Tether.to

Crypto

• Support the Head of Tokenization Expansion in managing active client and partner projects from onboarding to delivery.
• Act as an Account Manager for assigned clients, ensuring consistent communication, alignment of expectations, and timely follow-up on all requests.
• Coordinate communication and execution between clients, product, and tech teams.
• Manage relationships with ecosystem partners.
• Conduct research, prepare memos, and synthesize insights for business and product decisions.
• Track project milestones, maintain accurate reporting, and ensure timely delivery.
• Assist in drafting client proposals, commercial documents, and presentations.
• Help structure internal workflows and process improvements to enhance scalability.
• Support the functional development of the Hadron platform according to client and market needs.

Expansion Manager – Tokenization

AI Research Scientist

• Conduct end-to-end research and engineering initiatives to advance post-training of agentic and tool-use models to achieve SOTA results.
• Drive broad, cross-cutting model improvements, including factuality, instruction adherence, tool/function use, multi-agent coordination, and reasoning calibration.
• Design and enhance large-scale post-training systems, including data pipelines, training workflows, evaluation frameworks, and benchmark infrastructure.
• Develop rigorous evaluation suites and diagnostic tools to assess model readiness for deployment.
• Strengthen feedback loops from real-world product usage, incorporating both explicit and implicit user signals into post-training.
• Collaborate with tooling, product, and training teams to improve the usefulness, reliability, and agentic capabilities of frontier models.
• Closely liaise with research, engineering and cross-functional teams to determine which integrations are production-ready for inclusion in major model releases.

AI Research Engineer – Agentic Post-training

• Drive innovation in model serving and inference architectures for advanced AI systems.
• Focus on optimizing model deployment and inference strategies.
• Work on a wide spectrum of systems, from resource-efficient models to complex, multi-modal architectures.
• Develop, test, and implement novel serving strategies and inference algorithms.
• Engineer robust inference pipelines, establish performance metrics, and resolve bottlenecks in production environments.
• Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value.

AI Research Engineer – Kernel & Inference Optimization

• Drive innovation in model serving and inference architectures for advanced AI systems.
• Focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real-world applications.
• 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.
• Adopt a hands-on, research-driven approach to develop, test, and implement novel serving strategies and inference algorithms.
• Engineer robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments.
• Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios.

AI Research Engineer – Kernel, Inference Optimization

• Conduct research on reinforcement learning algorithms for multimodal models,
• Design and build reinforcement learning infrastructure that supports scalable training,
• Develop and refine reward modeling strategies,
• Create and curate multimodal simulation environments and datasets,
• Analyze and optimize policy performance across modalities,
• Investigate and develop next-generation reinforcement learning paradigms,
• Publish research findings in top-tier conferences.

AI Research Engineer – Multi-Modal Reinforcement Learning

• Conduct end-to-end research and engineering on vision-language models, covering training, evaluation, and optimization across the full model development lifecycle.
• Design and implement post-training pipelines including supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback.
• Develop and maintain high-quality multimodal datasets, including data curation, filtering, and balancing for domain-specific tasks.
• Drive model efficiency and deployability, adapting models for resource-constrained environments using compression and optimization techniques.
• Design and implement evaluation frameworks and benchmarks to measure model performance, robustness, and real-world task success.
• Build and scale training workflows across distributed GPU infrastructure.
• Identify and resolve bottlenecks in training pipelines to achieve state-of-the-art model quality on target benchmarks.
• Contribute to and leverage open-source ecosystems including models, datasets, and tooling to accelerate development.
• Stay current with the latest research in multimodal learning and vision-language systems, translating relevant findings into practical improvements.
• Publish research findings in top-tier AI conferences and journals where applicable.

