Machine Learning Systems Engineer

This is a fully remote position, open to applicants in Ireland.

📋 Description

• Design, develop, and manage scalable backend services for a media intelligence platform, emphasizing clean, maintainable, and production-ready systems.

• Take ownership of essential backend components throughout the entire lifecycle, from system architecture and API contracts to implementation, deployment, monitoring, and iterative improvements.

• Influence architectural choices across APIs, processing pipelines, distributed computing, storage solutions, search functionalities, observability, cloud infrastructure, and model-serving processes.

• Create data models and storage strategies for media assets, generated metadata, embeddings, processing tasks, model outputs, search indexes, and audit trails.

• Develop high-throughput media ingestion and processing pipelines capable of handling large quantities of video, audio, image, and text content.

• Construct distributed, event-driven workflows for media processing utilizing queues and pub/sub systems such as SQS, Kafka, Pub/Sub, or similar technologies.

• Apply reliable asynchronous processing techniques, including retries, idempotency, dead-letter queues, backpressure management, and fault-tolerant job execution.

• Spearhead the development and enhancement of metadata extraction, content analysis, scene detection, transcription, embedding generation, and multimodal AI inference workflows.

• Integrate and fine-tune AI/ML services within backend processes, covering 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 evaluate models, analyze quality/latency trade-offs, and safely implement model upgrades.

• Enhance AI/ML inference workflows for latency, throughput, reliability, and cost-effectiveness across both real-time and batch-processing scenarios.

• Design and manage vector search and indexing systems utilizing technologies such as Pinecone, Weaviate, Qdrant, Elastic Vectors, FAISS, pgvector, or similar tools.

⛳️ Requirements

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.

• 5-7+ years of backend engineering experience, preferably in building scalable distributed systems, media platforms, data pipelines, or high-throughput backend services.

• Previous experience owning significant backend modules from start to finish, including architecture, implementation, deployment, monitoring, and production operations.

• 3+ years of experience integrating AI/ML inference systems into backend processes, covering model APIs, embedding pipelines, OCR, speech-to-text, scene detection, or multimodal model outputs.

• Practical experience in building AI-powered processing pipelines for image, video, audio, or text analysis.

• Hands-on experience with production model optimization, particularly for image, video, embedding, or multimodal models, including batching, caching, quantization, prompt optimization, routing strategies, latency reduction, and cost optimization.

• Previous experience with vector search, semantic search, media retrieval, or similarity-matching systems is highly preferred.

• Experience mentoring engineers, leading technical discussions, and shaping architectural decisions across backend, infrastructure, and AI/ML workflows.

🏝️ Benefits

• Competitive salary

• Flexible working hours

• Professional development budget

• Home office setup allowance

• Global team events