Machine Learning Systems Engineer

🕒 May 28

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Tether.to

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

₿ Crypto

💳 Fintech

💸 Finance

Crypto • Fintech • Finance

Tether. to is a leading digital asset company that pioneers the use of stablecoins in the blockchain space. As the most widely adopted stablecoin, Tether tokens are designed to be pegged 1-to-1 with fiat currencies, offering a stable digital asset option for users. The platform facilitates these token transactions across multiple blockchains, enhancing cross-border transactions while maintaining transparency with daily records of total assets and reserves. Tether's initiatives include educational programs promoting digital asset usage, especially targeting regions like the Middle East, Turkey, and the Philippines. Tether thus positions itself as a disruptor in the traditional financial system by enabling a stable, efficient method of handling transactions in the digital currency world.

📋 Description

• 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. • 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. • 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. • 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. • 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.

🎯 Requirements

• 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.

🏖️ Benefits

• Competitive salary • Flexible working hours • Professional development opportunities • Remote work options