Own backend and AI infrastructure for autonomous trading agents. Build the Go-based agent runtime, execution and data layers, real-time trading systems, deployment platform, model hosting migration, telemetry, CI/CD, monitoring, and incident response. Develop production services primarily in Go, Python, and TypeScript while operating AWS EKS infrastructure. The role requires end-to-end ownership of reliable, low-latency systems managing real-time trading and financially sensitive positions.
This is a remote position.
The company is an OnchainGPT for autonomous trading. It is an AI-powered mentor, advisor, and companion that helps users explore the onchain world, find alpha, auto-trade, and outsmart the market.
They are looking for Staff Software Engineer (Backend and AI Infra) who will own two critical workstreams :
- the agent runtime and backend infrastructure that powers every trade in their fleet,
- and the migration of model hosting and agent deployment in-house, moving the company off third-party LLM providers and hosted agent platforms to own infrastructure.
NOTE : This role is NOT a pure Devops role but rather a building role in which you will spend 80% of your time writing Go, Python, and TypeScript that ships to production.
You’re writing the backend services, runtime engine, and deployment systems that the entire company's agent fleet runs on.
When you ship, every agent in the fleet immediately gets faster, more reliable, and more autonomous.
You are in charge of managing the very infrastructure you are building, as its designer you are be the best person to operate it.
You’re building the backend for autonomous AI agents that manage real money in real time.
The runtime you build determines whether positions are protected.
The model hosting you stand up determines whether agents can think.
The deployment pipeline you create determines whether the fleet can evolve.
This is foundational infrastructure for a new category of software.
MISSIONS
Agent Runtime & Backend (+/- 50%)
The runtime is the engine that makes every agent work. You’ll own the core systems:
Plugin Runtime — the per-agent process that runs position tracking (10s polling), the RatchetStop exit engine (tiered trailing stops with sub-second evaluation), and DSL state management. Currently Go + Python; migrating to a centralized Go service with Postgres state and real-time websocket price feeds
Scanner Gateway / Rules Engine — a YAML-configurable evaluation layer that sits between scanners and execution. Scanners produce raw signal variables; the rules engine applies gates, scoring, and filters defined in YAML. Users customize trading behavior without touching Python. This is the next major runtime feature
RatchetStop Backend — centralized profit-trailing service that protects positions even when the agent is offline. Evaluates tier upgrades and places stop-loss orders on Hyperliquid via websocket, replacing per-agent polling with condition-based evaluation across all positions
Execution Layer — the MCP (Model Context Protocol) server that bridges agents to the company's 48+ platform tools: position creation, clearinghouse state, market data, Smart Money intelligence. You’ll own auth, rate limiting, and the contract between agents and the exchange
Data Layer — enriched Hyperfeed pipeline (top 1K trader positions, momentum events, market concentration) flowing through Redis, Postgres, and ClickHouse. Real-time ingestion, 4-hour rolling windows, and the APIs that every scanner calls
Model & Agent Hosting Migration (+/- 30%)
The company is moving off third-party hosted agents and external LLM inference to the company's owned infrastructure. You’ll lead the technical execution:
Agent deployment platform — migrate agents from Railway/OpenClaw to own-hosted infrastructure. Each agent needs isolated workspace, cron scheduling, state persistence, MCP connectivity, and Telegram notifications. Target: deploy any skill from a GitHub repo with one command
Model hosting — evaluate and implement the path from external LLM APIs (Anthropic, Google) to self-hosted inference. Options range from proxied external models with full telemetry capture, to fine-tuned models running on own GPUs. You’ll own the decision and execution
Agent telemetry — capture every scanner evaluation, every trade decision, every signal score across all agents. This data feeds the self-reinforcing loop: agents learn from fleet-wide performance, fork winning strategies, and improve autonomously
Deployment pipeline — CI/CD for shipping scanner updates, runtime patches, and skill configs to 50+ live agents without interrupting open positions. Zero-downtime rollouts where downtime = unprotected capital
Infrastructure & Operations (+/- 20%)
Build monitoring and alerting that catches agent failures, orphaned positions, state corruption, and Auth expiration before they cost money
Manage cloud infrastructure (AWS/EKS) with infrastructure-as-code
Own incident response — in a trading system, every minute of downtime is real dollars at risk
Health monitoring for the agent fleet: which agents are scanning, which are stuck, which have the midnight rollover bug
Requirements
MANDATORY
- Strong backend engineering — you write production code daily in at least two of: Go, Python, Node.js/TypeScript. Go being the preferred language for the runtime services
- Experience building backend services from scratch at a startup: APIs, job scheduling, state management, distributed systems
- Solid understanding of real-time systems where latency matters: websocket connections, condition-based evaluation, sub-second response requirements
- Production experience with Postgres, Redis, and at least one analytics DB (ClickHouse, TimescaleDB, BigQuery)
- Kubernetes experience — deploying, scaling, and debugging production workloads on AWS EKS
- You’ve owned a system end-to-end: designed it, built it, deployed it, operated it, fixed it at 3am
STRONG PLUS
- Experience with model serving / LLM infrastructure — deploying, scaling, and optimizing inference (vLLM, TGI, TensorRT-LLM, or managed endpoints)
- Background in trading systems, exchange APIs, or fintech where uptime has direct financial consequences
- Experience with onchain infrastructure: wallet operations, RPC nodes, transaction monitoring, DEX integration
- Familiarity with MCP (Model Context Protocol) or similar agent-to-tool connectivity patterns
- Experience building multi-agent platforms — orchestrating many independent processes sharing infrastructure but operating autonomously
- Experience with CI/CD for systems where “deploy” means updating live trading agents, not just web servers
Benefits
- Contract : Permanent role - Remote EU or US
- Benefits: healthcare and life insurance, retirement plan, sponsored transportation, gym, lunch card, home office equipment, and other perks.
Recruitment process :
- Video call with CEO/CTO (30mn).
- Take home coding test
- Code focus interview with tech teams
- Final Interview
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What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
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