents to make interactions more natural and human-compatible.
- **Enterprise deployment**: Offering self-hosting options and private previews for large-scale customers.
- **Building the full agentic pipeline**: From data and modeling to training, tuning, and serving – all in-house.

## Why Work Here
- **Culture**: Described as “a diverse group of researchers, engineers, and industry specialists united by a passion for innovation.” Emphasis on building scalable AI that serves millions.
- **Work policy**: Most roles are on-site at Santa Clara HQ or Toronto office. A Site Reliability Engineer role was listed as remote (Toronto). Likely hybrid/on-site for core engineering.
- **Engineering environment**: Deep technical stack (PyTorch, TensorFlow, Kubernetes, NVIDIA, Supermicro, etc.). Opportunity to work on frontier AI models and real-time systems.
- **Growth stage**: Small team (~32 people) with strong research roots; employees have previously worked at AWS, Google, Robinhood, and alumni go to OpenAI, Anthropic, xAI – indicating high-caliber talent and career mobility.
- **Hiring process**: Uses AI tools to assist with resume review and analysis, but final decisions made by humans.

## Sources
1. [boson.ai/about](https://www.boson.ai/about)
2. [boson.ai/about/team](https://www.boson.ai/about/team)
3. [linkedin.com/company/boson-ai](https://linkedin.com/company/boson-ai)
4. [jobs.lever.co/bosonai](https://jobs.lever.co/bosonai)

## Other roles at Boson AI

- [Datacenter Technician](https://feeny.ai/job/datacenter-technician-boson-ai-barrie-11tetdz9z3vr) — Barrie, Canada
- [Software Engineer - Platform & Application](https://feeny.ai/job/software-engineer-platform-application-boson-ai-santa-clara-zct1esjbfzcn) — Santa Clara, CA
- [Site Reliability Engineer](https://feeny.ai/job/site-reliability-engineer-boson-ai-toronto-69xyfzggnjaj) — Toronto, Canada
- [Senior Software Engineer - Systems](https://feeny.ai/job/senior-software-engineer-systems-boson-ai-santa-clara-94h0qsa26h84) — Santa Clara, CA
- [Frontend Engineer](https://feeny.ai/job/frontend-engineer-boson-ai-santa-clara-nbsv99agxh8b) — Santa Clara, CA
- [Machine Learning Engineer - Enterprise](https://feeny.ai/job/machine-learning-engineer-enterprise-boson-ai-toronto-cm87w800xvcq) — Toronto, Canada