Pre-Sales Machine Learning Engineer, Customer Success – US (Remote) at Weights & Biases #vacancy #remote

San Francisco, CaliforniaCustomer Success Success ML Engineer /Full-time /RemoteAt Weights & Biases, our mission is to build the best developer tools for AI developers. Weights & Biases is a series C company with $250 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.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&Bs 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 customers 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.Responsibilities:Be an expert in implementing effective, robust, and reproducible machine learning pipelines for engineering teams using Weights & Biases toolsPartner 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 problemEffectively articulate W&B product best practices for instrumenting machine learning pipelines to our customers as a trusted advisorProvide product demos and workshops covering best practices & different solutions W&B offers to establish technical success in the evaluation processPartner 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 feedbackRequirements:2-3 years of relevant experience in a similar roleExperience using one or more of the following packages: TensorFlow/Keras, PyTorch LightningStrong programming proficiency in Python and eagerness to help customers who are primarily users of Python deep learning frameworks and tools be successfulExcellent communication and presentation skills, both written and verbalAbility to effectively manage multiple conflicting priorities, respond promptly and manage time effectively in a fast-paced, dynamic team environmentAbility to break down complex problems and resolve them through customer consultation and execution.Experience with cloud platforms (AWS, GCP, Azure)Experience with Linux/UnixStrong plusProficiency with one or more of the following packages: HuggingFace, Fastai, scikit-learn, XGBoost, LightGBM, RayExperience with hyperparameter optimization solutionsExperience with data engineering, MLOps and tools such as Docker and KubernetesExperience with data pipeline toolsExperience as an ML educator and/or building and executing customer training sessions, product demos and/or workshops at a SaaS companyOur Benefits: Flexible time offMedical, Dental, and Vision for employees and Family Coverage Remote first culture with in-office flexibility in San FranciscoHome office budget with a new high-powered laptopTruly competitive salary and equity12 weeks of Parental leave (U.S. specific) 401(k) (U.S. specific)Supplemental benefits may be available depending on your locationExplore benefits bycountry$139,000 – $186,000 a yearThe 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 …@wandb.com.#LI-Remote

Associated topics: business advisory, business analysis, business analyst, consult, customer, information technology consultant, market, sap, senior consultant, support analyst

Unix scikit-learn ray Customer success LightGBM Python Amazon Web Services (AWS) Hugging Face Azure Linux MLOps Google Cloud Platform (GCP) Docker xgboost Kubernetes Machine Learning TensorFlow keras

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