Remote Staff Machine Learning Engineer, Gen AI Job in UK Mozilla

Who you are

• 6+ years experience as a Machine Learning engineer building tooling and services for machine learning applications in production.
• Experience with designing and building machine learning tooling and infrastructure for training, deploying, inference and validation of models.
• Experience with distributed systems and platforms for AI integrations across a breadth of applications such as LLM APIs, Cloud based and open source tooling.
• Leading architecture designs to execution independently and track record of improving the team’s overall AI development velocity.
• Strong problem-solving skills and ability to communicate complex concepts to technical and non-technical stakeholders.
• Experience working collaboratively with product managers, project managers, and/or other non-engineering teams.
• Effective documentation and communication skills on a distributed team.
• Commitment to values: Welcoming differences, Being relationship-minded, Practicing responsible participation, Having grit.

Desirables

• Knowledge of and experience with using LLM solutions across different cloud providers
• Previously successfully contributed to an open source project
• Track record of multiple projects deployed with an active user base
• Experience with responsible AI, transparent algorithms, and putting users' needs first
• Excellent communication skills in written and presentation form
• Lifelong learner
• Experience with the browser extensions architecture or ecosystem
• Experience working in open source environments

What the job involves

• Lead the design, development, and integration of Generative AI solutions in Firefox, collaborating cross functionally with product management, full stack engineering and design
• Build infrastructure for training and inference of LLMs and small language and vision models for use cases on the web and mobile Firefox product experiences
• Implement robust validation and testing procedures to ensure the developed models' generalizability and reliability
• Continuously monitor and optimize deployed models for performance and efficiency