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Science & Research Jobs in United Kingdom

Browse 13 open roles in Science & Research in United Kingdom. Employers hiring now include Anthropic, Perplexity and Scale AI. The latest role was posted 6 days ago.

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Perplexity

FullTime

Engineering Site Lead

Perplexity is revolutionizing how people discover and interact with information through AI-powered search and knowledge tools. As we expand our global footprint, we're establishing a strategic presence in London to drive innovation and growth across Europe. The Role: We're seeking an exceptional Site Lead to establish and scale our London office. This is a unique opportunity to shape Perplexity's presence in one of the world's leading tech hubs, building teams and culture from the ground up while driving technical excellence in infrastructure and AI systems. As Site Lead, you'll serve as the face of Perplexity in London, responsible for building our technical organization, fostering a world-class engineering culture, and directly managing one or more infrastructure teams. You'll report to senior leadership and work cross-functionally with teams across our global footprint. The individual in this role will manage teams in LON themselves while also facilitating. Responsibilities: Site Leadership & Culture Establish and lead Perplexity's London office, setting the cultural foundation and operating principles Build a collaborative, high-performance engineering culture that aligns with Perplexity's values while embracing the strengths of the London tech ecosystem Serve as the primary point of contact for all London-based activities and represent the site in company-wide strategic discussions Partner with People/HR, Finance, and Operations to ensure seamless site operations Drive local community engagement, partnerships, and Perplexity's brand presence in the London and European tech community Technical Leadership: Directly manage and mentor one or more infrastructure or AI infrastructure teams in London (5-15+ engineers) Set technical direction and strategy for London-based infrastructure initiatives in alignment with company-wide goals Drive architectural decisions and technical excellence across teams Ensure robust systems for deployment, monitoring, scalability, and reliability of infrastructure supporting AI/ML workloads Collaborate with engineering leaders globally to align on technical standards, best practices, and cross-site initiatives Team Building & Talent: Build and scale high-performing infrastructure and AI infrastructure teams through strategic hiring Develop and execute talent acquisition strategy for the London site in partnership with recruiting Create career development frameworks and growth opportunities for engineers Foster technical mentorship and knowledge sharing across teams and sites Cross-functional Collaboration: Partner with Product, Engineering, and Research teams globally to understand infrastructure needs and deliver solutions Coordinate with other site leads and engineering leaders to ensure effective cross-site collaboration Contribute to company-wide infrastructure strategy and roadmap planning Facilitate knowledge transfer and best practice sharing across global teams Qualifications: Required 10+ years of experience in software engineering with 5+ years in infrastructure, cloud infrastructure, or AI infrastructure roles 3+ years of people management experience, including building and scaling teams Proven track record of establishing or significantly growing an engineering site or office Deep technical expertise in distributed systems, cloud platforms (AWS, GCP, or Azure), and infrastructure automation Experience with infrastructure supporting large-scale AI/ML systems, including: GPU infrastructure and orchestration ML training and inference pipelines Model serving and deployment at scale Strong understanding of modern infrastructure technologies: Kubernetes, Terraform, container orchestration, CI/CD systems Demonstrated ability to set technical vision and drive execution across multiple teams Excellent communication and stakeholder management skills Experience working in fast-paced, high-growth technology companies Passion for building inclusive, diverse, and high-performing teams Preferred Experience at companies focused on AI/ML, search, or large-scale consumer applications Previous experience as a site lead, office lead, or similar multi-team leadership role Background in building infrastructure for LLM training or inference Contributions to open-source infrastructure or AI infrastructure projects Experience scaling teams from 0 to 20+ engineers Active involvement in the London or European tech community MBA or advanced technical degree What Success Looks Like: 30 Days Deep understanding of Perplexity's infrastructure, technology stack, and organizational structure Established relationships with key stakeholders across engineering, product, and leadership Initial hiring plan and culture strategy for London site established Help the Search, API, AI and Infra teams build out their hiring pipelines 90 Days Core infrastructure team established and ramping in London Clear technical roadmap and priorities defined for London-based teams Site culture and operating rhythms established (team meetings, all-hands, cross-site syncs) London office actively participating in company-wide infrastructure initiatives 1 Year London site operating as a high-functioning hub with 15-30+ engineers Infrastructure teams delivering measurable impact on system reliability, performance, and scalability Strong talent brand established in London market with healthy hiring pipeline London recognized internally as a strategic site contributing to Perplexity's technical leadership Why Join Perplexity Ground-floor opportunity to build and lead a strategic site for a fast-growing AI company Work on cutting-edge AI infrastructure challenges at massive scale Shape the culture and technical direction of an entire office Competitive compensation including equity Comprehensive benefits package Flexible work environment Opportunity to make a significant impact on how millions of people access and interact with information Location: London, United Kingdom (Hybrid) Perplexity is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

London
3 months ago
Sector

Science & Research

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Perplexity

FullTimeremote

Member of Technical Staff (AI Infrastructure Engineer)

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters. RESPONSIBILITIES Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands QUALIFICATIONS Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization REQUIRED SKILLS Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource management, and cluster configuration Python and C++ programming with focus on systems and infrastructure automation Hands-on experience with ML frameworks such as PyTorch in distributed training contexts Strong understanding of networking, storage, and compute resource management for ML workloads Experience developing APIs and managing distributed systems for both batch and real-time workloads Solid debugging and monitoring skills with expertise in observability tools for containerized environments PREFERRED SKILLS Experience with Kubernetes operators and custom controllers for ML workloads Advanced Slurm administration including multi-cluster federation and advanced scheduling policies Familiarity with GPU cluster management and CUDA optimization Experience with other ML frameworks like TensorFlow or distributed training libraries Background in HPC environments, parallel computing, and high-performance networking Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices Experience with container registries, image optimization, and multi-stage builds for ML workloads REQUIRED EXPERIENCE Demonstrated experience managing large-scale Kubernetes deployments in production environments Proven track record with Slurm cluster administration and HPC workload management Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure Experience supporting both long-running training jobs and high-availability inference services Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

London
7 months ago
Sector

Science & Research

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Perplexity

FullTime

Member of Technical Staff (AI Inference Engineer)

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 serving runtime, Python for model code, CUDA/CuteDSL for kernels. You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday. Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you. NICE-TO-HAVE: ML compilers and framework internals: PyTorch internals, torch.compile, custom operators. Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism. Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving. Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis. Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads. QUALIFICATIONS: 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems. Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow). Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores). Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation). Final offer amounts are determined by multiple factors including experience and expertise. Equity: In addition to the base salary, equity may be part of the total compensation package.

London
7 months ago
Sector

Science & Research

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