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 dont actually do a whole lot, theyre kind of just there.

They dont 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. Thats why were building one : a browser that can help us grow, create, and stay curious.

To accomplish this lofty task, were building a diverse team of people from different backgrounds and experiences. This isnt optional, its 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, youll build our strategy and define how we incorporate ML to advance Dias product vision. The browser holds most of the context about your workday, from memory of the tasks youre 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.

Dias Assistant doesnt just execute actions, it also suggests and recommend 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, were 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. Youll 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 Dias assistant and measurable user outcomes. Youll stay ahead of the latest AI advancements and translate them into strategic opportunities for Dias 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. Youll 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.

Browser context engine : learn a unified embedding space that fuses app integrations, enterprise connectors, and on-device signals to enable low-latency retrieval, routing, and personalization

Personalize the command bar : develop ranking, intent understanding, and context-aware suggestions; fine-tune and evaluate models to measurably boost relevance and engagement

Evolve our data flywheel by defining how we instrument product signals, design collection / storage pipelines, create labeling / evaluation loops, and continuously retrain to improve feature quality and personalization

6+ years of experience training, optimizing, and product ionizing modern ML models, especially ones that run in a real-world product environment (bonus if youve worked closely with transformer models)

3+ years mentoring and leading senior…