Staff Software Engineer

🕒 il y a 6 mois

🇺🇸 États-Unis – Télétravail

💵 $120 000 - $200 000 / an

⏰ Temps Plein

🔴 Expert

🧑‍💻 Développeur Full-Stack

👻 Score fantôme 65%

Ce que ça veut dire : la probabilité que cette offre reste ouverte sans recrutement actif.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

Postuler Maintenant

Trouver des Emplois à Distance Similaires

Configurer une alerte email pour les nouveaux emplois

📊 Vérifiez votre score de CV pour ce poste

Améliorez vos chances d'obtenir un entretien en vérifiant votre score de CV avant de postuler.

Télécharger le CV

Startup Talent

Site WebLinkedInTous les Emplois

11 - 50 employés

Fondée en 2014

💼 Conseil

📣 Marketing

📦 Logistique

Consulting • Marketing • Logistics

Startup Talent est un cabinet de recrutement spécialisé dans la mise en relation des employeurs avec les meilleurs talents, tous secteurs confondus. Nous aidons les entreprises à constituer des équipes solides et à atteindre leurs objectifs de recrutement en attirant des candidatures pertinentes et en fidélisant leurs collaborateurs. Startup Talent met en avant son expertise dans des domaines tels que l’IT, la technologie et les fonctions commerciales, et propose des solutions de recrutement sur mesure, adaptées à divers modes de travail et types de contrat.

Description

• 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 • 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.

🎯 Exigences

• 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 • Experience with model serving / LLM infrastructure — deploying, scaling, and optimizing inference (vLLM, TGI, TensorRT-LLM, or managed endpoints) (strong plus) • Background in trading systems, exchange APIs, or fintech where uptime has direct financial consequences (strong plus) • Experience with onchain infrastructure: wallet operations, RPC nodes, transaction monitoring, DEX integration (strong plus) • Familiarity with MCP (Model Context Protocol) or similar agent-to-tool connectivity patterns (strong plus) • Experience building multi-agent platforms — orchestrating many independent processes sharing infrastructure but operating autonomously (strong plus) • Experience with CI/CD for systems where “deploy” means updating live trading agents, not just web servers (strong plus)

🏖️ Avantages

• healthcare and life insurance • retirement plan • sponsored transportation • gym • lunch card • home office equipment • other perks