Aimling

Perplexity is looking for a Search Machine Learning Engineer Intern to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. You will work closely with experienced engineers to improve search quality, experiment with new models, and ship features that directly impact how users search and discover information. Internship program: 12 - 24 weeks, full-time, in-person in the London office. Responsibilities Contribute to experiments that improve search quality through better models, data usage, and evaluation tools, under the guidance of senior engineers. Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models. Train evaluating models (including LLM-based approaches) for retrieval, ranking, and classification tasks. Support deployment and monitoring of search and ranking models in a scalable and performant way. Help build and iterate on RAG pipelines for grounding and answer generation. Collaborate with Data, AI, Infrastructure and Product teams to deliver improvements quickly and learn best practices in production ML. Qualifications Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems. Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow, JAX) through academic, open source, or personal projects. Familiarity with evaluating model quality using offline metrics and/or A/B testing is a plus, but not required. Previous experience (internships, research, or significant projects) working on search, recommendation, or NLP is a plus, but not required. Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment

Aimling is seeking a Search Machine Learning Engineer Intern to enhance search technologies with a focus on retrieval and ranking. This full-time internship lasts 12-24 weeks and requires working in person in the London office. Your responsibilities include improving search quality through experiments, designing model components, and collaborating with cross-functional teams. Candidates should have a strong foundation in machine learning, experience with Python, and a keen interest in search technologies.

Aimling, located in Greater London, is seeking an AI Infrastructure Engineer to join their fast-growing team. The ideal candidate will work closely with inference and research teams to build, deploy, and optimize large-scale AI training and inference clusters using Kubernetes and Slurm. The role requires strong expertise in managing Kubernetes deployments, experience with Slurm, and proficiency in Python and C++. Successful candidates will play a critical role in ensuring efficient and reliable AI workloads.

Overview 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