Machine Learning Systems Engineer

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

📋 Description

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

• Take ownership of essential backend components from system design and API contracts through to implementation, deployment, monitoring, and iterative improvements.

• Influence architectural decisions concerning APIs, processing pipelines, distributed computing, storage, search, observability, cloud infrastructure, and model-serving workflows.

• 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 for substantial volumes of video, audio, image, and text content.

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

• Implement robust asynchronous processing patterns, incorporating 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.

⛳️ Requirements

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

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

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

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

• Practical experience in creating AI-driven processing pipelines for analyzing images, videos, audio, or text.

• 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.

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

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

🏝️ Benefits

• Flexible working arrangements

• Professional development opportunities

• Collaborative global team

• Access to cutting-edge technologies

• Opportunities for career advancement