AI Research Engineer (Model Compression & Quantization) - 100% Remote Worldwide

Tether Operations Limited; Deutschland; Remote; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

Tether Operations Limited is hiring for a role in its AI model team to drive innovation in model serving and inference architectures. The position focuses on optimizing deployment, ensuring high throughput, low latency, and scalable performance across resources from edge to cloud.

You will design robust inference pipelines, monitor performance, and collaborate across teams to deliver efficient on-device AI solutions that meet real-world demands.

Qualifikationen

• Degree in Computer Science or related field; PhD in NLP/ML is a plus.
• Strong background in model serving architectures and inference optimization.
• Experience with low-resource/edge devices and memory-focused optimizations.

Aufgaben

• Design and deploy state-of-the-art model serving architectures with low latency and memory efficiency.
• Build, test, and monitor inference pipelines across edge and on-device environments.
• Analyze performance metrics and optimize bottlenecks in production.
• Collaborate with cross-functional teams to integrate serving frameworks into edge pipelines.
• Define success metrics for real-world performance and scalability.

Kenntnisse

Model serving

Inference optimization

GPU kernels (mobile)

Edge deployment

Performance monitoring

Bottleneck diagnosis

C/C++ proficiency

Collaborative开发

Ausbildung

Degree in Computer Science

PhD in NLP/ML (preferred)

Tools

Metal Shading Language (MSL)

GPU kernels for mobile devices

Model serving frameworks

Jobbeschreibung

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

As a member of our AI model team, you will drive innovation in model serving and inference architectures for advanced AI systems. Your work will focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real‑world applications. You will work on a wide spectrum of systems, ranging from resource‑efficient models designed for limited hardware environments to complex, multi‑modal architectures that integrate data such as text, images, and audio.

We expect you to have deep expertise in designing and optimizing model serving pipelines and inference frameworks as well as a strong background in advanced model architectures. You will adopt a hands‑on, research‑driven approach to develop, test, and implement novel serving strategies and inference algorithms. Your responsibilities include engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments. The ultimate goal is to enable high‑throughput, low‑latency, low‑memory footprint, and scalable AI performance that delivers tangible value in dynamic, real‑world scenarios.

Responsibilities

• Design and deploy state‑of‑the‑art model serving architectures that deliver high throughput and low latency while optimizing memory usage. Ensure these pipelines run efficiently across diverse environments, including resource‑constrained devices and edge platforms. Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint.

• Build, run, and monitor controlled inference tests in both simulated and live production environments. Track key performance indicators such as response latency, throughput, memory consumption, and error rates, with special attention to metrics specific to resource‑constrained devices. Document iterative results and compare outcomes against established benchmarks to validate performance across platforms.

• Identify and prepare high‑quality test datasets and simulation scenarios tailored to real‑world deployment challenges, specifically those encountered on low‑resource devices. Set measurable criteria to ensure that these resources effectively evaluate model performance, latency, and memory utilization under various operational conditions.

• Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring both processing and memory metrics. Address issues such as suboptimal batch processing, network delays, and high memory usage to optimize the serving infrastructure for scalability and reliability on resource‑constrained systems.

• Work closely with cross‑functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on‑device applications. Define clear success metrics such as improved real‑world performance, low error rates, robust scalability, optimal memory usage and ensure continuous monitoring and iterative refinements for sustained improvements.

• A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).

• Must have knowledge of Metal Shading Language (MSL). You should be comfortable writing custom compute shaders from scratch.

• Proven experience in low‑level kernel optimizations and inference optimization on mobile devices is essential. Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain‑specific applications, particularly on resource‑constrained devices and edge platforms.

• A deep understanding of modern model serving architectures and inference optimization techniques is required. This includes state‑of‑the‑art methods for achieving low‑latency, high‑throughput performance, and efficient memory management in diverse, resource‑constrained deployment scenarios.

• Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones) as well as a deep understanding of model serving frameworks and engines. Practical experience in developing and deploying end‑to‑end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource‑constrained devices is required.

• Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints. You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency.

• Distributed Inferenc​e Systems: Designing and optimizing high‑performance inference engines using techniques like Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism to handle massive models on GPU clusters.

• Deep understanding of the math and structure behind Diffusion Models and Vision Transformers

• Understanding of Pruning, Quantization, Flash attention, KV Cache, Speculative Decoding (Eagle) etc.

When in doubt, feel free to reach out through our official website.

Find Jobs in United Kingdom on Arbeitnow