Research Engineering Manager, AI Agent Harness; Product R&D

THE TECHNICAL CHALLENGEKEY RESPONSIBILITIES

• Set the technical direction of the harness and the interfaces between its levels of abstraction
• Establish the evaluation regime and the fortnightly score, and hold the team to measured results
• Run the R&D team on a two-week rhythm: planning, review with working software, retrospective, written post-mortems for failed experiments and incidents
• Coach, level and hire the engineers; interface with product management on the backlog and with the platform team on what the harness exposes

• Managed both a research-cadence team and a sprint-cadence team, and can articulate the interface between them
• Built an agent harness or an evaluation harness for computer-use systems, agents or reinforcement learning, with an evaluation culture recognised outside the team, and can say which abstraction was right and which was wrong
• Has taken apart the harnesses of coding agents (Open Hands, SWE-agent, Aider, Deep Seek Harness or comparable) and can explain how they work in plain terms
• Has fine-tuned an LLM, from data preparation to evaluation on a held-out set

• T-shaped: deep in one domain, with working breadth in a neighbouring one
• Structures a large, incompletely specified problem and drives it to a working result independently
• Managed a team building on LLMs or machine-learning models in production, with evaluation pipelines as a management requirement and a clear account of how quality was gated
• Turns research or prototype code into reference implementations others reuse, using AI coding tools daily and verifying their output

• Product engineering: we own what we build and run it in production
• Small teams, two-week cycles, working software at every review
• AI coding tools are part of the standard workflow

• AI-augmented engineering environment
• Access to on-premise Nvidia B200s
• Flexible work environment

• Practical session; the format is agreed with you

In coding exercises, AI tools are allowed and expected. No Leet Code.

New product organisation as part of a large semi-government in Abu Dhabi. International, ex-FAANG team. Completely greenfield, with a modern tech stack.

• Clear written and spoken English; has reported technical progress to non-technical executives on a fixed cadence
• 6+ years in engineering, of which 2+ leading a team of engineers where work was accepted on measured results
• Still coding, able to review Type Script and Python; own code operated in production at a product company or a startup
• Bachelor's degree in any field, or self-taught with a track record of open-source contributions

• Agent harnesses and frameworks:
  Open Hands, SWE-agent, Aider, Deep Seek Harness, Hermes Agent, Lang Graph, smolagents
• Programmatic prompt and pipeline optimisation: DSPy, Text Grad, Ax
• Evaluation and observability: evaluation harnesses, LLM-as-judge, trajectory evaluation, Inspect, Langfuse, Open Telemetry; benchmarks such as Online-Mind2
  
  Web, Web Arena, OSWorld
• Models and serving: open-weight LLMs, vLLM, SGLang
• State and orchestration: state machines, event sourcing, XState, Trigger.dev, Temporal, Kubernetes, Type Script, Python