Staff Machine Learning Engineer, Mozilla

• 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 our values

Desirable

• A Staff Engineer is the next level from a Senior Engineer. At Mozilla this role can vary but typically a Staff Engineer is a domain expert who leads others within a single team to tackle multi-month projects
• Tasks that may be initially ambiguous or require significant planning and require them to influence or direct the work of several engineers
• They sequence deliverables and manage risks in their team's projects, provide feedback on our strategy and goals affecting the team, and turn our strategy into action for their team members
• Staff Engineers mentor others by stewarding some responsibilities to more junior and senior engineers so they can take on new ones
• They collaborate with management on building team consensus and providing direction
• Staff Engineers identify gaps and opportunities for improvement to enable a culture of inclusion and allyship, at all levels of the organization
• 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