AI/ML Developer (Speech AI)

Our client is a technology startup building advanced voice automation solutions for the quick-service restaurant industry. The company develops a privacy-conscious, high-performance drive-thru voice assistant that automates real-time customer interactions, improves order accuracy, and helps restaurant chains increase revenue and operational efficiency. Its product is designed for fast deployment in noisy, high-volume drive-thru environments and is already gaining market traction through cooperation with a major restaurant chain.

The project is a next-generation voice automation engine for drive-thru order-taking in quick-service restaurants. It enables fully automated, real-time conversations between customers and the restaurant ordering system using modern speech recognition, natural language processing, and text-to-speech technologies.

• Advanced Python development skills
• Deep hands-on expertise with Speech-to-Text and Text-to-Speech systems
• Proven experience improving speech recognition quality in noisy or otherwise challenging acoustic environments
• Strong expertise in noise suppression, echo cancellation, voice activity detection, and speech enhancement
• Strong understanding of real-time and streaming audio architectures, including conversational voice pipelines and real-time inference
• Experience building low-latency, production-grade AI systems
• Experience with modern speech AI frameworks, models, and APIs
• Experience deploying and scaling AI services in cloud environments
• Ability to troubleshoot complex audio quality, latency, and reliability issues
• Product-oriented mindset with a focus on real-world performance, customer experience, and high ownership
• Ability to collaborate effectively with engineering, LLM, and conversational AI teams
• English level: B2 or higher

• Optimize low-latency, real-time Speech-to-Text pipelines for production drive-thru environments
• Improve Text-to-Speech naturalness, responsiveness, and overall conversational quality
• Design, tune, and improve noise suppression, echo cancellation, and speech enhancement systems
• Improve speech recognition accuracy and robustness under challenging acoustic conditions, including engine noise, weather, overlapping speech, poor microphone quality, and outdoor environments
• Build and scale audio processing infrastructure for production deployments
• Evaluate, benchmark, and compare speech models using real-world audio data and production scenarios
• Experiment with modern Speech AI technologies, models, and architectures to improve system performance
• Collaborate with LLM and conversational AI teams to improve end-to-end voice interaction quality