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Machine Learning Engineer, Tech Lead

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. As a Machine Learning Engineer at The Browser Company, you’ll be working alongside product engineers, designers, and our Co-Founder, Hursh Agrawal, to build the next LLM-powered interface for the internet. You’ll be working to improve our on-device models so that our Members are able to get answers fast and take the busy work out of their days.

Overall you will...

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

  • Innovate our on-device model architecture in MLX, ONNX, and other frameworks and model formats

  • Trial and improve new on-device models, including fine-tuning new LLMs, to be performant on a variety of machines

  • Build infrastructure to collect or generate training data for building or improving models in a privacy safe way

  • Create ways for us to determine and track model performance and accuracy to improve our app efficiency overtime

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

  • 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 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 Arc

  • Be interview trained and interviewing 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 pragmatic solutions ranging from basic heuristics, regressions, ML models, to AI depending on the feature

  • Own our on-device model architecture, updating it to try new models, change how we work with LoRA adapters, and optimizing it for performance and quality

  • 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 efficacy of our models and heuristics

  • Be mentoring and pair-programming with newer engineers to help them get spun up on the codebase

Qualifications

  • 8+ years of experience optimizing and productionizing modern machine learning models, especially ones that run in a real-world product environment (bonus if you’ve worked closely with transformer models)

  • You have production experience with a modern coding language like Python

  • You have experience with fine-tuning, distilling, and improving modern machine learning infrastructure and models

  • You're passionate about performance and efficiency and coming up with creative approaches to building a new kind of browser

  • You have experience independently running critical projects or initiatives with minimal guidance

  • You’re pragmatic, motivated by nebulous problems, and excited to work in a startup environment with quick product validation cycles.

  • We’re primarily focused on hiring in North American time zones and require that folks have 4+ hours of overlap time with team members in Eastern Time Zone.

Compensation and Benefits

💰 With our flexible compensation model, employees have the ability to choose the cash-to-equity ratio that best suits their individual needs. Every offer we extend includes three options: a salary-optimized offer, an equity-optimized offer, and a balanced offer.

The annual salary range for this role is $250,000 - $300,000 USD. The actual salary range offered will vary based on experience level and interview performance.

đŸ§˜đŸ»â€â™€ïž In addition to a competitive salary and equity package, we provide every employee with the following benefits:

  • comprehensive benefits package with employee medical, dental, and vision - we cover 100% of premiums for employees, and up to 95% for dependents

  • 401k plan

  • flexible vacation policy - on average, our team members take between 15-20 vacation days a year, plus federal holidays (holidays vary by location)

  • remote-friendly working environment - our core working hours are 11 AM-2 PM Eastern Time

  • 12 weeks of paid parental leave

  • $1,500 USD home office stipend

  • Employees based in the US also receive additional services like free annual memberships to One Medical (where available), Talkspace, Teladoc, and HealthAdvocate

The Browser Company is a well-funded, ambitious startup of about 85 people (and growing!) who are passionate about building great products. We are a remote-first, distributed team, with the option to work in office in New York. We strongly support diversity and encourage people from all backgrounds to apply. 

🚙 To read more about what we value as a company, check out Notes on Roadtrips on our blog.

Average salary estimate

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What You Should Know About Machine Learning Engineer, Tech Lead , The Browser Company

Join The Browser Company as a Machine Learning Engineer, Tech Lead, where we are dedicated to revolutionizing the way you interact with the internet. Our team is on a mission to build a browser that goes beyond just a tool – we aim to create an experience that helps you organize your life, fosters creativity, and ignites curiosity. As a pivotal member of our engineering team, you'll collaborate with talented product engineers and designers to design the next LLM-powered interface. Imagine being at the forefront of improving on-device models that empower our Members to save time and effort by reducing repetitive tasks. Your core responsibilities will include scoping projects to enhance transformer models and refining our architecture using advanced ML frameworks. You'll build infrastructure to securely collect training data and assess model performance, influencing the future of our cutting-edge product. By joining us, you'll be part of a diverse, inclusive team that values a variety of perspectives. We welcome applicants from all backgrounds, particularly women and people of color. If you're excited about tackling unique challenges and thriving in a dynamic startup environment, The Browser Company could be your next adventure. Together, we can redefine what a browser can do for millions worldwide!

Frequently Asked Questions (FAQs) for Machine Learning Engineer, Tech Lead Role at The Browser Company
What responsibilities does the Machine Learning Engineer, Tech Lead at The Browser Company oversee?

