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Plaud

Full-Stack Engineer - Palo Alto

San Francisco, CAHybrid · United States
FullTime · Algorithms & Platform R&D
POSTED 31 AUGVERIFIED 1H AGO
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Open for
64d
since 31 Aug
H-1B · FY26
21
certified LCAs
Hiring · 30d
46
0 net · open roles
PythonGoJavaC++LLMsDistributed systems

About Plaud Inc.

Plaud is building the real-world AI interface for professionals to amplify intelligence, elevate productivity and performance, loved by over 3,000,000 users worldwide since 2023. With a mission to amplify human intelligence, Plaud captures, structures, and compounds the intelligence generated in conversations — so humans can think better, decide faster, and execute with clarity.

Plaud Inc. is a Delaware-incorporated, San Francisco-based company. With ISO 27701, GDPR, SOC 2 Type II, HIPAA, and EN 18031 compliance, Plaud is committed to the highest standards of data security and privacy protection.

To learn more about Plaud, please visit https://www.plaud.ai and follow along on Instagram, X, Facebook, LinkedIn, and YouTube

Why You Should Join Us

Plaud is building the next generation intelligence infrastructure and interfaces to capture, extract, and utilize intelligence from what people say, hear, see, and think.

Plaud is a bootstrapped, skyrocketing, profitable company with a $300M revenue run rate achieved in just three years.

Define the next-gen paradigm for human-AI interaction.

Gain exposure to cutting-edge AI for Pro tools and play a direct role in our global expansion.

Work with passionate teammates who value innovation, collaboration, and customer success.

Grow your career in a culture that champions continuous learning and fast career development.

Market-competitive compensation, global exposure, and a vibrant, creativity-fueled work atmosphere.

What You Will Do

AI Application & Backend Core Delivery: Engineer and iterate realtime voice AI services and Agent workflows; build key modules including tool orchestration, memory, and multi-step reasoning; translate business requirements into scalable service capabilities.

Production-Grade System Engineering: Design and implement highly-available, low-latency, scalable architectures (latency is especially critical for realtime voice); establish monitoring, logging, alerting, and tracing foundations to support both high-frequency consumer usage and enterprise deployment.

AI Integration & Closed-Loop Iteration: Integrate LLM / ASR / TTS capabilities into product pipelines; help build evaluation frameworks and automated testing for Agent and voice quality; partner with Applied AI and Product to drive data-driven, closed-loop improvement in production.

Full-Stack Collaboration & Rapid Iteration: Keep backend as your main line while extending into mobile, SDK, or frontend surfaces as needed to ship end-to-end; collaborate closely with Product, Applied AI, and mobile teams across enterprise PoCs and consumer features.

AI-Native Engineering Velocity: Actively adopt AI coding assistants, agentic dev workflows, and automated testing/debugging tools to accelerate iteration while maintaining strong engineering quality.

Minimum Qualifications

Strong Engineering Execution (Required)

Proven track record of owning and delivering critical systems/components end-to-end (we value demonstrated delivery and depth over a specific number of years)

Solid CS fundamentals: data structures, algorithms, system design

Consistently high-quality output in fast-paced environments

Backend / Distributed Systems (Required · Primary)

Proficiency in at least one backend language (Python, Go, Java, C++, etc.)

Experience building scalable online services: API design, async workflows, microservices

Familiarity with distributed systems fundamentals: databases, caching, messaging, performance optimization

Strong AI Sense & Hands-On Practice (Required · Emphasis)

Not just "interested" — you've actually built products with LLMs / Agents / workflow automation

Ability to turn prototypes into scalable, maintainable, production-ready solutions

You already use AI tools in your day-to-day work

Production Engineering Excellence

Experience delivering production systems with a focus on reliability, latency, and scalability

Knowledge of observability best practices: logging, monitoring, alerting, tracing

Engineering rigor via automated testing, CI/CD, and disciplined code review

Source · PlaudFirst seen 2 NovOriginal ↗