Software Engineer, Weights & Biases
Registry- Weights & Biases
$109-160k
+ Discretionary bonus + Equity + ESPP
MLOps platform for developers
Be an early applicant
MLOps platform for developers
201-500 employees
Be an early applicant
$109-160k
+ Discretionary bonus + Equity + ESPP
201-500 employees
Company mission
Weights & Biases's mission is to build the best tools for machine learning.
Role
Who you are
- 2-4 years of experience building and maintaining user-facing web applications
- Experience with modern web development tools and frameworks, such as React, TypeScript, GraphQL
- Solid understanding of programming fundamentals and experience with at least one modern language (e.g., Go, Python, C++, Java, Javascript)
- Enthusiasm for learning how to build reliable, high-quality software through hands-on experience, mentorship, and feedback
- Curiosity and persistence in understanding ambiguous problems and exploring potential solutions with a tendency towards action
- Ability to work with product and customer-facing teams to translate user feedback into technical solutions
- Interest in the AI/ML space and motivation to grow in a fast-paced, mission-driven environment
- Outgoing and friendly: You'll love this role if you enjoy connecting with real users day to day, helping them solve issues and understand good patterns for using our tools. Day to day you'll be answering questions and requests with a kind, thoughtful tone that makes users feel appreciated and connected to our team
- Autonomous: If you work well in a self-directed environment and proactively find ways to improve processes and collaborate with team members or engaged users, your initiative will really shine in this role
- Curious and driven: Explore machine learning and learn more about the engineering stack and common ML workflows. Solve problems in both fast-paced, short-term sprints and in larger, more long-term projects
- Organized: A core part of engineering support at Weights & Biases is organizing feedback from many channels into a single, orderly stream. Your organizational skills and time management will be key to running this process well
What the job involves
- Reporting to the Engineering Manager, you will help evolve the Registry—the system ML and product teams use to catalog, discover, and manage models, datasets, and related assets across the organization
- The team is now focused on scaling the platform for high-throughput enterprise use
- You’ll implement well-scoped features across the stack, learn from senior/staff engineers, and contribute to a product used by some of the largest ML teams in the world
- As a full-stack engineer, you will collaborate with engineers, product, and design to ship user-focused improvements quickly and safely
- As the platform grows, you’ll help build the foundations that make these capabilities reliable and delightful
- Deliver well-scoped features and projects with growing independence (frontend, backend, or both)
- Contribute to executing the Registry roadmap: implement new functionalities, build accessible, performant components; and smooth out rough edges
- Apply system design principles at a basic level to build scalable and maintainable solutions
- Identify and propose small improvements in code quality, performance, or team processes
- Collaborate with product and design to translate user needs into clear technical implementations
- Contribute to code reviews, providing thoughtful feedback and learning from peers
- Proactively learn new technologies relevant to the Registry platform and share knowledge with the team
- Help onboard new hires or interns by sharing context and team practices
- Participate actively in team discussions, offering input on design, implementation, and trade-offs
Our take
Weights & Biases (W&B) builds development tools for machine learning, helping businesses to create and use deep learning models. In order for deep learning to work, it’s necessary for companies to be able to monitor how the models are behaving as they are trained. With over 200,000 customers, W&B has differentiated itself from its competitors such as IBM Watson Studio and Valohai by focusing exclusively on machine learning practitioners and not on sales aspects of the technology, and by its competitive pricing.
The company has also launched a product to help different parties collaborate on machine learning models. This is geared at researchers, reducing the need for them to rerun their training examples repeatedly. Over time, this will help to move machine learning technology forward. What's more, W&B's technology is free for personal and academic use, which has fueled adoption enormously, and it is now being used by research institutions such as UC Berkeley and enterpise companies like Samsung and Salesforce.
The dependence on machine learning is growing as more and more companies begin to use artificial intelligence to handle business tasks, so demand for this sort of software is rising. Having raised substantial funding, W&B is moving to futher develop its product, grow its team and increase its marketing reach.
Steph
Company Specialist at Welcome to the Jungle
Insights
31% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Aug 2023
$50m
LATE VC
Oct 2021
$135m
SERIES C
Total funding: $250m
Company benefits
- 🏝️ Unlimited vacation time
- 🩺 100% Medical, Dental, and Vision for employees and Family Coverage
- 🏠 Remote first culture with in-office flexibility in San Francisco
- 💵 $500 home office budget with new high-powered laptop
- 🥇 Truly competitive salary and equity
- 🚼 12 weeks of Parental leave
- 📈 401(k)
Company HQ
SoMa, San Francisco, CA
Leadership
Chris Van Pelt
(CISO)Earned a BA in Computer Science at Hope College. Previously Lead Engineer and Founder of Figure Eight (acquired by Appen).
Lukas Biewald
(CEO)Earned a BS in Mathematics and MS in Computer Science from Stanford University. Previously Research Scientist and Founder, CEO of Figure Eight (acquired by Appen).
Shawn Lewis
(CTO)Earned his BS in Computer Engineering at Virginia Tech. Previously Engineer at Google and Founder at Beep Networks.
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