Staff Machine Learning Engineer (Speech AI)

TL;DR

Staff Machine Learning Engineer (Speech AI): Developing and deploying advanced, customer-centric speech systems by translating cutting-edge research into scalable solutions with an accent on model optimization for speed, accuracy, and cost efficiency. Focus on integrating contemporary transformer architectures, distributed training techniques, and best practices for software engineering and model serving into production.

Location: Hybrid in Cambridge, England, United Kingdom (2-3 designated office days per week)

Company

hirify.global is a world leader in Speech AI, using Machine Learning to unlock business value from human speech worldwide.

What you will do

• Develop and deploy ML models, translating research into scalable, user-centered solutions.
• Optimize ML models for speed, accuracy, and cost efficiency.
• Evaluate and integrate cutting-edge approaches into the ML stack.
• Collaborate cross-functionally to align initiatives with business goals.
• Mentor junior engineers and foster technical excellence.
• Contribute to technical strategy and roadmap.

Requirements

• Deep understanding of the modern Machine Learning stack, including contemporary transformer architectures and distributed training.
• Proven track record of developing and deploying models at scale.
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch).
• Experience with cloud platforms.
• Strong familiarity with software engineering practices and ML testing frameworks.
• User-centricity, with understanding how ML solutions address customer needs.

Culture & Benefits

• Flexible working with a hybrid approach (2-3 office days in Cambridge).
• Focus Fridays for undisturbed work and Together Tuesdays for team meetings.
• Private Medical and Dental for employees and family.
• Generous holiday allowance and pension/401K matching.
• Working from home allowance for tech or home office equipment.
• Support for parents, including adoption assistance and reproductive health services globally.
• Inclusive environment valuing diverse experiences and viewpoints.