Sable Logo

Sable

Platform Engineer

Reposted 3 Months Ago
In-Office
San Francisco, CA, USA
Mid level
In-Office
San Francisco, CA, USA
Mid level
Build and own the end-to-end platform that runs realtime AI-human sessions: provisioning, streaming performance, cost per session, security/isolation, reliability, and observability. Provide self-service tooling for engineers and deployment teams, implement SLOs and incident response, and scale browser-based sessions while enforcing enterprise data-handling standards.
The summary above was generated by AI
About Sable

Sable built Aidan, the first AI employee who can lead customer calls using realtime voice, vision, and browser use. Aidan runs a live, two-way conversation inside a real product environment, clicking through the product like a human, watching the user's screen, and adapting the journey on the fly. Every conversation feeds a self-improving context graph we call the Brain, so Aidan gets smarter with each call.

The role

You build the foundation upon which Sable's product exists. Every session runs on a real product environment in realtime, with an AI employee and a human sharing control. You own that system end to end: provisioning, streaming performance, cost per session, and the security standards that lets customers trust us with their products. You'll treat our engineers and deployment team as your customers, building the self-service platform and paved paths that let them spin up, configure, and operate environments without waiting on anyone. This is a role of extraordinary leverage: routinely supporting thousands of sessions, and as our volume grows with usage-based pricing, this infrastructure is both critical to margin and the user experience. You will be measured on the things that matter most: reliability, security, data handling, and uptime.

What you'll do
  • Build upon and grow the infrastructure that all engineers, the deployment team, and the product depend on

  • Own security and isolation guardrails, data-handling standards, and implement enterprise-level best practices

  • Own reliability and observability across every product surface: SLOs, incident response, and the instrumentation that flags any issues before the customer does

  • Allow us to scale browser use with optimal reliability as volume grows

Who you are
  • Experience in infrastructure and distributed-systems: container orchestration (Kubernetes), cloud cost engineering, realtime streaming systems, and DevOps

  • You bring a product mindset to internal platforms: your customers are the engineers and deployment team who build on your work, and their adoption and feedback is one of the ways of how you judge success

  • Excited to be the first dedicated owner of infrastructure, and to build the leverage that lets a small team operate like an enterprise

Similar Jobs

5 Days Ago
In-Office or Remote
2 Locations
165K-242K Annually
Senior level
165K-242K Annually
Senior level
Cloud • Information Technology • Machine Learning
Designs and operates enterprise-scale data platforms, including batch and streaming pipelines, transformation and orchestration workflows, analytical data warehouses, telemetry systems, data catalogs, semantic layers, and AI-supporting data interfaces. The role requires production monitoring, incident support, architectural decision-making, and collaboration with engineering, analytics, data science, and business stakeholders.
Top Skills: Apache AirflowAWSDagsterDbtGoGoogle Cloud PlatformJavaAzurePythonScalaSQL
6 Days Ago
Hybrid
2 Locations
Senior level
Senior level
Financial Services
Leads software engineering and AI platform initiatives, designing secure, scalable cloud infrastructure for AI/ML workloads. Builds Kubernetes-based platforms, CI/CD pipelines, automation, and infrastructure-as-code for ML deployment and operations. Partners with AI teams on infrastructure requirements, optimizes cloud performance and costs, and promotes responsible AI-assisted engineering practices, governance, validation, and secure SDLC automation.
Top Skills: C#DockerGoGrafanaHybrid CloudIaasInfrastructure As CodeJavaKubernetesLinuxMicroservicesMlflowNoSQLNvidia BcmNvidia DcgmNvidia Dynamo InferencePaasPrivate CloudPrometheusPublic CloudPythonRay.IoSaaSSlurmSQLTransformer ArchitectureVllm
8 Days Ago
Easy Apply
In-Office
San Francisco, CA, USA
Easy Apply
200K-265K Annually
Senior level
200K-265K Annually
Senior level
Artificial Intelligence • Logistics • Transportation
Own infrastructure scaling, reliability, portability, observability, incident response, and developer productivity for a platform running millions of AI agent executions daily. The role addresses distributed systems, capacity planning, performance, enterprise integrations, cloud and on-premise deployments, identity, networking, authentication, data residency, CI, deployments, and production debugging. As an early infrastructure hire, this engineer will set technical direction and take substantial ownership of platform operations.
Top Skills: DatadogGCPOpentelemetryPostgresRedisTypescript

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account