FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing advanced memory structures and reinforcement learning mechanisms for AI agents, with a strong focus on production ML/AI systems and agent architectures. Proficient in Python and PyTorch, with the ability to integrate quality gates and manage training infrastructure effectively.
Highest-signal resume keywords
Reinforcement LearningPython FluencyPyTorch ProficiencyAgent Architecture ExperienceEvaluation Instincts
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Memory Structures DesignAutonomous Learning MechanismsMulti-Agent DelegationReward Curricula DevelopmentAutomated Trajectory AnalysisError RecoveryCustom Solution DevelopmentEvaluation BuildingQuality Gates IntegrationRegression Detection
Soft Skills
CollaborationJudgment
Tools & Technologies
ZendeskCI/CD SystemsAgent Frameworks
Industry Keywords
Production ML/AI SystemsCustomer Service ResolutionAmbiguous-Goal DecompositionMemory-Tier ManagementCapabilities-Based Access Control
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Design advanced memory structures and autonomous learning mechanisms for AI agents
- Push agent architecture forward through improved ambiguous-goal decomposition, memory-tier management, selective skill acquisition, and multi-agent delegation via A2A
- Build domain-specialized agent models for customer service resolution using reinforcement learning on production trajectories
- Own reinforcement learning training infrastructure, including reward curricula, rollout systems, and feedback loops
- Develop multi-turn evaluation and automated trajectory analysis
- Integrate quality gates and regression detection into CI to block deployments when agent performance drops
- Implement multi-layered enterprise-scale guardrails, including supervisor patterns, capabilities-based access control, and output validation
- Collaborate in Zendesk's hybrid working environment to connect, collaborate, and learn
Requirements
What you’ll need- 5+ years building production ML/AI systems
- Hands-on experience with agent architectures, including planning, tool dispatch, memory, and error recovery
- Strong evaluation instincts and experience building internal evaluations
- Python and PyTorch fluency
- Familiarity with at least one agent framework
- Judgment to know when to build custom solutions
- Experience with or genuine depth in reinforcement learning for language models is optional, including reward shaping, online/offline tradeoffs, and reward hacking as a diagnostic signal
Benefits
Comp & perks- Hybrid way of working, with flexibility to work remotely for part of the week
- Inclusive workplace experience
- Reasonable accommodations for applicants with disabilities and disabled veterans