Staff ML Systems Engineer Media Intelligence

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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. Transparency is the bedrock of everything we do, ensuring trust in every transaction.

Innovate with Tether

Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services.

But that’s just the beginning:

Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities.

Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET, our flagship app that redefines secure and private data sharing.

Tether Education: Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity.

Tether Evolution: At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.

Why Join Us?

Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry.

If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.

Are you ready to be part of the future?

About the job

We are building a highly scalable media intelligence platform that processes, analyzes, and flags potentially problematic content across large volumes of video, audio, image, and text. The platform powers content safety workflows for Tether Data products, including Keet, and must operate reliably at scale across diverse languages, formats, and content types.

As a Staff ML Systems Engineer, you will own the core of this platform - from ingestion and async processing pipelines through AI/ML model integration, inference optimization, vector search, and structured report generation. This is not a research role and it is not a prompt-engineering role. It is a production engineering role where the models are one component of a larger system that you are responsible for making fast, reliable, cost-efficient, and maintainable.

You will be the senior technical owner of the media intelligence backend. That means you define the architecture, make the hard tradeoff calls, mentor other engineers, and carry responsibility for the system in production. You will work closely with engineering leadership and collaborate with ML researchers, data engineers, and product teams to deliver a platform that provides actionable, timestamped findings to human reviewers at scale.

Responsibilities

Backend Architecture & System Ownership

• Design and operate scalable backend services for media ingestion, processing, and report generation - clean, well-tested, and built for horizontal scaling from day one

• Own API contracts, data models, and storage patterns for media assets, processing jobs, model outputs, embeddings, and audit trails

• Build high-throughput async processing pipelines for video, audio, image, and text using queues and event-driven patterns (SQS, Kafka, Pub/Sub, or equivalent)

• Implement reliable asynchronous processing with retries, idempotency, dead-letter queues, backpressure handling, and graceful degradation

AI/ML Integration & Model Workflows

• Integrate and optimize AI/ML inference workflows within the backend - embedding pipelines, multimodal models, OCR, speech-to-text, scene analysis, and visual classifiers

• Own model-serving infrastructure: batching strategies, concurrency tuning, warmup behavior, timeout handling, autoscaling, and GPU utilization

• Apply practical model optimization techniques - quantization, distillation, batching, caching, routing to smaller models where appropriate - to hit latency, throughput, and cost targets on constrained hardware

• Benchmark and evaluate candidate models using domain-relevant metrics, not just standard leaderboards. Set operating thresholds using data-driven calibration methods and document the rationale

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

• Mentor junior and mid-level engineers through code reviews, design discussions, and hands-on pairing

• Drive architectural decisions and raise engineering quality across backend, infrastructure, and ML integration work

• Translate ambiguous product requirements into clear technical deliverables with defined success criteria

These three capabilities are non-negotiable. Please do not apply if you cannot speak to all three with specific examples and real numbers.

• Model optimization for constrained hardware. You have deployed an ML model (any modality) on a memory-constrained inference server and improved its performance in production. You have hands-on experience with at least two of: quantization (GPTQ, AWQ, GGUF, or bitsandbytes), batching strategy tuning, KV-cache optimization, knowledge distillation, or serving framework configuration (vLLM, Triton, TGI, or equivalent). You can cite specific before/after latency, throughput, or memory numbers from that work.

• Production async pipeline ownership. You have owned an async media-processing or ML inference pipeline end to end - including queue design, worker failure handling, idempotency, retry logic, dead-letter queues, and an observability layer. You understand what happens under backpressure and what breaks first.

• Evaluation and calibration rigor. You have set model operating thresholds using data-driven methods - precision/recall curves, cost-weighted metrics, or domain-specific benchmarks - not intuition or trial and error. You can explain the false-positive and false-negative tradeoffs of a classifier in plain language to a non-technical stakeholder.

Additional requirements:

• 8+ years of backend engineering experience building scalable distributed systems, data pipelines, or media processing services

• 4+ years of hands-on ML integration experience in production (model APIs, embedding pipelines, OCR, speech-to-text, video/image analysis, or multimodal inference)

Strong Python proficiency; deep understanding of R