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Artian

Software Engineer - AI/ML

New York$125k–$150kfulltimemidAdded 1 month ago

About this role

Artian AI seeks a Software Engineer to develop the intelligence layer of an agentic AI platform for financial services. You'll build and productionize ML components that enable autonomous agents to execute reliable, business-critical workflows in regulated enterprise environments.

What you'll do

  • Build and optimize AI/ML components powering autonomous enterprise workflows
  • Develop evaluation frameworks to measure agent performance, reliability, and failure modes
  • Prototype new capabilities and productionize solutions that deliver customer value
  • Design systems for observability, debugging, and continuous improvement of production AI agents
  • Collaborate with backend, product, and solutions teams to integrate AI into end-to-end workflows
  • Stay current with applied AI developments and assess which techniques suit enterprise environments

What they're looking for

  • Python and production software engineering
  • Applied machine learning and LLMs
  • NLP, embeddings, retrieval, and ranking systems
  • ML tooling (PyTorch, scikit-learn, vector databases)
  • Model evaluation and data pipeline frameworks
  • Agentic system architecture
  • First-principles reasoning about model behavior and reliability
  • Communication and ownership mindset

Benefits

  • Build applied AI for real enterprise use cases, not demos
  • Work on hard problems in reliability, automation, and AI governance
  • Join an in-person team in New York with high ownership
  • Direct customer impact in regulated financial services
  • Help define how autonomous agents operate safely in enterprise environments
  • Opportunity to shape emerging AI deployment practices
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Artian

Artian builds an agentic AI platform designed to automate complex financial workflows in regulated enterprise environments. The company is hiring software engineers to develop the platform's intelligence layer, core infrastructure, and customer-facing solutions that enable autonomous agents to execute business-critical operations reliably.

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Likely interview questions

  • Walk us through a production ML system you've built end-to-end. How did you approach evaluation and what were your main reliability challenges?
  • Describe your experience with LLMs or agentic systems. What techniques have you used to improve accuracy, latency, or failure handling?
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