Among computer occupations, all other, AI is already used for a large share of the tasks. Measured exposure for computer occupations, all other: 31% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

New York City or San Francisco Bay Area

Python | AWS | Distributed Systems | Multi-tenant SaaS

$160K–$190K USD base salary + equity

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

Pre-seed AI startup | Early engineering team

Build the platform that makes AI customer conversations accountable

Are you a hands-on software engineer who enjoys building scalable backend systems, working with modern cloud infrastructure, and solving complex engineering challenges?

We're looking for 2 Platform Engineers to help build the core technology 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.

You'll work closely with the CTO and engineering team, contributing to the architecture and development of a platform that processes and evaluates customer conversations across voice, chat, email, and SMS.

This is an opportunity to join an early-stage company, work across a broad technology stack, and make a meaningful contribution to a product from its early stages.

The opportunity

Enterprises are deploying AI agents to handle customer conversations. However, businesses need more than capable AI systems — they need independent oversight, measurable quality, and reliable evidence that their AI systems meet regulatory and business requirements.

The company is building an AI monitoring and assurance platform that evaluates customer interactions, identifies potential issues, reduces risk, and helps enterprises 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 work on

Backend and platform engineering

Develop backend services for a scalable, multi-tenant SaaS platform.

Build APIs, data models, and distributed processing components.

Contribute to architectural decisions and engineering improvements.

Data pipelines and integrations

Develop pipelines that transform raw conversations into structured and enriched data.

Build integrations with contact-centre platforms, ticketing systems, and other external services.

Contribute to voice-processing and audio-ingestion workflows.

Cloud infrastructure and DevOps

Work with AWS infrastructure, infrastructure as code, and CI/CD pipelines.

Improve observability, logging, monitoring, and deployment workflows.

Help maintain production reliability and operational readiness.

Performance and scalability

Build reliable data storage, queueing, and processing systems.

Improve system performance, concurrency, and resource utilisation.

Contribute to systems designed to process millions of interactions per month.

Security and enterprise readiness

Implement tenant isolation, permissions, and customer-specific configurations.

Support sensitive-data handling and redaction.

Contribute to audit trails, data provenance, and enterprise security requirements.

What we're looking for

Software engineering experience: Approximately 2–4 years of professional engineering experience, with hands-on experience delivering production software.

Backend development: Strong Python skills and experience building APIs and backend services.

Cloud experience: Practical experience with AWS and cloud-based application development.

Distributed systems: Understanding of event-driven architectures, data pipelines, concurrency, and scalable processing.

Programming languages: Experience with Go (Golang) or Rust is highly desirable.

Infrastructure: Familiarity with infrastructure as code, CI/CD, deployment workflows, and monitoring.

Database experience: Knowledge of PostgreSQL, MongoDB, or similar technologies.

AI/ML exposure: An understanding of machine learning systems, LLM integrations, model evaluation, or AI-powered applications. You don't need to be an ML specialist.

Startup mindset: Comfortable working in a small engineering team where flexibility, initiative, and a willingness to learn are important.

AI-assisted development: Familiarity with coding agents such as Claude Code is beneficial.

Bonus points

Experience with high-throughput ingestion systems.

Speech and audio processing, including ASR and transcription.

Contact-centre technology or customer experience platforms.

Auditability, data provenance, or compliance-related systems.

Experience with TypeScript and React.

Why consider this opportunity?

Join an early-stage AI company with significant technical challenges to solve.

Work directly with the CTO and experienced engineering colleagues.

Contribute to a platform from its early development stages.

Gain exposure to distributed systems, cloud infrastructure, and applied AI.

Work on a meaningful product addressing the growing need for AI accountability in enterprise environments.