3 hours ago
Responsibilities
- Design, build, and operate core platform services and the model gateway.
- Create reliable deployments for client cloud accounts, dedicated instances, and sovereign or air-gapped environments.
- Build and maintain CI/CD, infrastructure-as-code, deployment tooling, and GitOps workflows.
- Design secure multi-tenant systems with clear operational ownership and accountability.
- Establish observability using service-level objectives, logging, metrics, tracing, and alerting.
- Develop telemetry to track model and compute costs by tenant.
- Participate in on-call operations and incident response and help improve production operating processes.
Requirements
- At least 6 years of platform or infrastructure engineering experience supporting production SaaS.
- Experience shipping products that customers operate in their own environments, including VPC, self-hosted, or air-gapped environments.
- Deep expertise in at least one backend language such as Go, Ruby, Python, or TypeScript, with the ability to work across the others.
- Deep experience with at least one cloud and working familiarity with a second, including private networking, KMS, IAM, Kubernetes, Helm, Terraform or Pulumi, and GitOps.
- Experience carrying a pager, running incidents, and owning reliability and observability.
- Familiarity with Postgres, object storage, queues, and vector stores.
- Preferred experience with model serving and model gateways using vLLM, LiteLLM, or Triton.
- Preferred experience implementing SOC 2 controls in CI and deploying AI systems in regulated or highly controlled environments.
Benefits
- Hybrid work model with regular in-office presence in New York City near Madison Square Park.
- Opportunity to work at the frontier of AI, contribute to AI research and enterprise deployments, and collaborate with experienced colleagues from leading technology companies.
- Opportunity to showcase work at conferences such as ICLR, ICML, and NeurIPS.
Tech Stack
About Turing
Turing builds AI data pipelines and systems for enterprises and AI labs. It creates large-scale datasets and reinforcement-learning environments to train and evaluate models, and deploys agentic AI applications in domains like software engineering and enterprise workflows. The company also operates an AI-powered talent cloud to match and manage developers and AI trainers. Founded in 2018 and headquartered in San Francisco, it is privately held and serves Fortune 500 customers.