Machine Learning Engineer

Machine Learning Engineer – Multimodal LLMs (Speech Focus)

About the Role

ConnexAI is developing a transformative product that enables speech-to-speech capabilities in large language models. This is a greenfield project with significant scope to influence both its technical architecture and product impact from the ground up.

We’re looking for a hands-on Machine Learning Engineer with deep expertise in building, optimising, and deploying ML systems — particularly in the areas of speech, LLMs, or multimodal learning. You will take cutting-edge research and turn it into production-ready models, enabling real-time, scalable, and reliable multimodal AI experiences.

What You'll Be Doing

• Building and productising machine learning models for speech-to-text, text-to-speech, and speech-to-speech tasks
• Translating academic and internal research into scalable, maintainable code and services
• Developing and maintaining training pipelines, inference services, and deployment workflows
• Implementing robust data pipelines for sourcing, preprocessing, and versioning multimodal datasets
• Collaborating with research scientists to refine model architectures and integrate the latest techniques into production
• Evaluating model performance with custom metrics and developing automated test frameworks for ML systems
• Contributing to MLOps tooling and infrastructure to support model lifecycle management and monitoring in production
• Working closely with product, research, and backend engineering to deliver seamless end-to-end features

What We're Looking For

• Strong engineering background with experience shipping ML systems to production
• Deep familiarity with speech technologies (ASR, TTS), LLMs, or multimodal machine learning
• Proficient in Python, with expertise in ML frameworks such as PyTorch
• Experience building scalable ML pipelines (training, validation, deployment, monitoring)
• Knowledge of Docker, Kubernetes, and ML deployment platforms
• Comfort reading and adapting recent research papers into performant implementations
• Strong debugging and optimisation skills, particularly around model inference speed and resource usage
• Experience working in cross-functional teams and contributing to engineering culture and best practices
• Bonus: experience with streaming audio processing, real-time systems, or speech synthesis engines

Why Join Us?

• Be part of a foundational team building novel, multimodal AI capabilities
• Shape the architecture and product direction from an early stage
• Work in a fast-moving, collaborative environment with a strong focus on execution and innovation
• Opportunity to grow alongside a rapidly scaling AI startup