Tether.to

Financial Planning and Analysis

• Manage trade confirmations, settlements, and lifecycle events across multiple asset classes.
• Oversee wallet, custodian, and digital asset operational processes.
• Perform daily booking and reconciliation of securities positions, cash balances, and P&L across internal systems.
• Investigate and resolve trade breaks, settlement issues, and operational discrepancies with counterparties and custodians.
• Support operational controls relating to treasury, trading, and digital asset activities.
• Assist with audit requests, confirmations, and supporting documentation.
• Prepare daily, weekly, and monthly operational reporting.
• Build, enhance, and maintain operational workflows, internal tooling, and automation processes.
• Identify opportunities for process optimisation and operational scalability.
• Create and maintain technical documentation, procedures, and user guides.
• Ensure adherence to internal controls, policies, and applicable regulatory requirements.

Operations Associate/Senior Associate – Finance, Digital Assets Exposure

Developer Relations

• Developer Advocacy & Community Building
• Build and nurture relationships with developers, partners, and open-source communities.
• Engage and interact with the developer ecosystem — hosting AMAs, office hours, webinars, and play a part in online communities.
• Collect and synthesize developer feedback to inform product and engineering teams.
• Identify influential developers, projects, and communities to establish collaboration opportunities.
• Technical Content & Enablement
• Create and maintain high-quality technical content (blog posts, tutorials, sample apps, videos, documentation, SDK guides, etc.) directly targeted to developers.
• Develop demos, prototypes, and reference implementations that showcase real-world use cases.
• Contribute to improving developer experience through documentation feedback, onboarding flows, and API design suggestions.
• Support developer onboarding through workshops, hackathons, and training sessions.
• Product Evangelism
• Advocate for the company’s technology stack at industry events, developer meetups, and online communities.
• Translate complex technical concepts into clear, relatable value propositions.
• Collaborate with marketing to align technical storytelling with broader brand messaging.
• Track and report community sentiment and adoption metrics to measure evangelism impact.
• Cross-Functional Collaboration
• Partner closely with engineering, product management, and marketing to align developer needs with product strategy.
• Provide structured feedback loops between external developers and internal teams to improve product usability.
• Support launch strategies for new developer-facing features, APIs, and SDKs.
• Contribute to roadmap discussions to ensure developer experience remains a strategic priority.
• Support initial technical discussions with potential customers and partners
• Thought Leadership
• Represent the company as a credible technical voice in the ecosystem through public speaking, open-source contributions, and community engagement.
• Stay ahead of industry trends, tools, and frameworks relevant to the developer community.
• Mentor internal teams on effective developer communication and community engagement practices.

Developer Relations, P2P

• Build and nurture relationships with developers, partners, and open-source communities.
• Engage and interact with the developer ecosystem — hosting AMAs, office hours, webinars, and play a part in online communities.
• Collect and synthesize developer feedback to inform product and engineering teams.
• Identify influential developers, projects, and communities to establish collaboration opportunities.
• Create and maintain high-quality technical content (blog posts, tutorials, sample apps, videos, documentation, SDK guides, etc.) directly targeted to developers.
• Develop demos, prototypes, and reference implementations that showcase real-world use cases.
• Contribute to improving developer experience through documentation feedback, onboarding flows, and API design suggestions.
• Support developer onboarding through workshops, hackathons, and training sessions.
• Advocate for the company’s technology stack at industry events, developer meetups, and online communities.
• Translate complex technical concepts into clear, relatable value propositions.
• Collaborate with marketing to align technical storytelling with broader brand messaging.
• Track and report community sentiment and adoption metrics to measure evangelism impact.
• Partner closely with engineering, product management, and marketing to align developer needs with product strategy.
• Support launch strategies for new developer-facing features, APIs, and SDKs.

Developer Relations P2P

• Build and nurture relationships with developers
• Create and maintain high-quality technical content
• Advocate for the company's technology stack at industry events
• Partner closely with engineering and product teams

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.

Tether.to

Operations Associate/Senior Associate – Finance, Digital Assets Exposure

Developer Relations, P2P

Developer Relations P2P

Developer Relations, P2P

Expansion Manager – Tokenization

Expansion Manager – Tokenization

AI Research Engineer – Agentic Post-training

AI Research Engineer – Kernel & Inference Optimization

AI Research Engineer – Kernel, Inference Optimization

AI Research Engineer – Multi-Modal Reinforcement Learning

AI Research Engineer – Multi-Modal, Vision

AI Research Engineer – Model Compression, Quantization

AI Research Engineer, Model Compression, Quantization

Senior Fullstack Software Engineer, Websites

Senior Fullstack Software Engineer, Websites