Zendesk

2 days ago

Senior Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Build and harden the Resolution Platform’s agent runtime that lets AI agents perceive, reason, and act to autonomously resolve Zendesk customer service tickets. Work on large-language-model planning, multi-agent coordination, memory structures, evaluation systems with regression detection, and enterprise guardrails against misuse, prompt injection, and hallucination loops. Own both the research and systems needed to develop domain-specialized models via RL and production feedback loops.

Location: Melbourne, New South Wales

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design and advance agent architectures for planning, tool dispatch, memory, and error recovery used to resolve customer service tickets without a human in the loop.
•Improve multi-agent coordination, including agent delegation patterns using the Agent-to-Agent (A2A) protocol.
•Develop and own reinforcement learning infrastructure for domain-specialized customer service resolution models, including reward curricula and feedback loops.
•Harden automated evaluation systems with multi-turn analysis and trajectory insights, integrating quality gates into CI.
•Implement enterprise guardrails (supervisor patterns, capabilities-based access control, and output validation) to mitigate tool misuse, prompt injection, and hallucination loops at scale.

Key Requirements

•5+ years building production ML/AI systems with hands-on experience in agent architectures (planning, tool dispatch, memory, error recovery).
•Strong evaluation instincts: build internal evals to close gaps between public benchmarks and production performance.
•Optional: depth in RL for language models, including reward shaping and online/offline tradeoffs.
•Python and PyTorch fluency, with familiarity with at least one agent framework and good judgment on when to build custom.

Tech Stack:PythonPyTorchLangChainLarge language modelsMulti-agent coordinationRLReward shapingBraintrustA2A (Agent-to-Agent protocol)CI

Company Brief

Zendesk

Provides cloud-based customer service and engagement software, including help desk, ticketing, chat, and CRM tools that enable businesses to manage customer support, self-service, and omnichannel communication at scale.

Industry: Enterprise Software

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Valuation: Decacorn (USD 10B+)

Funding: Private Equity Backed

Headquarters: San Francisco, United States

Founded: 2007

Glassdoor: 4.0

Website LinkedIn Glassdoor

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Zendesk

2 days ago

Senior Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Location: Melbourne, New South Wales

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design and advance agent architectures for planning, tool dispatch, memory, and error recovery used to resolve customer service tickets without a human in the loop.
•Improve multi-agent coordination, including agent delegation patterns using the Agent-to-Agent (A2A) protocol.
•Develop and own reinforcement learning infrastructure for domain-specialized customer service resolution models, including reward curricula and feedback loops.
•Harden automated evaluation systems with multi-turn analysis and trajectory insights, integrating quality gates into CI.
•Implement enterprise guardrails (supervisor patterns, capabilities-based access control, and output validation) to mitigate tool misuse, prompt injection, and hallucination loops at scale.

Key Requirements

•5+ years building production ML/AI systems with hands-on experience in agent architectures (planning, tool dispatch, memory, error recovery).
•Strong evaluation instincts: build internal evals to close gaps between public benchmarks and production performance.
•Optional: depth in RL for language models, including reward shaping and online/offline tradeoffs.
•Python and PyTorch fluency, with familiarity with at least one agent framework and good judgment on when to build custom.

Tech Stack:PythonPyTorchLangChainLarge language modelsMulti-agent coordinationRLReward shapingBraintrustA2A (Agent-to-Agent protocol)CI

Company Brief

Zendesk

Industry: Enterprise Software

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Valuation: Decacorn (USD 10B+)

Funding: Private Equity Backed

Headquarters: San Francisco, United States

Founded: 2007

Glassdoor: 4.0

Website LinkedIn Glassdoor