Founding Platform Engineer | AI, Distributed Systems & SaaS

SourceIn; San Francisco, New York (CA, NY); On-site; USD 200,000 - 230,000; Full time

Benefits offered by this job

Visa sponsorship

Founding team

Job summary

SourceIn is building an AI-powered platform to monitor and assure enterprise customer conversations. We are hiring a Founding Platform Engineer at staff/principal level to architect, own, and deliver the core backend platform for a high-scale SaaS product.

You will work with the CTO, set technical direction, define data models, and implement production-grade systems from the ground up. You will own conversation pipelines, auditability, and infrastructure as code, while ensuring performance,

Qualifications

• Staff/Principal-level ownership with critical production systems.
• Experience with high-volume data processing and distributed architectures.
• Hands-on backend development with Go or Rust and solid Python skills.
• Experience with infrastructure as code, CI/CD, and observability.
• Early-stage startup experience and ability to take broad ownership.

Responsibilities

• Design and build backend systems powering a scalable, multi-tenant SaaS platform.
• Establish APIs, data models, and distributed processing architecture.
• Make architectural decisions to scale as the company grows.
• Develop conversation pipelines and integrations with enterprise systems.
• Ensure auditability, data provenance, and tamper-evident records.
• Own infrastructure, CI/CD, and deployment workflows.
• Define SLAs and ensure reliability, performance, and security.

Skills

Senior ownership

Backend systems

Distributed systems

Go

Rust

Python

IaC / Infra as code

CI/CD pipelines

Security & reliability

AWS knowledge

Education

Bachelor's degree in CS or related

Tools

AWS

PostgreSQL

MongoDB

Terraform

Kubernetes

Docker

Job description

New York City or San Francisco Bay Area

$200K–$230K USD base salary + equity

Visa sponsorship available for candidates already in the US who require a visa transfer

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

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

Senior engineering ownership: You've operated at Staff, Principal, or equivalent level, owning critical production systems and setting technical direction in a small engineering team.

Strong backend and distributed-systems expertise: You understand high-volume data processing, event-driven architectures, API design, data modelling, and the challenges of building reliable, scalable systems.

High-performance ingestion systems: You have current, hands-on experience with Go (Golang) or Rust, ideally building high-throughput ingestion engines or data pipelines. You know how to design for concurrency, low latency, efficient resource utilisation, and scale.

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

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.

Strong Python skills: You're comfortable building production-grade backend systems in Python and can help evolve the existing prototype, built with Python, PostgreSQL/MongoDB, and AWS, into a faster, production-ready platform. TypeScript and React experience is useful for contributing to the product portal when needed.

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

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

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.

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?

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

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

A meaningful problem to solve: Help enterprises adopt AI customer support with greater confidence, transparency, and accountability.

Broad engineering scope: Work across backend systems, cloud infrastructure, data pipelines, security, and product-facing functionality.

Room to shape the team: Establish the technical standards and foundations that future engineers will build upon.

If you're a hands-on technical leader who enjoys solving difficult platform problems, building from first principles and taking ownership beyond the code, we'd love to hear from you.