Research Engineer, Deepmind

Google Google · Big Tech · Mountain View, CA +1

Research Engineer at Google DeepMind focused on building and scaling intelligent agents and their evaluation frameworks. The role involves designing self-improving systems, deploying agents with enterprise customers, developing automated evaluation pipelines, and identifying model limitations to inform next-generation model development.

What you'd actually do

  1. Design and build the self-improving framework, creating the core scaffolding, diagnostic tools, and feedback loops that drive continuous quality improvement.
  2. Engage directly with enterprise customers, public sector teams, and academic partners to safely deploy, monitor, and scale intelligent agents in real-world environments.
  3. Build automated evaluation pipelines that accurately measure real-world agent performance and capture quality metrics far beyond standard lab benchmarks.
  4. Develop sophisticated learning generalization systems that extract and distill insights from individual customer deployments, scaling improvements across all agents.
  5. Identify and characterize foundational model limitations with empirical evidence, collaborating directly with GDM research teams to pioneer next-generation models.

Skills

Required

  • Python
  • software development
  • large-scale infrastructure
  • distributed systems

Nice to have

  • systems design
  • data analytics pipelines
  • data flows
  • Map Reduce
  • Hadoop
  • Spark
  • Flume
  • Hive
  • Impala
  • SparkSQL
  • BigQuery

What the JD emphasized

  • build the world's first general-purpose learning agent
  • measuring the intelligence of our prototypes
  • cutting edge AI agents
  • systems for agent testing
  • test new algorithms on robots
  • self-improving framework
  • continuously improve agent quality at scale
  • building the infrastructure to optimize and improve our products quickly and accurately
  • pioneering AI lab
  • advancing AI development
  • accelerate high-quality product innovation
  • ensure safety and ethics are always our highest priority
  • pushing the boundaries across multiple domains
  • achieve exceptional results through collective effort
  • safely deploy, monitor, and scale intelligent agents
  • accurately measure real-world agent performance
  • capture quality metrics far beyond standard lab benchmarks
  • sophisticated learning generalization systems
  • foundational model limitations
  • pioneer next-generation models

Other signals

  • building self-improving framework
  • deploying intelligent agents in real-world environments
  • automated evaluation pipelines
  • scaling improvements across all agents
  • pioneer next-generation models