Machine Learning Engineer - Fakespot

Hiring Ranges: US Tier 1 Locations: $137,000 USD - $200,000 USD US Tier 2 Locations: $126,000 USD - $185,000 USD US Tier 3 Locations: $116,000 USD - $170,000 USD  

To learn more about our Hiring Range System, please click this link.

Why Mozilla?

Mozilla Corporation is the non-profit-backed technology company behind pioneering brands like Firefox, the privacy-minded web browser, and Pocket, a service for keeping up with the best articles online. More than 225 million people around the world use its products each month.

Along with 60,000+ volunteer contributors and collaborators all over the world, Mozilla Corporation’s staff are driven by our mission to ensure the internet is a global public resource, open and accessible to all. We design, build and distribute open-source software that enables people to enjoy the internet on their terms. 

About this team and role:

Fakespot is now part of Mozilla, where our mission to bring trust and transparency back to the eCommerce space is now aligned with ensuring the Internet is a global resource, open and accessible to all. Fakespot leverages machine learning, and other state-of-the-art technologies to detect spurious product reviews and vendors so consumers can make informed purchasing decisions based on real feedback by real people. Fakespot also facilitates the shoppers’ decision making process by utilizing language models to interactively summarize and highlight product features.  

At the Fakespot team, you will have the opportunity to play a crucial role in bringing trustworthy tools to millions of Firefox and Fakespot users.  By joining our team, you will be at the forefront of ethical and responsible AI, and contribute to a trustworthy Internet. 

What you’ll do:

• Apply statistical and machine learning techniques to process and analyze unstructured textual data
• Develop and finetune machine learning models for tasks such as entity recognition, classification, and text generation
• Utilize pretrained language models (e.g. GPT, LLAMA) and adapt them for specific use cases
• Optimize the models for production usage, including considerations for scalability, latency, and resource
• Monitor and refine deployed models for performance and efficiency, and conduct troubleshooting when necessary
• Work closely with interdisciplinary teams to deliver high-quality features and solutions
• Stay current with advancements in NLP research, methodologies, and best practices

What you’ll bring:

• A bachelor’s degree in Statistics, Computer Science, related technical field, or equivalent practical experience
• A minimum of 3 years of experience in a quantitative role, ideally as a machine learning engineer or a data scientist
• Knowledge of and experience with Natural Language Processing (NLP)
• Proficiency in a data query language (e.g. SQL), and a programming language (e.g. Python)   
• Demonstrable experience with the full lifecycle of machine learning models - from development to deployment and monitoring
• Being an excellent team player with a proven ability to work effectively in cross-functional teams, showing a high degree of collaboration, flexibility, and respect for diverse perspectives
• Commitment to our values:
• Welcoming differences
• Being relationship-minded
• Practicing responsible participation
• Having grit

Bonus Points For…

• An advanced degree (master or PhD) in a quantitative field
• A deep understanding of deep learning, reinforcement learning, and natural language processing
• Experience with cloud platforms such as AWS, Google Cloud, or Azure, and familiarity with related machine learning services
• Experience with big data technologies like Hadoop, Spark, or similar
• Demonstrated prior experience with large language models, and generative AI 
• Enthusiasm in bringing trust to the e-commerce platforms and beyond

What you’ll get:

• Generous performance-based bonus plans to all regular employees - we share in our success as one team
• Rich medical, dental, and vision coverage
• Generous retirement contributions with 100% immediate vesting (regardless of whether you contribute)
• Quarterly all-company wellness days where everyone takes a pause together
• Country specific holidays plus a day off for your birthday
• One-time home office stipend
• Annual professional development budget
• Quarterl