Senior Machine Learning Engineer

Skip to main contentThis website uses cookies to improve your web experience. By using the site, you agree to the use of cookies. Privacy PolicyDeclineAccept CookiesEnglishSign InSenior Machine Learning Engineer page is loadedSenior Machine Learning EngineerApplyremote typeHybridlocationsMelbourne, AustraliaRemote, New South Wales, Australiatime typeFull timeposted onPosted Todayjob requisition idR35189Job DescriptionThe Mission: The enterprise software landscape is fracturing. We are transitioning from "Systems of Record" to "Systems of Action." Zendesk is leading this shift with the Resolution Platform, aiming to be the first CX company to secure $1B in AI-driven revenue. To achieve this, we must solve the "Orchestration Trap." We are not just building features; we are building the high-performance runtime that allows fleets of AI Agents to Perceive, Reason, and Act.The Opportunity: You will be a key Staff-level architect building the "Digital Nervous System" that allows probabilistic LLMs to function as highly reliable, deterministic digital employees. This role is strictly dedicated to infrastructure, distributed state management, and multi-tenant scaling. You will partner with our AI Scientists who focus on model reasoning, cognitive architecture, and prompt engineering while you own the robust execution environments that allow their agents to operate safely in production.What You Will ArchitectMulti-Tenant Durable Agent Runtime: You will move beyond basic loops to architect fault-tolerant execution environments based on the principles of durable execution: incremental execution, state persistence, and fault tolerance via automated recovery. You will solve the complex challenges of long-running workflows, ensuring that transient network failures or server restarts never result in lost progress or phantom state crashes.Enterprise Memory Functions: Infinite context windows are a myth. You will design convergent, high-throughput memory infrastructures to support the working, episodic, semantic, and procedural memory tiers of our agents. This requires building state management systems capable of ultra-low latency retrieval and strict ACID transactions to maintain data integrity across thousands of concurrent sessions.Multi-Tenant Resource Isolation: You will ensure strict logical boundaries across shared infrastructure pools. This includes implementing zero-trust execution validation and native Row-Level Security within the database layer to completely isolate tenant context, preventing any possibility of cross-tenant data leakage.The Integration Fabric (MCP): You will drive our adoption of the Model Context Protocol (MCP), building the secure servers and clients that allow agents to dynamically discover tools, negotiate capabilities, and lay the groundwork for secure Agent-to-Agent (A2A) collaboration.Platform FinOps & Observability: You will establish the telemetry required to trace agent actions back to specific tenants, enforcing rate limits and optimizing the compute cost-per-token to ensure the unit economics of the platform remain highly viable.Who You AreStaff-Level Systems Thinker: You operate beyond tactical, day-to-day coding. You identify systemic architectural bottlenecks, define the technical direction for the platform, and mentor engineering teams on how to build for the age of agentic AI.Architectural Depth: You have extensive backend experience (Java, Go, or Python) and deeply understand the nuances of managing distributed state, event-driven architectures (Kafka), and non-deterministic execution in cloud environments.Reliability Expert: You understand that reliability in an AI system isn't just about uptime; it's about externalizing state, safe self-correction, and deterministic recovery.Protocol Aware: You are excited by open standards like MCP and understand the extreme complexities of API design, capability negotiation, and secure sandboxing for tool execution.Tech StackLanguages: Go/Java (Platform...