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- Tasks: Build and optimise ML models for speech tasks in a cutting-edge AI project.
- Company: Join ConnexAI, a pioneering startup transforming speech capabilities in AI.
- Benefits: Enjoy a collaborative environment with opportunities for growth and innovation.
- Why this job: Shape the future of multimodal AI while working on impactful projects.
- Qualifications: Strong background in ML systems, Python, and familiarity with speech technologies required.
- Other info: This is a full-time role based in Manchester, England.
The predicted salary is between 36000 - 60000 ÂŁ per year.
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Direct message the job poster from ConnexAI
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
Seniority level
-
Seniority level
Associate
Employment type
-
Employment type
Full-time
Job function
-
Job function
Information Technology
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Industries
Software Development, Research Services, and Telecommunications
Referrals increase your chances of interviewing at ConnexAI by 2x
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Machine Learning Engineer employer: ConnexAI
Contact Detail:
ConnexAI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in speech technologies and multimodal machine learning. Being able to discuss recent research papers or breakthroughs during your conversations can really set you apart from other candidates.
✨Tip Number 2
Showcase your hands-on experience with ML frameworks like PyTorch. If you have personal projects or contributions to open-source that demonstrate your skills, be ready to discuss them in detail during interviews.
✨Tip Number 3
Network with professionals in the AI and machine learning community. Attend meetups or webinars focused on speech technologies and LLMs to make connections that could lead to referrals or insider information about the role.
✨Tip Number 4
Prepare to discuss your experience with MLOps tooling and infrastructure. Understanding how to manage the model lifecycle and monitor performance in production is crucial for this role, so be ready to share relevant experiences.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning systems, particularly in speech technologies and LLMs. Use specific examples of projects you've worked on that relate to the job description.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how your skills align with ConnexAI's mission. Mention any relevant experience with multimodal learning or real-time systems to stand out.
Showcase Technical Skills: Clearly list your technical skills, especially in Python, ML frameworks like PyTorch, and tools such as Docker and Kubernetes. Provide examples of how you've used these in past projects.
Highlight Collaboration Experience: Since the role involves working closely with cross-functional teams, include examples of how you've successfully collaborated with others in previous roles. This could be in research, product development, or engineering contexts.
How to prepare for a job interview at ConnexAI
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning frameworks like PyTorch and your familiarity with speech technologies. Bring examples of projects where you've built or optimised ML systems, especially those related to speech-to-text or text-to-speech.
✨Understand the Company’s Vision
Research ConnexAI and their focus on multimodal AI capabilities. Be ready to explain how your skills align with their goals and how you can contribute to shaping the architecture and product direction from the ground up.
✨Prepare for Technical Questions
Expect questions that test your understanding of model performance evaluation, debugging, and optimisation techniques. Brush up on custom metrics and automated testing frameworks relevant to ML systems to demonstrate your expertise.
✨Emphasise Collaboration Skills
Since the role involves working closely with cross-functional teams, be ready to share examples of how you've successfully collaborated with product managers, research scientists, and backend engineers in previous roles.
