Tether.to

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 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 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

AI Research Scientist

• 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.

AI Research Engineer – Kernel, Inference Optimization

• Drive innovation in model serving and inference architectures
• Optimize model deployment and inference strategies
• Work on resource-efficient models for limited hardware
• Engineer robust inference pipelines
• Establish comprehensive performance metrics
• Identify and resolve bottlenecks in production environments

AI Research Engineer – Kernel & Inference Optimization

• 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.

• 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
• 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
• Document iterative results and compare outcomes against established benchmarks
• Identify and prepare high-quality test datasets and simulation scenarios
• Analyze computational efficiency and diagnose bottlenecks in the serving pipeline
• Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines

• Drive innovation in model serving and inference architectures for advanced AI systems
• Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency
• Ensure pipelines run efficiently across diverse environments
• Establish clear performance targets
• Build, run, and monitor controlled inference tests
• Identify and prepare high-quality test datasets and simulation scenarios
• Analyze computational efficiency and diagnose bottlenecks in the serving pipeline
• Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines

Artificial Intelligence

• Act as the primary point of contact for clients and partners during AI implementation initiatives.
• Guide clients in translating business requirements into AI deployment strategies leveraging QVAC capabilities.
• Lead end-to-end coordination of QVAC-based implementations from kickoff through production deployment.
• Define implementation roadmaps, milestones, and dependencies across AI models, infrastructure, and integration layers.
• Coordinate closely with product, engineering, and research teams to align on QVAC capabilities.
• Establish and maintain governance frameworks including implementation plans, risk tracking, and decision logs.

Engagement Manager, AI Implementations

• Act as the primary point of contact for clients and partners during AI implementation initiatives, ensuring clear communication, expectation alignment, and structured execution.
• Guide clients in translating business requirements into AI deployment strategies leveraging QVAC capabilities (local inference, delegated compute, privacy-preserving architectures).
• Support Expansion team in shaping AI-related opportunities by providing input on feasibility, integration complexity, and delivery approach.
• Identify opportunities to extend implementations across additional use cases, geographies, or Tether technologies.
• Lead end-to-end coordination of QVAC-based implementations from kickoff through production deployment.
• Define implementation roadmaps, milestones, and dependencies across AI models, infrastructure, and integration layers.
• Ensure alignment between client expectations and actual product capabilities, avoiding scope drift or mispositioning.
• Track progress across multiple concurrent AI deployments, ensuring timely delivery and readiness for production environments.
• Coordinate closely with product, engineering, and research teams to align on QVAC capabilities, limitations, and roadmap evolution.
• Facilitate integration between client systems and QVAC components, including model deployment pipelines, APIs, and compute environments.
• Work with legal and compliance teams where required, particularly in sensitive AI deployments involving data locality or privacy constraints.
• Maintain structured communication flows across all stakeholders involved in the implementation lifecycle.
• Establish and maintain governance frameworks including implementation plans, risk tracking, and decision logs.
• Produce executive-level updates summarizing progress, risks, blockers, and next steps.
• Ensure documentation of implementation architectures, deployment patterns, and key learnings for reuse across future projects.
• Support escalation management and ensure timely resolution of technical or operational challenges.

Engagement Manager – AI Implementations

Technical Product Manager

• Define and champion the product vision for the QVAC SDK, aligning technical capabilities with Tether’s broader objectives around local AI, privacy, decentralization, and P2P infrastructure.
• Own the roadmap for SDK capabilities, APIs, developer workflows, integrations, documentation, examples, templates, and tooling.
• Curate the end-to-end developer experience, from first install to production integration.
• Translate product goals into precise technical requirements for engineering teams.
• Use coding ability to explore SDK workflows, build proof-of-concept integrations, and create credible technical prototypes.
• Work closely with engineering leads to make informed product decisions around SDK architecture and developer ergonomics.
• Develop a strong understanding of the developer tools, AI infrastructure, Local AI, edge computing, and enterprise AI markets.
• Prioritize SDK capabilities based on developer demand and business impact.
• Partner with engineering, developer relations, and marketing to ensure accurate and usable SDK documentation.
• Build and own SDK launch plans in partnership with various teams, defining positioning and target developer segments.
• Define and track SDK success metrics and communicate clearly with stakeholders.

• Define and champion the product vision for the QVAC SDK
• Own the roadmap for SDK capabilities, APIs, developer workflows, integrations, documentation, examples, templates, and tooling
• Curate the end-to-end developer experience, from first install to production integration
• Translate product goals into precise technical requirements for engineering teams
• Use coding ability to explore SDK workflows, build proof-of-concept integrations, test APIs, and create credible technical prototypes
• Work closely with engineering leads to make informed product decisions
• Develop a strong understanding of the developer tools, AI infrastructure, Local AI, edge computing, and enterprise AI markets
• Prioritize SDK capabilities based on developer demand and measurable business impact
• Partner with engineering to ensure excellent documentation and technical content
• Build and own SDK launch plans in partnership with marketing, developer relations, ecosystem, and business teams
• Define and track SDK success metrics
• Communicate clearly with executives, engineers, partners, enterprise customers, and developers.

Technical Product Manager, QVAC

• Act as the primary point of contact for clients and partners during AI implementation initiatives, ensuring clear communication, expectation alignment, and structured execution.
• Guide clients in translating business requirements into AI deployment strategies leveraging QVAC capabilities (local inference, delegated compute, privacy-preserving architectures).
• Lead end-to-end coordination of QVAC-based implementations from kickoff through production deployment.
• Define implementation roadmaps, milestones, and dependencies across AI models, infrastructure, and integration layers.
• Ensure alignment between client expectations and actual product capabilities, avoiding scope drift or mispositioning.
• Coordinate closely with product, engineering, and research teams to align on QVAC capabilities, limitations, and roadmap evolution.

Manager

• Assist in tracking engineering and product team performance by updating dashboards
• Maintain operational standards within Confluence
• Provide administrative support in tracking OKRs
• Assist in budget spreadsheets and expenditure logs

Performance Manager Associate

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

Senior Fullstack Software Engineer, Websites

Senior Fullstack Software Engineer, Websites

AI Research Engineer, Kernel & Inference Optimization

AI Research Engineer – Kernel, Inference Optimization

AI Research Engineer – Kernel & Inference Optimization

AI Research Engineer – Kernel & Inference Optimization

AI Research Engineer – Kernel & Inference Optimization

AI Research Engineer – Kernel & Inference Optimization

Engagement Manager, AI Implementations

Engagement Manager – AI Implementations

Technical Product Manager

Technical Product Manager, QVAC

Engagement Manager, AI Implementations

Performance Manager Associate

Expansion Manager – Tokenization