Weights & Biases (51-200 Employees, 2 Yr Employee Growth Rate)

1-Year Employee Growth Rate | 2-Year Employee Growth Rate | LinkedIn | $200M Venture Funding

What Is Employee Growth Rate & Why Is It Important?


At Weights & Biases, our mission is to build the best developer tools for machine learning. Weights & Biases is a series C company with $200 million in funding and a rapidly growing user base. Our platform is an essential piece of the daily work for machine learning engineers, from academic research institutions like FAIR and UC Berkeley to massive enterprise teams including iRobot, OpenAI, Toyota Research Institute, Samsung, NVIDIA, Salesforce, Blue Cross Blue Shield, Lyft, and more.

Reporting to Stacey Svetlichnaya, the Deep Learning Engineer will test, explore, prototype, and prioritize different features to guide product development from our core users’ perspective.

The Deep Learning Engineer will partner closely with Product and Engineering, existing customers, and the broader ML practitioner community to deliver the most powerful, flexible, and intuitive tools for cutting-edge research in robust applications of deep learning.


    • Actively use W&B features in a variety of scenarios to provide internal product guidance.
    • Contribute ideas and feedback and help prioritize feature requests and bug fixes.
    • Design and build clear and compelling example projects to guide users and showcase our product to customers: end-to-end ML models with reproducible workflows, illustrative tutorials and how-tos for specific tasks in W&B, general explanatory blog posts, and presentations, etc.
    • Listen to and partner with our customers, academic users, and the broader community to understand and help prioritize their workflows in W&B
    • Maintain ontology of ML verticals, techniques, etc.
    • Keep the organization apprised of new developments in applied & theoretical ML
    • Prototype new features for visualization and analysis in cutting-edge deep learning research


    • Applied machine learning work in the industry: training, tuning, debugging, and deploying machine learning models integral to a product or service, in a collaborative team environment
    • Familiarity with a range of ML frameworks and domains (computer vision, natural language processing, reinforcement learning, statistics, etc)—generalization and a high learning rate are more important here than specific architectures or priors
    • Building internal tools and/or giving internal demos and interactive access to your ML models, e.g. sharing scripts, notebooks, or an endpoint to help your team visualize results, understand model performance, or evaluate improvement across versions
    • Caring deeply about the user experience for what you build: thinking through the details, anticipating and testing the edge cases, and considering future applications
    • Writing easy-to-follow code and effective documentation for your projects
    • Ability to clearly communicate your ideas to folks across a range of backgrounds and levels of technical knowledge
    • Strong writing and data visualization skills

Core skills

    • Here are some key characteristics that will help you thrive in this role:
    • Outgoing and friendly: Product research requires interfacing with all teams at Weights & Biases. We seek someone who is adept at communicating complicated material with everyone — from scientists and engineers to the general public. We also encourage all of our employees to connect with our users; you’ll love this role if you enjoy seeking users to better understand how they use our product.
    • Autonomous: We value employees who thrive in a self-directed environment and proactively find ways to improve processes and collaborate with team members or engaged users.
    • 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 product research at Weights & Biases is delivering feedback in a digestible format for all stakeholders involved — including engineers, product managers, and customers. Your organization skills and time management will be key to running this process well.

Why join us?

    • Top-tier machine learning teams rely on our tools for their daily work at companies including OpenAI, Toyota Research Institute, Lyft, Samsung, and Pandora.
    • You’ll never stop learning. This role gives you first-hand experience talking with leading researchers in the field, understanding their problems, and directly shaping the product direction.
    • Our experienced founding team has successfully built and sold ML tools in the past at Figure Eight, and their deep knowledge of our industry, empathy for our users, and skillful management is driving W&B to success.
    • Customers genuinely benefit from our tool. Here’s a quote from Wojciech Zaremba, Cofounder and Robotics Lead, OpenAI: “W&B allows to scale up insights from a single researcher to the entire team, and from a single machine to hundreds of them.”

Our 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)

We encourage you to apply even if your experience doesn’t perfectly align with the job description as we seek out diverse and creative perspectives. Team members who love to learn and collaborate in an inclusive environment will flourish with us. We are an equal opportunity employer and do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need additional accommodations to feel comfortable during your interview process, reach out at [email protected]

Tagged as: 51-200 Employees, Hide US-Only Jobs, Venture Funded

Job Overview
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