Senior Backend Engineer - SG

Plaud is building the real-world AI interface for professionals to amplify intelligence, elevate productivity and performance, loved by over 2,500,000 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 pushing the boundary of human–AI intelligence through a hardware–software combination. With full ISO 27001, ISO 27701, SOC 2, GDPR, EN18031, and HIPAA compliances, 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.

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.

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  Own the end-to-end evolution of the evaluation platform: design and deliver step-level evaluation, automated regression analysis, and release quality gates that enforce production-grade standards across product and engineering teams; benchmark architecture and reliability against SOTA platform engineering practice at companies such as Grab and Sea.

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  Build and operate the data governance and security layer: deliver real-time PII/sensitive-data masking, schema synchronization, data ownership, versioned data dictionary, audit controls, and least-privilege governed access interfaces for AI Agents and internal tools — with no direct database connections and full auditability.

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  Design and ship reusable platform capabilities: shared SDKs, common workflow definitions, dataset/model/metric management APIs, and observability instrumentation that reduce per-team integration effort and enable new evaluation data sources to be onboarded within 1 day.

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  Drive platform reliability and observability: define and enforce SLA targets, build alerting and incident-analysis tooling, and establish quality-gate mechanisms that make evaluation results a trusted signal for release decisions.

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  Apply AI/LLM techniques where they replace brittle rule-based logic: identify evaluation or governance sub-problems where ML/LLM approaches outperform hand-crafted rules, prototype and validate them, and integrate proven approaches into the platform.

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  AI-native engineering: Actively uses AI-assisted tools for coding, debugging, analysis, documentation, and incident investigation to improve engineering efficiency and quality.

• 5+ years of backend/platform engineering experience, including 3+ years designing and operating complex distributed systems in production; strong proficiency in Go and/or Java.

• Demonstrated experience building shared SDKs, reusable platform components, and production microservices with Kubernetes/Docker, HTTP/gRPC, retries, rate limiting, circuit breaking, auth, and fault isolation — shipped and operated at scale, not prototype-only.

• Strong command of database design, high availability, transaction isolation, idempotency, eventual consistency, observability, alerting, and incident analysis; able to reason from first principles about failure modes and recovery paths.

• Hands-on experience with workflow orchestration (e.g. Argo Workflows) and automated evaluation or validation systems, including execution, comparison, reporting, and quality gates.

• Proven experience with secure production-data access patterns: access control, auditing, data ownership, retention policies, least-privilege design, and PII/sensitive-data masking in a regulated or compliance-conscious environment.

• Experience with modern data infrastructure and processing frameworks such as Spark/Flink, Snowflake, Databricks, and Athena; familiarity with data warehouse, data lake, or data governance practices, including building data systems that are manageable, traceable, access-controlled, and secure.