Machine Learning Systems Engineer

Overview

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.Search, Indexing & Data Retrieval

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.