Hi, we're The Browser Company 👋 and we're building a better way to use the internet.

Browsers are unique in that they are one of the only pieces of software that you share with your parents as well as your kids. Which makes sense, they're our doorway to the most important things — through them we socialize with loved ones, work on our passion projects, and explore our curiosities. But on their own, they don’t actually do a whole lot, they’re kind of just there. They don’t help us organize our messy lives or make it easier to compose our ideas. We believe that the browser could do so much more — it can empower and support the amazing things we do on the internet. That’s why we’re building one: a browser that can help us grow, create, and stay curious.

To accomplish this lofty task, we’re building a diverse team of people from different backgrounds and experiences. This isn’t optional, it’s crucial to our mission, as we need a wide range of perspectives to challenge our assumptions and shape our browser through a bold, creative lens. With that in mind, we especially encourage women, people of color, and others from historically marginalized groups to apply.

About The Role

Browsers know everything about us and what we do everyday, and yet they can’t predict our next move, morph themselves to better suit our tasks, or proactively take work off our plate. As the first AI Engineer at The Browser Company, you will work with product engineers to prototype and explore how we can build a smarter, more personalized web browser with a focus on privacy-preserving, on-device models.

You’ll work to answers questions like –

• What kind of features are better suited for on-device models vs modern LLM APIs like GPT?

• How can we get on-device inference quality to match that of GPT 3.5 or 4?

• How can we get local LLM and embedding models to run faster and more performantly on our members’ machines?

• Can we distill larger models into a smaller footprint? Or fine-tune local models to work well for our particular use-cases?

• Can we fine-tune performant neural networks to do narrow tasks where the generalizability of LLMs are not necessary?

• How do we collect or build synthetic datasets for our models in a privacy-safe way so we can continue to be one of the most privacy-sensitive browsers on the market

Overall you will...

• Scope and spearhead projects to fine-tune, distill, or train models for various features within Arc

• Push the boundaries of what on-device and privacy-safe AI and ML can be used for

• Work with Product Engineers, Product Designers, and Design Engineers to understand how we can use heuristics, neural networks, and LLMs to create magical experiences within Arc

• Build infrastructure to collect or generate training data for building or improving models

• Build ways for us to determine and track model performance and accuracy, and improve performance and accuracy over time
  

After 1 month you will...

• Onboard to the team and codebase with your onboarding buddy

• Attend a number of onboarding presentations on the company, product, codebase, and culture

• Get familiar with the Swift language, the Arc codebase, and how we ship features

• Discuss and start formulating our ML roadmap with our CTO

• Ship a few bug fixes and small improvements across our codebase and tooling

• Have pair programmed with a few people on the engineering team

• Be regularly posting product feedback about the browser in our #dogfooding channel
  

After 3 months you will...

• Be familiar with how we prototype and build new features, working with product engineers to brainstorm ways to use models to add intelligence to Arc

• Be familiar with our infrastructure and data pipelines

• Ship a few prototypes with existing, on-device models to test performance and viability

• Participate in product brainstorms to think about the future of Arc

• Regularly attend weekly engineering discussions about our architecture, how we do code review, code style, and more
  

After 6 months you will...

• Creatively solve problems with product engineers, using pragmatic solutions ranging from basic heuristics, regressions, ML models, to AI depending on the feature

• Drive projects from conception to production launch independently

• Own our infrastructure to collect training data and fine-tune models for our use-cases

• Have built out mechanisms to assess quality and performance, and be working with product teams to improve the effica