Machine Learning Systems Engineer — Tether Operations
CHF 73'500 - 111'500
Tether Operations · Zürich, Zürich (ZH)
- Location
- Zürich
- Contract
- remote
- Posted
- 22 days ago
SalaryCHF 73'500 - 111'500
Role overview
Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution.
Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains.
By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost.
- Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution.
- Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains.
- 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.
Main 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.
Application process
- 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.
- only through our official channels.
- We do not use third-party platforms or agencies for recruitment unless clearly stated.
- All open roles are listed on our official careers page: https://tether.recruitee.com/
- Verify the recruiter’s identity.
- All our recruiters have verified LinkedIn profiles.
- If you’re unsure, you can confirm their identity by checking their profile or contacting us through our website.
- Be cautious of unusual communication methods.
Contacts
- Double-check email addresses.
Additional details
- Responsibilities Backend Architecture & System Ownership
- Implement reliable asynchronous processing patterns, including retries, idempotency, dead-letter queues, backpressure handling, and fault-tolerant job execution. AI/ML Integration & Model Workflows
- 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
- Ensure system reliability through logging, metrics, tracing, alerting, dashboards, operational runbooks, and incident-response best practices. Collaboration & Engineering Leadership
- Ensure code quality through testing, peer review, clear documentation, and maintainable implementation patterns. Education & Experience
- Experience mentoring engineers, leading technical discussions, and influencing architectural decisions across backend, infrastructure, and AI/ML workflows.
- 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
- Apply only through our official channels.
Notes and original content
- Implement reliable asynchronous processing patterns, including retries, idempotency, dead-letter queues, backpressure handling, and fault-tolerant job execution.
- AI/ML Integration & Model Workflows
- 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
- Ensure system reliability through logging, metrics, tracing, alerting, dashboards, operational runbooks, and incident-response best practices.
- Collaboration & Engineering Leadership
- Ensure code quality through testing, peer review, clear documentation, and maintainable implementation patterns.
- Education & Experience
- Technical Skills
- Strong experience with SQL/NoSQL databases, schema design, and data modeling