AI Research Engineer – Multi-Modal, Vision

• Apply low-bit quantization to reduce model size and inference latency for generative AI models (LLMs, VLMs, multimodal) while maintaining accuracy and output quality.
• Leverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models, enabling efficient multimodal reasoning across text, image, and audio inputs.
• Implement pruning techniques to remove redundant parameters and attention heads, reducing computational overhead without sacrificing task performance.
• Analyze trade-offs between model efficiency (size, latency, memory) and accuracy across quantization, distillation, and pruning methods; propose improvements based on empirical findings.
• Research and apply mixed-precision quantization and other advanced compression strategies (e.g., adaptive pruning schedules, distillation with intermediate feature matching) to optimize the accuracy–performance balance.
• Stay current with the latest research in model compression, including emerging techniques for multimodal and generative architectures.
• Document methodologies, experiments, and results clearly to support reproducibility, internal collaboration, and stakeholder communication.
• Author technical papers and publish findings in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, AAAI) to advance the field of model compression for multimodal AI.

AI Research Engineer – Model Compression, Quantization

• Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, including large language models (LLMs) and vision-language models (VLMs).
• Reduce model footprint and computational cost while preserving accuracy, enabling high-performance AI to run efficiently across resource-constrained edge devices.
• Apply and advance compression techniques such as quantization, knowledge distillation, and pruning.
• Build robust compression pipelines, establish performance and fidelity metrics, and address bottlenecks in production inference.

AI Research Engineer, Model Compression, Quantization

Full-stack Engineer

• Build and ship pages, components, and layouts using React, TypeScript, and Tailwind CSS within a Next.js application.
• Build and ship RESTful apis used across developer portals, integrate custom services with different Tether departments.
• Configure Payload CMS collections, custom fields, and access control for documentation and website content.
• Translate Figma designs into pixel-perfect, responsive interfaces following the established design system.
• Collaborate on information architecture and navigation patterns serving developer audiences across multiple products.
• Implement monitoring, logging, and telemetry solutions to ensure high availability and performance of the platform services.
• Optimize for Core Web Vitals and search engine visibility.
• Work closely with the product/DX lead, designers, and engineers from different departments to ensure smooth delivery.

Senior Fullstack Software Engineer, Websites

• Build and ship pages, components, and layouts using React, TypeScript, and Tailwind CSS within a Next.js application
• Build and ship RESTful APIs used across developer portals, integrate custom services with different Tether departments
• Configure Payload CMS collections, custom fields, and access control for documentation and website content
• Translate Figma designs into pixel-perfect, responsive interfaces following the established design system
• Collaborate on information architecture and navigation patterns serving developer audiences across multiple products
• Implement monitoring, logging, and telemetry solutions to ensure high availability and performance of platform services
• Optimize for Core Web Vitals and search engine visibility
• Work closely with the product/DX lead, designers, and engineers from different departments to ensure smooth delivery

• Build and ship pages, components, and layouts using React, TypeScript, and Tailwind CSS within a Next.js application
• Build and ship RESTful APIs used across developer portals, integrate custom services with different Tether departments
• Configure Payload CMS collections, custom fields, and access control for documentation and website content
• Translate Figma designs into pixel-perfect, responsive interfaces following the established design system
• Collaborate on information architecture and navigation patterns serving developer audiences across multiple products
• Implement monitoring, logging, and telemetry solutions to ensure high availability and performance of the platform services
• Optimize for Core Web Vitals and search engine visibility
• Work closely with the product/DX lead, designers, and engineers from different departments to ensure smooth delivery

• Drive innovation in model serving and inference architectures for advanced AI systems
• Focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance
• Work on a wide spectrum of systems, ranging from resource-efficient models designed for limited hardware environments to complex, multi-modal architectures
• Engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks
• Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios
• Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage
• 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
• Document iterative results and compare outcomes against established benchmarks
• Identify and prepare high-quality test datasets and simulation scenarios tailored to real-world deployment challenges

AI Research Engineer, Kernel & Inference Optimization

• Drive innovation in model serving and inference architectures for advanced AI systems.
• Focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real-world applications.
• 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.
• Develop, test, and implement novel serving strategies and inference algorithms.
• Engineer robust inference pipelines, establish comprehensive performance metrics, and identify and resolve bottlenecks in production environments.
• Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios.