Engineering Manager, Machine Learning

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.

As our founding ML Engineering Manager at the Browser Company, you’ll build our strategy and define how we incorporate ML to advance Dia’s product vision. The browser holds most of the context about your workday, from memory of the tasks you’re trying to accomplish to access to all of your web apps. We use ML and AI to turn that context into high‑utility experiences that feel personal and improve over time, grounded in capabilities like computer‑use, memory, and web‑app integrations.

Dia’s Assistant doesn’t just execute actions, it also suggests and recommends tasks, and learns which choices drive real outcomes. We leverage modern training techniques such as SFT, RL, and GEPA to optimize these experiences while protecting privacy. To move fast and learn continuously, we’re building a first‑class ML developer environment: curated data pipelines, tight experiment loops, offline/online evals with telemetry, and one‑click deploys with strong observability.

Engineering Managers at Browser Company are hands‑on technical leaders who build high‑performing and psychologically‑safe teams with a diverse group of individuals. You’ll work closely with your team to make product decisions, prioritize work, ship features, and promote an engineering culture of knowledge sharing and mentorship. Engineering Managers follow the same onboarding pathway as an IC engineer to learn our product data, and build processes, then ramp into leadership.

• Define and build our ML strategy
  , sequencing bets that improve Dia’s assistant and measurable user outcomes. You’ll stay ahead of the latest AI advancements and translate them into strategic opportunities for Dia’s roadmap.

• Prototype, architect, and ship LLM‑powered features
  ; establish techniques to train models that improve over time and personalize experiences, partnering with Design and Product Engineering to balance quality, speed, and scale for real‑world use.

• Audit and evolve the ML stack and infrastructure - spanning Swift for on‑device inference and Python for models and tooling - to support both encoder and decoder model families across client and server. Over time, scale the system using fine‑tuned open‑source LLMs.

• Partner on privacy and security by working with Security and Infra on data stewardship, deployment strategies, and responsible scaling.

• Establish ML Developer Experience by building tooling and workflows for high quality data curation, experimentation (fine‑tuning, RL, prompting), evals, and continuous training to improve the models that power Dia.

• Support and build a talented team of machine learning engineers, helping them grow both technically and with a product mindset through fast iteration cycles. You’ll play a key role in contributing across the engineering org as we scale—owning processes, recruiting, and onboarding—and proactively improve architecture and practices to enhance performance, stability, and maintainability.