Founding Platform Engineer - Data Engineering

About this role

Founding Platform Engineer | AI, Distributed Systems & SaaS
AllUnited StatesUnited States - San FranciscoUnited States - New York City23 Oct

• New York City or San Francisco Bay Area

• Python | AWS | Distributed Systems | Multi-tenant SaaS

• Pre-seed AI startup | Founding engineering team

Build the platform that makes AI customer conversations accountable
Are you a Staff or Principal-level engineer who thrives on building complex systems from the ground up?
We're looking for a Founding Platform Engineer to help architect and build the core platform of an early-stage AI startup tackling one of the biggest emerging challenges in enterprise technology: ensuring AI-powered customer interactions are safe, compliant, reliable, and accountable.
As the first senior engineer, you'll work directly with the CTO, take ownership of the platform's architecture, and write production code while establishing the engineering foundations for future growth.
This is an opportunity to shape both the technology and the engineering culture of a company from its earliest stages.
The opportunity
Enterprises are deploying AI agents to handle customer conversations across voice, chat, email, and SMS. But as these systems take on increasingly sensitive interactions, businesses need more than capable AI — they need independent oversight, measurable quality and reliable evidence that their AI systems meet the standards required of them.
The company is building an AI monitoring and assurance platform that evaluates customer interactions against applicable regulations and business requirements, helping enterprises identify issues early, reduce risk, and demonstrate accountability.
You'll help build the infrastructure that makes this possible, from high-volume conversation processing to enterprise-grade auditability and security.
What you'll own
Core platform architecture

• Design and build the backend systems that power a scalable, multi-tenant SaaS platform.

• Establish robust APIs, data models, and distributed processing architecture.

• Make key technical decisions that will shape the platform as the company grows.

Conversation pipelines and integrations

• Build pipelines that transform raw conversations into structured, enriched, and scorable data.

• Develop integrations with contact-centre platforms, ticketing systems and direct-upload workflows.

• Help evolve the voice-processing architecture, including transcription and audio ingestion.

Auditability and data provenance

• Implement immutable versioning for scoring rubrics and configurations.

• Build tamper-evident audit trails and preserve evidence throughout the conversation lifecycle.

• Ensure historical results can be reproduced and explained, including why a particular interaction received its original score.

Infrastructure and engineering excellence

• Own infrastructure as code, CI/CD, environment management and deployment workflows.

• Establish observability, logging, metrics, tracing, alerting, secrets management and disaster recovery.

• Take responsibility for production reliability, incident response and operational readiness.

Scale, performance and reliability

• Build reliable data storage, retention and queueing systems.

• Implement idempotent processing and reprocessing to prevent duplicate work and unnecessary AI inference costs.

• Define service-level objectives and help scale the platform from early pilots to millions of interactions per month.

Security and enterprise readiness

• Build tenant isolation, permissions, customer-specific configuration, and entitlements into the platform.

• Implement sensitive-data handling and redaction before conversations reach AI models.

• Support evidence exports, SOC 2 readiness, and enterprise security reviews.

What we're looking for

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Senior engineering ownership: You've operated at Staff, Principal or equivalent scope, owning critical production systems and setting technical direction for other engineers.

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Strong backend and distributed-systems expertise: You understand high-volume data processing, event-driven architectures, API design and data modelling.

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Hands-on infrastructure experience: You've built infrastructure as code, CI/CD and observability capabilities yourself, rather than relying entirely on a dedicated platform team.

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Early-stage startup experience: You've shipped products in an early-stage or high-growth environment where ambiguity, broad ownership and limited support functions were part of everyday life. This is essential.

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Strong Python skills: You're comfortable building production-grade backend systems and have enough TypeScript and React experience to contribute to the product portal when needed.

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ML systems fluency: You can discuss model evaluation, deployment, monitoring, accuracy, latency, and inference costs with an ML Lead. Research experience is not required.

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Enterprise SaaS experience: You understand multi-tenancy, tenant isolation, access controls, and customer-specific entitlements.

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A pragmatic approach to AI-assisted development: You're comfortable using coding agents, including Claude Code, while maintaining high standards for code quality, testing, security, and maintainability.

Bonus points
Experience in any of the following would be particularly valuable:

• Auditability, data provenance or compliance-grade systems.

• Speech and audio processing, including ASR, diarisation, streaming transcription or real-time systems.

• Contact-centre technology, CCaaS, quality assurance or workforce optimisation.

• Governance, risk and compliance (GRC) platforms or regulated enterprise environments.

Why consider this opportunity?

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Founding-level impact: Work directly with the CTO and influence the platform's architecture from day one.

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Real technical ownership: Build the systems, infrastructure, and engineering practices rather than inheriting established processes.

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A meaningful problem to solve: Help enterprises adopt AI customer…