Member of Technical Staff

Chakra Labs; New York (NY); On-site; USD 100,000 - 140,000; Full time

Job summary

A tech innovation company in New York is looking for a skilled professional to manage infrastructure for AI agents. The role involves orchestrating container environments, maintaining distributed systems, and ensuring LLM workloads run efficiently. Ideal candidates will have experience with Kubernetes, SQS, and Kafka, alongside a passion for working in a fast-paced, early-stage team environment. You will have the chance to collaborate with AI researchers directly and influence the future of agent technology.

Qualifications

• Experience with container orchestration tools in production environments.
• Knowledge of distributed systems and message-driven architectures.
• Experience in running LLM workloads at scale.

Responsibilities

• Manage agent orchestration and dispatch layer for AI workloads.
• Design environments and tasks for agent evaluation.
• Monitor and maintain model behavior and infrastructure.

Skills

Container orchestration

Distributed systems

LLM infrastructure

Debugging token throughput

Tools

Kubernetes

SQS

Kafka

Job description

What You'd Work On

• Agent orchestration at scale. Hundreds of agent runs at once, each with its own stateful environment. 100M tokens per minute across the fleet. You own the dispatch layer: SQS, concurrency control, failure handling.

• Environment and task design. We need environments that feel real and scenarios that actually push agents to their limits. You'd figure out how to build new evaluations and design the tasks that test what matters, not just what's easy to measure.

• New frontiers. The agent evaluation space is moving fast. You'd stay on that edge, supporting new environment modalities and shipping integrations with external orchestration frameworks.

• Observability. Prometheus and OpenTelemetry across services, Grafana dashboards, structured logging.

About You

• Container orchestration. You're comfortable running Kubernetes or similar in production. Auto-scaling, pod lifecycle, persistent storage, networking. You can figure out why something won't schedule and reason about resource contention.

• Distributed systems. You've built or maintained message-driven architectures. SQS, Kafka, or similar. You know how to keep jobs moving when things back up, retry without duplicating, and fail without losing work.

• LLM infrastructure. You've run LLM workloads at scale. Token instrumentation, rate limit handling, prompt caching, multi-provider routing. You've built the plumbing between models and external tools, and you know what it takes to keep it all running under load.

• Experience. No hard rule. Ideally at least 3 years at this level, but less works if the above sounds like you.

What Makes This Different

• It's infra, but the workload is AI agents. You're monitoring model behavior alongside pod health, debugging token throughput alongside network throughput.

• Our customers are AI researchers and labs. You'd work directly with the people pushing the frontier of what agents can do, and build the infrastructure they run it on.

• Early-stage team. You own whole systems, not tickets in a queue. One week you're shipping a new environment type, the next you're scaling the dispatch layer to handle 10x the throughput.