Artificial Intelligence Research Engineer

Paradigm Talent

London

4 months ago

Applications closed

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Role: Staff Software Engineer (Rust & AI Inference) Location: London (23 days a week onsite)
Compensation: Up to 160,000 + equity

Were supporting an emerging deep tech company building high-performance infrastructure to support AI model deployment at scale. Theyre focused on enabling enterprises to run advanced AI systems in production, with a strong emphasis on privacy, performance, and full-stack control.

This is a hands-on senior IC role for an engineer who thrives in low-latency, compute-intensive environments, and enjoys mentoring others. Youll play a key role in shaping the core inference layer powering complex real-time AI workflows.

If you're excited by the challenge of optimising distributed systems, designing for reliability, and scaling cutting-edge AI applications, this could be the role for you.

You'll be:
Building scalable, low-latency LLM inference infrastructure
Optimising performance with caching, quantisation, and speculative decoding
Contributing to core systems in Rust (we are happy for individuals to lean this on the job)
Designing distributed GPU orchestration and inference servers
Mentoring engineers and influencing technical direction across the team

You should bring:
~5+ years software engineering experience, with deep backend/system-level experience
~ Strong coding skills in a typed systems languages like C++ / Go / Rust (would also consider Python)
~ Familiarity with Kubernetes, and cloud infra
~ A strong engineering mindset with a bias for clean abstractions, reliability, and performance

Bonus points for:
Experience deploying open-source LLMs or VLMs in production
Experience with ML inference systems, PyTorch, Triton, or CUDA kernels
Background in document intelligence, enterprise search, or NLP pipelines
Prior exposure to multi-agent systems or complex orchestration workflows

This is a high-impact, high-autonomy role in a technically elite team thats pushing the boundaries of whats possible in AI infrastructure.

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