Speechify

Full-stack Engineer

• Lead the design, architecture, and development of native Windows desktop applications using Windows App SDK, WinUI (or related UI frameworks), C#, XAML, and — when needed — C++.
• Define and enforce best-practices for Windows desktop development across the codebase: code architecture, performance, memory usage, responsive UI, cross-version compatibility (Windows 10/11+), and maintainability.
• Drive accessibility efforts: integrate and validate support for accessibility APIs (e.g. Microsoft UI Automation or other relevant Windows accessibility frameworks), ensure UI controls, focus management, keyboard navigation, screen-reader support, and usability for users with disabilities.
• Collaborate closely with product designers, UX researchers, QA, and other stakeholders to shape feature planning, UI/UX architecture, and long-term roadmap for the Windows platform.
• Take ownership of full lifecycle of features: conception → design → implementation → testing → release → maintenance. Ensure quality, reliability, and consistency across releases.
• Identify, diagnose, and resolve complex bugs, performance bottlenecks, memory leaks, rendering issues, or compatibility problems — and propose robust architectural or design solutions.

Senior Software Engineer, Windows/Desktop Applications

• Work alongside machine learning researchers, engineers, and product managers to bring our AI Voices to their customers for a diverse range of use cases
• Deploy and operate the core ML inference workloads for our AI Voices serving pipeline
• Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our deployed models
• Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues

Tech Lead, Web Core Product – Chrome Extension

Software Engineer – Platform

Senior Software Engineer, Core Experiences

Software Engineer – iOS Core Product

Product Designer

• Pitch, prototype, test, build, ship, and refine new features
• Refine our UX/UI patterns across our iOS and Android apps
• Rethink our onboarding and core user flows
• Influence the product roadmap
• Refine our process and help shape the design org

Senior Product Designer

Infrastructure Engineer

• Build & Scale AI Infrastructure: Design, implement, and maintain high-performance ML training and inference platforms.
• Develop MLOps Tools: Ship tools that allow any ML engineer to deploy a model in minutes, not days.
• Optimize Performance: Improve scalability, reliability, and cost efficiency of model training and serving systems.
• Collaborate Across Teams: Partner with researchers to turn experimental voice models into production-ready systems.
• Ensure Best Practices: Establish standards for model versioning, testing, monitoring, and governance.
• Drive Automation: Automate data/model pipelines to reduce manual intervention and speed up experimentation.

AI Infrastructure Engineer

Android Engineer

• Owning major features and working closely with our design team - take ownership of features inside the app and become responsible of delivering high quality features
• Shape the future of our Android team
• Own, maintain and improve reliability metrics for key features
• Participate in discussions across different teams - Product, Design, Engineering
• Review pull requests, and support other teammates
• Handle critical issues or cope with unexpected challenges
• Take ownership of feature releases and provide nightly builds for the QA team

Software Engineer, Android Core Product

Tech Lead, Web Core Product and Chrome Extension

Senior Software Engineer, AI Model Serving

Tech Lead, Web Core Product & Chrome Extension

iOS Engineer

Software Engineer, iOS Core Product

• Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases
• Deploy and operate the core ML inference workloads for AI Voices serving pipeline
• Introduce techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models
• Build tools to give visibility into bottlenecks and sources of instability and implement solutions to address highest priority issues
• Collaborate across distributed, cross-functional teams in a 100% remote environment

Software Engineer, Data Infrastructure and Acquisition

• Work alongside machine learning researchers, engineers, and product managers to bring our AI Voices to their customers for a diverse range of use cases
• Deploy and operate the core ML inference workloads for our AI Voices serving pipeline
• Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our deployed models
• Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues
• Make product decisions and contribute to building great user experiences that delight users
• Operate and maintain critical production services and improve system reliability and observability

Software Engineer, Platform

• Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases
• Deploy and operate the core ML inference workloads for the AI Voices serving pipeline
• Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models
• Build tools to identify bottlenecks and instability sources and design/implement solutions for high-priority issues
• Collaborate within a distributed team and contribute to product decisions and user experience design