Pre-Sales Machine Learning Engineer, Customer Success - US (Remote)

San Francisco, California
Customer Success – Success ML Engineer /
Full-time /
At Weights & Biases, our mission is to build the best tools for AI developers. We founded our company on the insight that while there were excellent tools for developers to build better code, there were no similarly great tools to help ML practitioners build better models. Starting with our first experiment tracking product, we have since expanded our solution into a comprehensive AI developer platform for organizations focused on building their own deep learning models and generative AI applications.

Weights & Biases is a Series C company with $250M in funding and over 200 employees. We proudly serve over 1,000 customers and more than 30 foundation model builders including customers such as OpenAI, NVIDIA, Microsoft, and Toyota.

We're hiring a Machine Learning Engineer - Customer Success to help our customers solve difficult, real-world problems and engage in ground-breaking research by using our developer tools in their machine learning pipelines.

In this role, you'll be working with the most sophisticated ML teams in the world working on some of the toughest ML problems in computer vision, robotics, natural language processing, and more. This specific role will be focusing more on W&B’s engagement with Prospective customers who are evaluating W&B for their use case. You'll have the opportunity to work with ML teams across multiple industries to uncover their ML needs, improve their ML workflow, explore how W&B fits into their environment, collaborate on projects, and educate them on the best practices of our product. Specifically, the person in this role will be responsible for assessing the prospective customer’s technical requirement and specification, demonstrate W&B functionality that highlights those capabilities, designing and executing proof of values (as needed) and securing a technical success in the evaluation process.

Machine Learning Engineers on our customer success teams are critical to the success of our customers at Weights & Biases. You'll partner with Sales, Support, Product and Engineering teams to own the technical success of the pre-sales evaluation of W&B for our prospective customers, serving as the primary knowledge owner and face to our customers.

This is a perfect opportunity for anyone with machine learning experience, is customer-oriented, and is looking to work with the top ML companies in the world.


    • Be an expert in implementing effective, robust, and reproducible machine learning pipelines for engineering teams using Weights & Biases tools
    • Partner with our customers and prospects to uncover their desired outcomes and be the trusted advisor to help them evaluate the full potential of W&B in solving their problem
    • Effectively articulate W&B product best practices for instrumenting machine learning pipelines to our customers as a trusted advisor
    • Provide product demos and workshops covering best practices & different solutions W&B offers to establish technical success in the evaluation process
    • Partner with Account Executives to create processes for the pre-sales lifecycle (POVs, Demos, etc.)
    • Collaborate closely with Support, Product and Engineering teams to influence product roadmap based on customer feedback


    • 2-3 years of relevant experience in a similar role
    • Experience using one or more of the following packages: TensorFlow/Keras, PyTorch Lightning
    • Strong programming proficiency in Python and eagerness to help customers who are primarily users of Python deep learning frameworks and tools be successful
    • Excellent communication and presentation skills, both written and verbal
    • Ability to effectively manage multiple conflicting priorities, respond promptly and manage time effectively in a fast-paced, dynamic team environment
    • Ability to break down complex problems and resolve them through customer consultation and execution.
    • Experience with cloud platforms (AWS, GCP, Azure)
    • Experience with Linux/Unix

Strong plus

    • Proficiency with one or more of the following packages: HuggingFace, Fastai, scikit-learn, XGBoost, LightGBM, Ray
    • Experience with hyperparameter optimization solutions
    • Experience with data engineering, MLOps and tools such as Docker and Kubernetes
    • Experience with data pipeline tools
    • Experience as an ML educator and/or building and executing customer training sessions, product demos and/or workshops at a SaaS company

Our Benefits:

    • 🏝️ Flexible time off
    • 🩺 Medical, Dental, and Vision for employees and Family Coverage
    • 🏠 Remote first culture with in-office flexibility in San Francisco
    • 💵 Home office budget with a new high-powered laptop
    • 🥇 Truly competitive salary and equity
    • 🚼 12 weeks of Parental leave (U.S. specific)
    • 📈 401(k) (U.S. specific)
    • Supplemental benefits may be available depending on your location
    • Explore benefits by country
$139,000 - $186,000 a year
The US base pay for this position ranges from $139,000 USD per year in our lowest geographic market up to $186,000 USD per year in our highest geographic market. This position is eligible for additional variable compensation in the form of a bonus or commission component, which is dependent on personal or company performance. Weights & Biases is committed to providing competitive salary, equity, and benefits packages for all full-time employees. Individual compensation will be commensurate with the candidate's experience, qualifications, and geographic location.
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