• Desenvolver e manter APIs seguras e escaláveis que suportam os produtos digitais • Contribuir para entregas de valor com qualidade técnica e alinhamento às regras de negócio • Desenvolver e manter APIs REST • Contribuir na implementação de microsserviços com apoio de desenvolvedores mais experientes • Apoiar investigações técnicas analisando logs e comportamentos do sistema • Escrever testes unitários e apoiar na criação de testes de integração • Participar ativamente de dailies, refinamentos e plannings • Buscar mentoria com pares mais experientes e aplicar o aprendizado nas entregas

• Develop and maintain scalable, secure, high-performance backend services and APIs for software products • Ensure quality, security, and an excellent customer experience • Solve complex problems using sustainable engineering practices • Collaborate with cross-functional teams on technical and product decisions • Design, develop, and evolve backend services (REST APIs) with a focus on availability, performance, and security • Participate in architecture discussions and technical decision-making • Implement and maintain security standards • Conduct code reviews and uphold standards for code quality, testing, and documentation • Monitor and optimize performance • Instrument observability and respond to incidents • Collaborate with Product, QA, SRE/Cloud, and Support teams • Act as a technical reference, mentoring less experienced developers and promoting best practices.

• Build and maintain full stack features end-to-end: .NET backend services and React frontends that bring AI-powered capabilities into clinician workflows. • Integrate AI components (e.g., speech-to-text, LLM services) into our platform — handling the plumbing, error handling, and UX that make these services reliable in production. • Design and implement REST APIs, event-driven services, and microservice integrations within our Azure-hosted ecosystem. • Write clean, testable code and contribute to CI/CD pipelines, observability, and engineering quality practices. • Collaborate closely with the Team Lead, fellow engineers, and Product to break down requirements, estimate work, and ship iteratively. • Participate actively in a remote scrum team: sprint ceremonies, PR reviews, and knowledge sharing.

Location: Remote (US preferred) Level: Staff / Principal Competitive base salary ($175K - $225K) We are hiring on behalf of our client, a high-growth, venture-backed crypto company is hiring a Staff Backend Engineer to help build next-generation autonomous trading infrastructure. This is a senior, ownership-heavy role working on real-time trading systems, wallets, and execution infrastructure operating under extreme market conditions. The client is highly responsive and only interviewing candidates who meet all criteria. 🚨 Non-Negotiable Hiring Criteria Candidates must have all of the following: - Hands-on experience with crypto wallets, tokens, and/or trading systems - Deep production experience with Golang, Node.js, and TypeScript - Strong understanding of distributed systems, performance, and reliability - Deep familiarity with trading APIs, execution flows, and transaction lifecycles If any of the above is missing, this role will not be a fit. The Role You’ll own core systems inside the Agent Runtime — the infrastructure that prevents autonomous agents from destroying capital. Specifically: - Hyperfeed data pipeline — executing the enrichment roadmap: OI velocity, funding history, liquidation data, cross-asset flow detection. Server-side computation at scale. - Runtime execution layer — serial signal processing, atomic state management, idempotent order submission, risk enforcement against live exchange state - DSL exit engine — the trailing stop-loss state machine that protects every open position. Two-phase architecture, exchange reconciliation, state recovery - Scanner subsystem — building new scanner types, optimizing the provider layer, wiring new Hyperfeed signals into the confluence scoring system - Learning agent infrastructure — building the systems that let agents observe every trade, identify failure patterns, propose runtime improvements, and launch new experiments autonomously - Scale — taking the runtime from hundreds of agents to tens of thousands