Risk Labs

Senior LLM Systems Engineer

9.0/10

Risk Labs

$100,000 – $200,000 USD
Remote
senior
10 days ago
aicryptodefiweb3PythonTypeScriptPostgresGCPCloud RunGitHub ActionsTerraformReact

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Description

What You'll Own

  • LLM Accuracy: improve prompts, model selection, tool usage, structured outputs, retrieval, and evaluation coverage so the system gets more decisions right over time.
  • System Performance: reduce latency, token usage, and cost while preserving decision quality and operational reliability.
  • Resilience: design validation, retries, fallbacks, uncertainty handling, and human review paths for ambiguous, adversarial, incomplete, or conflicting inputs.
  • Evaluation and Monitoring: build datasets, regression tests, dashboards, traces, and review loops that make model quality visible and prevent repeated failures.
  • Agent and Tooling Architecture: Improve agent orchestration and tool use across internal services, APIs, search workflows, databases, and external data sources.
  • Production Operations: help debug live issues, investigate regressions, improve runbooks, and reduce repeated operator friction.

Compensation and Benefits

  • Pay packages include competitive salaries & meaningful long term equity participation.
  • Salaries for this role range from $100-200k (USD).
  • Will pay in stablecoins or fiat.
  • Philosophies for a culture that show we care: Take vacation when you need it, family care, training and development (just to name a few).
  • 100% remote, which means we encourage you to create the work environment that you thrive in.
  • At least two team wide offsites a year.

Requirements

Skills & Experience

#### Required

  • 3+ years of professional software engineering experience in Python, TypeScript, or similar production languages.
  • Hands-on experience building production systems that use LLMs, agents, retrieval, structured outputs, or model-powered workflows.
  • Experience designing evaluations, test datasets, regression checks, quality metrics, or manual review loops for AI systems.
  • Strong debugging ability across APIs, databases, queues, logs, model outputs, and external data sources.
  • Practical understanding of prompt engineering, tool calling, structured output validation, retrieval, and common LLM failure modes.
  • Ability to reason carefully about correctness in uncertain or adversarial environments.
  • High agency, strong ownership, and clear written communication.

#### Nice to Have

  • Experience with oracle systems, prediction markets, DeFi protocols, or other crypto infrastructure.
  • Experience with UMA, optimistic oracle mechanisms, Polymarket, or similar systems.
  • Experience building agentic systems that use tools, search, browser automation, APIs, or database queries.
  • Experience with LLM tracing, model monitoring, evaluation frameworks, or AI observability tools.
  • Experience optimizing model cost and latency at scale.
  • Experience with Postgres, data pipelines, queue-based systems, background jobs, or event-driven architectures.
  • Familiarity with blockchain operational constraints, especially RPC limits, indexing, event logs, finality, and chain-specific behavior.
  • Experience with GCP, Cloud Run, GitHub Actions, Terraform, or similar infrastructure.
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