As a Machine Learning Engineer, Tech Lead at The Browser Company, your responsibilities encompass scoping and leading projects to enhance transformer models used in the browser, innovating on-device model architecture, and testing new models for performance on various machines. Additionally, you'll build infrastructure to generate training data and assess model performance, all aimed at improving user experience.

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What qualifications are required for the Machine Learning Engineer, Tech Lead role at The Browser Company?

For the Machine Learning Engineer, Tech Lead position at The Browser Company, candidates should have over 8 years of experience in optimizing machine learning models, particularly in real-world product environments. Proficiency in Python, experience with fine-tuning models, and a passion for performance and efficiency are essential. Candidates should also be able to manage critical projects independently.

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How does the compensation structure work for the Machine Learning Engineer, Tech Lead position at The Browser Company?

The Browser Company offers a flexible compensation model for the Machine Learning Engineer, Tech Lead role, allowing candidates to choose between salary-optimized, equity-optimized, and balanced offers. The annual salary range is between $250,000 and $300,000, depending on experience and interview performance. This model ensures that you can select a compensation package that aligns with your needs.

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What type of work environment can a Machine Learning Engineer, Tech Lead expect at The Browser Company?

At The Browser Company, the work environment is primarily remote, encouraging a healthy work-life balance. We maintain core working hours from 11 AM to 2 PM Eastern Time, while also supporting flexible vacation policies. Additionally, our team consists of diverse individuals, providing a welcoming atmosphere for collaboration and innovation.

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What supportive benefits are provided to a Machine Learning Engineer, Tech Lead at The Browser Company?

In addition to a competitive salary and equity package, The Browser Company offers a comprehensive benefits package, including 100% covered medical, dental, and vision premiums for employees, a 401k plan, and a generous remote work stipend. Employees also enjoy 12 weeks of paid parental leave and wellness services, tailoring support to individual needs.

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Common Interview Questions for Machine Learning Engineer, Tech Lead
Can you explain your experience with optimizing machine learning models in a production environment?

When asked about your experience with optimizing machine learning models, focus on specific projects where you successfully improved model performance. Describe the techniques you used, the challenges you faced, and the impact your optimizations had on the product. Highlighting your ability to work under real-world constraints will show your competency in this area.

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How do you approach designing an on-device model architecture?

In response to this question, outline your thought process for designing on-device model architectures. Discuss factors such as performance, scalability, and user experience. Reference any unique approaches you've taken in the past, and how they contributed to the overall efficiency of the product.

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What strategies do you employ for model training data collection?

Discuss various strategies for collecting training data that ensure privacy and efficacy. You can touch upon the importance of designing infrastructure that adheres to data privacy regulations, and share methods you've used to minimize bias in data collection. Concrete examples will strengthen your answer.

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Can you describe a challenging machine learning project you led?

When discussing a challenging project, describe the problem, your approach to finding solutions, the collaboration involved, and the project's outcome. Emphasize any leadership roles you took and how you guided your team through obstacles, showcasing your capability as a Tech Lead.

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What do you see as key factors for a successful machine learning initiative?

In your response, focus on critical factors such as teamwork, iterative feedback loops, technical rigor, and alignment with business goals. Share your experiences in fostering these conditions in past projects, highlighting how they contributed to successful outcomes.

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How do you ensure the effectiveness of machine learning models over time?

Talk about the periodic assessment of model performance using established metrics and A/B testing to understand model efficacy in real-world scenarios. Discuss how you've iterated on model design based on user feedback and performance data in previous projects.

Join Rise to see the full answer
How do you handle disagreements in a team setting related to machine learning approaches?

When addressing disagreement, emphasize the importance of open communication, thorough research, and collaboration. Share an example where your mediation led to a consensus, reinforcing your ability to contribute positively to a team's dynamic.

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What tools and frameworks do you prefer for machine learning model development?

In your answer, identify specific tools and frameworks you’re proficient in, such as TensorFlow, PyTorch, or ONNX. Discuss why you prefer these tools, emphasizing aspects like performance, usability, and community support that have contributed to your past successes.

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How do you stay updated on the latest developments in machine learning?

Speak about how you read research papers, participate in online forums, and attend related conferences. Highlight any professional networks you belong to and how these resources support your ongoing learning and development in the field of machine learning.

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Describe your experience working with diverse teams in a remote-first environment.

In your response, discuss the challenges and benefits of working with diverse teams. Share how you promote inclusion and ensure all voices are heard in collaborative settings, illustrating your commitment to fostering a positive team culture, especially in a remote setup.

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The web browser is one of the most important tools we use — not just on our computers, but in our lives. The world has changed in the past 15 years, but our web browsers look and behave pretty much th...e same. We think it’s time to push the web b...

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DATE POSTED
December 22, 2024

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