Member of Technical Staff (AI Inference Engineer)
Perplexity - London, UK, United Kingdom
Posted Apr 13, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
- Not verified
- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
- Not verified
- Relocation assistance
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- Childcare support
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- Learning budget
- Not verified
- Verification
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- Salary
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Market context
- U.S. role benchmark (BLS OEWS)
- $116,543 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +9.8% - Much faster than average
Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Role
Schedule
- Shift type
- Not verified
- Weekend work
- Not verified
Company
- Equity
- Offered From the posting source checked Jun 20, 2026
Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Member of Technical Staff (AI Inference Engineer) London, UK, United Kingdom We are looking for an AI Inference Engineer to join our growing team. We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL. Responsibilities: - New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. - GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. - Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. - Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernels interleaving. - Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents. Who we're looking for: - Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus. - You understand modern LLM architectures and are able to bring them up reliably in a production environment. - You've built and operated production distributed systems under real load - ideally performance-critical ones. - Comfortable working across languages and layers: Rust for the
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