Staff ML Systems Engineer - Media Intelligence in London

We are developing a highly scalable media intelligence platform that processes, analyzes, and structures large volumes of multimedia content across text, image, video, and audio. As a Senior Applied ML Engineer, you will architect and build the core backend systems that power media ingestion, processing workflows, metadata generation, AI-based analysis, semantic search, and retrieval across large media libraries.

We are looking for a Senior Applied ML Engineer who can design, implement, optimize, and evaluate a production-grade moderation pipeline using open-source models. This role requires deep backend engineering expertise, strong system design capability, and practical experience integrating AI/ML systems into production workflows. You will work on complex media-processing pipelines, video/audio analysis, OCR, speech-to-text, embedding generation, vector search, multimodal model integrations, and high-throughput asynchronous workloads. You will collaborate closely with engineering leadership to define backend architecture, improve reliability and scalability, and guide other engineers in delivering secure, observable, and high-performance systems.

Responsibilities

• Backend Architecture & System Ownership
  • Architect, build, and operate scalable backend services for a media intelligence platform, with a focus on clean, maintainable, and production-ready systems.
  • Own critical backend components end to end, from system design and API contracts through implementation, deployment, monitoring, and iteration.
  • Drive architectural decisions across APIs, processing pipelines, distributed compute, storage, search, observability, cloud infrastructure, and model-serving workflows.
  • Design data models and storage patterns for media assets, generated metadata, embeddings, processing jobs, model outputs, search indexes, and audit trails.
  • Design high-throughput media ingestion and processing pipelines for large volumes of video, audio, image, and text content.
  • Build distributed, event-driven workflows for media processing using queues and pub/sub systems such as SQS, Kafka, Pub/Sub, or equivalent technologies.
  • Implement reliable asynchronous processing patterns, including retries, idempotency, dead-letter queues, backpressure handling, and fault-tolerant job execution.
• AI/ML Integration & Model Workflows
  • Lead the development and optimization of metadata extraction, content analysis, scene detection, transcription, embedding generation, and multimodal AI inference workflows.
  • Integrate and optimize AI/ML services within backend workflows, including model APIs, embedding pipelines, OCR, speech-to-text, scene analysis, multimodal inference, batching, caching, and fallback strategies.
  • Collaborate with ML engineers, data scientists, or external model providers to benchmark models, compare quality/latency trade-offs, and safely roll out model upgrades.
• Model Serving & Performance Optimization
  • Optimize AI/ML inference workflows for latency, throughput, reliability, and cost across both real-time and batch-processing paths.
  • Work with model-serving systems such as vLLM, Triton, TGI, SageMaker, Vertex AI, or custom inference services to improve batching, concurrency, warmup behavior, timeout handling, autoscaling, and GPU utilization.
  • Evaluate and apply practical model optimization techniques such as quantization, model distillation, batching, caching, prompt optimization, and routing to smaller or cheaper models where appropriate.
  • Design and maintain vector search and indexing systems using technologies such as Pinecone, Weaviate, Qdrant, Elastic Vectors, FAISS, pgvector, or similar tools.
  • Build retrieval workflows that support semantic search, similarity matching, duplicate detection, media discovery, and structured metadata search.
  • Monitor model and system performance in production, including API latency, queue depth, processing time, model error rates, GPU utilization, confidence distributions, drift signals, and cost per processed item.
• Infrastructure, Reliability & Observability
  • Deploy and operate systems on AWS, GCP, Azure, or equivalent cloud platforms, including compute, storage, networking, queues, model-serving infrastructure, and monitoring systems.
  • Ensure system reliability through logging, metrics, tracing, alerting, dashboards, operational runbooks, and incident-response best practices.
• Collaboration & Engineering Leadership
  • Collaborate with product, design, data, and ML teams to deliver media-rich, AI-powered product features.
  • Mentor junior and mid-level engineers, support technical planning, review designs, and raise engineering quality across the team.
  • Participate in code reviews, documentation, technical planning, and continuous improvement of engineering practices.
  • Ensure code quality through testing, peer review, clear documentation, and maintainable implementation patterns.

Education & Experience

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
• 5–7+ years of backend engineering experience, ideally building scalable distributed systems, media platforms, data pipelines, or high-throughput backend services.
• Prior experience owning major backend modules end to end, including architecture, implementation, deployment, monitoring, and production operations.
• 3+ years of experience integrating AI/ML inference systems into backend workflows, including model APIs, embedding pipelines, OCR, speech-to-text, scene detection, or multimodal model outputs.
• Hands-on experience creating AI-powered processing pipelines for image, video, audio, or text analysis.
• Practical experience with production model optimization, especially for image, video, embedding, or multimodal models, including batching, caching, quantization, prompt optimization, routing strategies, latency reduction, and cost optimization.
• Prior experience with vector search, semantic search, media retrieval, or similarity-matching systems is strongly preferred.
• Experience mentoring engineers, leading technical discussions, and influencing architectural decisions across backend, infrastructure, and AI/ML workflows.

Technical Skills

• Strong expertise in Python and/or Node.js with deep understanding of building scalable RESTful APIs and backend architectures.
• Experience with HuggingFace transformers ecosystem and deep learning frameworks such as PyTorch and TensorFlow.
• Strong experience with SQL/NoSQL databases, schema design, and data modeling.
• Preferred exposure to distributed systems, microservices, asynchronous processing, and event-driven patterns with SQS, Pub/Sub, Kafka, or other queueing/pub-sub systems.
• Experience deploying production systems on AWS, GCP, or similar cloud platforms.
• Knowledge of infrastructure patterns (compute, storage, networking, observability).

AI/ML Integration

• Experience orchestrating embedding generation, scene detection, OCR, speech-to-text, image classification, video analysis, and multimodal model integrations.
• Experience optimizing inference workflows for latency, throughput, reliability, and cost.
• Experience working with scalable and optimized inference settings, including tuning sampling parameters, managing output-length formats, and configuring reasoning-related behaviors.
• Familiarity with practical model optimization techniques such as batching, caching, quantization, model distillation, prompt optimization, fallback routing, and use of smaller models where appropriate.
• Experience working with model-serving systems such as vLLM, Triton, TGI, SageMaker, Vertex AI, or custom inference services is preferred.
• Experience working with LLM and multi-modal evaluation and benchmarking frameworks and domain-specific benchmarks with the ability to interpret results and optimize model performance accordingly.

System Design & Architecture

• Preferred understanding of distributed systems, scaling patterns, and performance engineering.
• Ability to design modular, maintainable, and efficient architectures.
• Experience with API versioning, modularization, and designing long-running workflows.
• Understanding of performance bottlenecks and low-latency backend patterns.