twentyAI
AI Systems Engineer | VC-backed Startup | TWE46402

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Company
Join a venture-backed AI infrastructure startup building autonomous systems that improve the performance, efficiency and scalability of modern machine learning workloads. The team is developing next-generation optimisation technology that enables AI systems to become faster, more efficient and increasingly autonomous.
Working at the intersection of machine learning, systems engineering and high-performance computing, you'll help solve some of the most challenging performance problems in modern AI while contributing to technology already being adopted by enterprise customers.
Role
As an AI Infrastructure Engineer, you will build autonomous systems that optimise modern AI workloads. Working across GPU programming, machine learning infrastructure and automated optimisation techniques, you'll tackle complex performance challenges with real production impact.
Responsibilities
- Develop, optimise and deploy performance-critical software for modern AI workloads, including low-level GPU acceleration where appropriate.
- Own production machine learning infrastructure and AI systems from design through to deployment.
- Measure how low-level performance improvements affect end-to-end application efficiency.
- Build infrastructure that enables autonomous agents to run large-scale optimisation experiments, including compilation, validation, benchmarking, scoring and experiment tracking.
- Design automated optimisation and search strategies that continuously improve system performance.
- Investigate performance bottlenecks using profiling tools and translate findings into practical engineering improvements.
- Share technical knowledge through documentation, technical talks and open-source contributions where appropriate.
- For lead-level candidates, hire, mentor and develop a small team of high-calibre engineers and researchers.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Key Skills
- Strong experience developing performance-critical software for GPU-accelerated computing environments.
- Deep understanding of GPU performance optimisation, including memory access, parallel execution, register utilisation, occupancy and instruction scheduling.
- Experience optimising mixed-precision and quantised AI workloads, including formats such as INT4, INT8 and FP8.
- Understanding of distributed AI systems and performance optimisation techniques.
- Ability to diagnose and resolve performance bottlenecks using modern profiling tools.
- Experience with GPU programming technologies such as CUDA, Triton, CuTe, Helion or similar, with confidence working close to the hardware where required.
- Knowledge of modern GPU architectures and how performance characteristics vary across hardware generations.
- Practical understanding of transformer architectures, attention mechanisms, KV caching and how model design influences system performance.
- Experience with production AI training or inference frameworks such as vLLM, Megatron-LM or similar.
- Experience building performance-critical tooling such as compilers, profilers, auto-tuners, optimisation frameworks or developer tooling.
- Strong understanding of optimisation algorithms, automated search techniques or evolutionary methods.


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Desirable
- Contributions to open-source AI infrastructure or performance optimisation projects.
- Published research in machine learning systems, GPU optimisation, high-performance computing or related fields.
- Experience with AMD GPUs, Apple MLX, edge AI hardware or HPC environments.
- Familiarity with emerging GPU programming tools and compiler technologies.
- Experience building AI agent systems or autonomous software.
- Public technical work demonstrating expertise in machine learning systems, GPU programming or performance engineering through GitHub, blogs, benchmarks, conference talks or similar.
- Interest in advanced optimisation techniques, reinforcement learning, neuroevolution or related research areas.
Benefits
- Join a well-funded early-stage company with significant technical ambition and growing enterprise adoption.
- Take ownership of core technology and play a significant role in shaping the company's technical direction.
- For experienced candidates, the opportunity to build and mentor a high-performing engineering team.
Next Steps
If interested, please apply below or reach out directly isaac.salem@twentyai.com
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