Principal Machine Learning Engineer (LLMs)

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, yet they can’t predict our next move, morph themselves to better suit our tasks, or proactively reduce repetitive tasks during your work day. At The Browser Company, we’re changing that by building Dia.

As a Principal Machine Learning Engineer, you’ll be working alongside product engineers, designers, and our cofounder and CTO, Hursh Agrawal, to build the next LLM-powered interface for the internet. You’ll collect datasets and build evals, fine-tune LLMs and smaller transformers like BERT, and iterate on our how we host models both in the cloud and on-device to improve latency and resource usage.

Overall you will...

• Fine-tune, distill, and optimize LLMs to improve performance, reduce latency, and enhance efficiency for on-device and cloud-based inference.

• Improve our on-device model architecture, leveraging frameworks like MLX, ONNX, and TFLite to ensure models run efficiently across different devices.

• Experiment with and integrate new LLMs, fine-tuning them for specific browser-based use cases while balancing quality, speed, and resource constraints.

• Build evaluation pipelines to track model performance, accuracy, and real-world effectiveness over time.

• Collaborate with product ops teams to build and improve datasets that accurately match product needs.

• Collaborate with product engineers and designers to prototype and ship AI-powered features that enhance user experience.

• Optimize inference strategies, including running models on-device, in the cloud, or in hybrid configurations to maximize throughput and resource usage.

After 1 month you will...

• Onboard to the team and codebase with your onboarding buddy

• Attend onboarding presentations about the company, product, codebase, and culture

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

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

• Have trained your first model, either improving an existing flow or enabling an entirely new one

• 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 Dia

• Be familiar with our cloud infrastructure and data pipelines

• Be familiar with how we run inference both on-device and in the cloud

• Be testing new prototypes with existing, on-device models to test performance and viability

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

• Be trained to interview candidates for roles at the Browser Company

• Be contributing to on-call rotations and jumping into incidents to support the team

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

After 6 months you will...

• Collaborate with our CTO and other ML and infrastructure engineers to shape the product roadmap

• Creatively solve problems with product engineers, using