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Astera

Talent Engineer

Emeryville HQ$140k–$160kfulltimemidAdded today

About this role

Astera seeks an AI-fluent Talent Engineer to design and operate intelligent workflows that modernize hiring operations. You'll build agentic systems using LLMs and automation tools to handle scheduling, communication, and pipeline management, while ensuring candidates receive thoughtful, high-quality experiences at scale.

What you'll do

  • Design and maintain AI agent workflows for scheduling, candidate communication, pipeline tracking, and reporting across the full hiring lifecycle
  • Configure and extend the ATS (Ashby/Rippling), integrate with LLM-based agents, and connect to calendars, email, and internal systems
  • Monitor end-to-end interview processes and personally step in where human judgment or empathy is required
  • Build dashboards and automated reporting on pipeline health, bottlenecks, and operational metrics for leadership
  • Partner with hiring managers to identify sourcing strategies and configure agentic outreach flows with human review checkpoints
  • Evaluate and pilot new AI tools, frameworks, and techniques against real hiring needs while maintaining safety guardrails

What they're looking for

  • AI agents and LLM prompt engineering
  • ATS platforms (Ashby, Rippling) configuration and extension
  • Workflow automation and integration (APIs, webhooks, integrations)
  • Data analysis and dashboard/reporting tools
  • Sourcing and talent research, including AI-assisted methods
  • Project management in ambiguous, fast-moving environments
  • Communication and stakeholder management
  • Understanding of hiring best practices and candidate experience
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Astera

Astera builds advanced infrastructure for large-scale distributed simulations, neuroscience research tools, and knowledge-sharing platforms aimed at accelerating scientific discovery. The company is hiring software engineers, ML researchers, and scientist-engineers to develop high-performance systems, specialized neuroscience instrumentation, and data-efficient machine learning approaches.

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Likely interview questions

  • Walk us through a time you designed an automation or system that handled repetitive work at scale—what was the outcome, and how did you handle edge cases?
  • Describe your experience with AI agents or LLMs in a production or operational context. What surprised you?
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