Why this role stands out
This role offers significant impact by building and scaling the core evaluation framework for cutting-edge AI agents, providing direct influence on product development and research direction. You will thrive here if you enjoy developing robust, high-performance systems and collaborating with diverse technical teams in a hybrid London environment. Join a reputable company at the forefront of AI innovation and contribute to shaping the future of computer-use agents.
Quick overview
Job description
About H:
When we released Holo4 on 28 September, we published every trajectory behind its public benchmark scores at trajectories.hcompany.ai. For OSWorld that's 369 desktop tasks, each run 3 times, with the steps, tokens and time of every attempt. This role builds and runs the evaluation framework that produces runs like these.
H builds computer-use agents and the models behind them. Developers use them through a managed API, and our forward deployed engineers take them into enterprise workflows.
What this team owns
The evaluation framework: orchestration, runtimes and observability. Researchers and forward deployed engineers bring the benchmarks, across web apps, desktop applications and the command line. Your job is to make the framework that runs them reliable, fast and cheap, and to make adding a new one quick. Research uses the results to choose checkpoints and decide whether a model ships. Product and the forward deployed engineers use them to measure agents on customer workflows. It carries roughly 50 benchmarks now. That number should be between 100 and 200 soon, and the framework has to keep up.
What you'd be doing
Integration support for researchers and forward deployed engineers bringing in a benchmark, with a shorter path each time.
Setting the standard for how a benchmark enters the framework, and building the checks that enforce it.
Scheduling and observability, so cluster capacity isn't left idle while evaluation jobs queue.
Reproducible results across trials, so a release decision rests on numbers that hold.
Whatever stack a benchmark calls for. One week that's cluster tuning; the next it's a browser extension or desktop environments.
Time with customers, from single developers to large companies, to find out what they want measured, then automating it so the results flow back into our harnesses and models.
The first few months
By 3 months you'll have helped researchers or forward deployed engineers integrate 5 benchmarks, and started fixing what slows the framework down. By 6 months one part of it is yours, for example scaling the runs, observability, or a group of related benchmarks, and a release will have gone out on your numbers. By 12 months you'll know the design and trade-offs of the whole evaluation system, and be the person the rest of H asks about evaluations.
Who you'd work with
Ceiran Chapman, our VP Engineering, is hiring for this role. You'd join the evaluation team. The people relying on your work day to day are H's researchers and forward deployed engineers.
What we think it takes
Likely a good fit if you
Have spent 5+ years in backend development, with production Python at the core, and use coding agents to go faster without letting quality drop.
Have built test, QA or evaluation tooling that other teams depended on, and care whether a number is right.
Have operated distributed systems on Kubernetes in a public cloud. AWS experience helps most.
Have built and shipped systems end to end, including APIs (REST or GraphQL) and integrations with outside services.
Know relational and non-relational databases, and message queues such as SQS, RabbitMQ or Kafka.
Instrument what you build, with metrics, tracing and monitoring from the start.
Stronger still if you have
Measured LLM quality before, or built agents yourself.
Packaged and run workloads in Docker and on virtual machines.
Used Temporal, Dask, FastAPI, PostgreSQL, Grafana or Datadog.
Automated web or desktop software with Playwright, Selenium or a browser extension you wrote.
Set standards other engineers follow, through code review, design review or mentoring.
You do not need a background in machine learning. We'll work that out with you. If you match most of this but not all of it, apply anyway.
How we hire
A 30 minute call with our Talent team, a 60 minute technical challenge, a 60 minute system design interview, and a 30 minute final conversation with Ceiran. About 3.5 hours in total.
Practicalities
London posting: Hybrid in London. That means 3 office days a week and a Paris trip about once every 4 to 6 weeks. There is a Paris posting for the same role. We offer a competitive package.
Similar jobs
- TA

Staff Software Engineer, Inference / Compute Infrastructure Engineering London or AmsterdamNew Auto Apply
Together AI · London & Amsterdam · Hybrid 🇬🇧
RustAnsibleCUDA+5TechnologySalary not listed11 hours ago - SA

Sr. Software Engineer, Cloud PlatformNew Auto Apply
Samsara · Remote - London; United Kingdom · Remote 🇬🇧
AWSIoTJava+4TechnologySalary not listed8 hours ago - ZO

Senior C# Software Engineer – London Auto Apply
Zopa · London · Hybrid 🇬🇧
DockerAWSC#+4TechnologySalary not listed8 months ago - ZO

Senior Java/Kotlin Software Engineer - London Auto Apply
Zopa · London · Hybrid 🇬🇧
InstagramTechnologySalary not listed1 year ago - ZO

Java/Kotlin Software Engineer - Manchester Auto Apply
Zopa · Manchester · Hybrid 🇬🇧
InstagramTechnologySalary not listed1 year ago - ZO

Java/Kotlin Software Engineer - London Auto Apply
Zopa · London · Hybrid 🇬🇧
InstagramTechnologySalary not listed2 years ago - WB
Software Engineer - Data Infrastructure Auto Apply
Woven By Toyota · London · Hybrid 🇬🇧
AWSBigQueryADAS+4TechnologySalary not listed1 week ago - WB
Software Engineer - Calibration Auto Apply
Woven By Toyota · London · Hybrid 🇬🇧
ADASAutonomous DrivingTechnologySalary not listed5 weeks ago - WB
Senior Software Engineer, Embedded Systems Auto Apply
Woven By Toyota · London · Hybrid 🇬🇧
Embedded SystemsADASAutonomous Driving+1TechnologySalary not listed2 months ago - WB
Staff Software Engineer - Data Infrastructure Auto Apply
Woven By Toyota · London · Hybrid 🇬🇧
AWSBigQueryADAS+4TechnologySalary not listed2 months ago - WM
Forward Deployed Engineer, Data & AI Auto Apply
Wmg · London · Remote 🇬🇧
SnowflakeDatabricksPython+2EngineeringSalary not listed2 months ago - SP
Staff Engineer - Content Platform Auto Apply
Spotify · London · Hybrid 🇬🇧
TechnologySalary not listed5 weeks ago