Platform Engineer

Responsibilities:

• Build provider-independent agent loops, state management, streaming, context handling, recovery, and graceful degradation.
• Develop the learning data plane around agents: structured trajectories, feedback and outcome signals, offline datasets, lineage, privacy controls, and reliable links between an agent version and its behaviour.
• Create evaluation systems for task completion, tool use, long-horizon behaviour, safety, latency, cost, accessibility, and business outcomes.
• Build offline analysis and improvement loops that can surface failure clusters, grow golden datasets, and propose changes to prompts, tools, skills, context, retrieval, memory, policies, and model routing.
• Design experiment and versioning systems for comparing changes through replay, shadow traffic, canaries, or controlled rollouts, with clear promotion and rollback criteria.
• Explore where approaches such as preference learning, reward modelling, reinforcement learning, or continual learning can improve agent behaviorand recognize when a simpler evaluation or systems change is the better answer.
• Build detectors that identify regressions and opportunities across quality, reliability, cost, latency, and user adoption without mistaking noisy proxy metrics for real progress.
• Design typed tool interfaces and protocol-based execution across internal capabilities and customer-authorised services.
• Build connectors and credential flows with explicit read/write modes, permissions, auditability, and predictable failure behaviour.
• Develop memory extraction and retrieval while enforcing tenant, user, and project visibility boundaries.
• Build durable orchestration for long-running tasks, checkpoints, approvals, handoffs, timers, and human-in-the-loop interactions.
• Publish reliable APIs, event-driven interfaces, reusable libraries, and pluggable improvement strategies that work across different agent categories and enterprise deployments.
• Build and refine React product surfaces for agent configuration, live execution, traces, evaluations, approvals, and improvement workflows.
• Own full-stack product slices across UI state, streaming, typed APIs, backend services, and persistence, with attention to accessibility, responsiveness, and failure recovery.
• Translate powerful platform primitives into product interactions that enterprise users can understand and trust.
• Debug difficult failures across models, prompts, tools, state, data, experiments, APIs, and distributed services.
• Join customer conversations when a repeated requirement or production outcome reveals a missing horizontal capability.

Requirements:

• 5-12 years of software engineering experience. We will make exceptions for exceptional people in either direction.
• Strong backend and distributed-systems fundamentals, including typed API design, asynchronous workflows, persistence, reliability, and production debugging.
• Practical experience shipping LLM, agent, search, retrieval, recommendation, experimentation, workflow, or developer-platform systems into production.
• An empirical approach to AI quality: you can form a hypothesis, design a useful evaluation, interpret noisy evidence, and distinguish a real improvement from movement in a proxy metric.
• The ability to reason about retries, idempotency, partial failure, long-running state, concurrency, latency, cost, experiment design, and safe rollout.
• A security-minded approach to multi-tenant systems, identity, authorisation, credentials, tool execution, privacy, and auditability.
• Product judgment. You can find a durable abstraction behind a real requirement without generalising too early or freezing customer-specific behaviour into the platform.
• AI-native. You use coding agents and modern models as a force multiplier while still owning architecture, correctness, evidence, and operational outcomes.
• Clear communication and high EQ. You can work with Product, Infrastructure, customer engineering teams, and applied AI researchers without losing technical depth.
• A generalist mindset and comfort moving between libraries, services, data, experiments, integrations, evaluations, and product surfaces.
• Comfort building product-facing software. You can work in React and TypeScript when a capability needs a great interface, and you can reason about streaming state, accessibility, and end-to-end user experience.
• Comfort with startup ambiguity, fast feedback loops, and broad ownership.
• Experience with reinforcement learning, preference learning, reward modelling, post-training, continual learning, ranking, causal inference, or large-scale experimentation is valuable but not required. We care more about whether you can connect learning ideas to production evidence and reliable systems than whether you have used a particular technique.