About Paperspace\n\nPaperspace is a cloud computing company creating simple and scalable accelerated computing applications. Our goal is to allow individuals and professional teams to build applications with ease -  from Machine Learning to 3D graphics.\n\nPaperspace is backed by leading investors including Y Combinator, Initialized Capital, Battery Ventures, and Intel Capital.\n\nOur Team \n\nPaperspace values technical excellence in an open and inclusive environment. The team is primarily based in NYC, but we have a strong remote/hybrid team. Communication is paramount and mutual respect is at the core of our collaborative work environment. We are also committed to building a team that represents a variety of backgrounds, perspectives, and skills. We believe creating a more diverse team directly impacts our ability to collaborate effectively, build a better community, and produce better products.\n\nThe Role\n\nIn this role, you'll be working with teams working on applied ML problems across a range of use cases such as computer vision, robotics, natural language processing, and more. Your responsibility will be to develop models for both internal and customer-facing applications with the Gradient platform. You'll have the opportunity to collaborate with ML teams across several industries to improve their workflow and educate them on ML best practices. \n\nYou'll partner with Customer Support, Product, and Engineering to develop in-house ML expertise and help our customers on-board and adopt a modern MLOps methodology. You'll help drive adoption, understand innovative customer use cases, and serve as the primary problem solver in our customers' ML workflows. In addition to supporting our customers, you will have access to a cloud-scale GPU platform to research and develop state-of-the-art ML systems.\n\nThis is a perfect opportunity for anyone who has machine learning experience, is customer-oriented, and is looking to work with the top ML companies in the world. If you enjoy working with highly technical engineering teams and thrive in an autonomous environment, we encourage you to apply.\n\nWhat you'll be doing\n\n• Be an expert in implementing effective, robust, and reproducible machine learning pipelines for ML teams using our platform\n• Effectively articulate best practices for instrumenting machine learning pipelines to our customers \n• Partner with our customers to uncover their desired outcomes and be the trusted advisor to help them realize the full potential of Gradient in solving their problems\n• Partner with the Sales Engineering team to ensure there's a smooth transition from POC to when a new customer is onboarded. \n• Create processes for the post-sales lifecycle (Onboarding/Training, Adoption, Workshops, Demos, etc.)\n• Develop and refine product documentation\n• Develop sample projects that can run seamlessly on the platform\n• Collaborate closely with Support, Product, and Engineering teams to influence product roadmap based on customer feedback\n• Actively use Gradient features in a variety of scenarios to provide internal product guidance\n• Contribute ideas and feedback and help prioritize feature requests and bug fixes\n• 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 Gradient, general explanatory blog posts, etc\n• Listen to and partner with our customers, academic users, and the broader community to understand and help prioritize their workflows in Gradient\n• Keep the organization apprised of new developments in applied & theoretical ML\n• Prototype new features for visualization and analysis in state-of-the-art deep learning research\n\nWhat we're looking for\n\n• 3+ years of experience in a professional working environment in an ML Engineer or adjacent role\n• Experience using one or more of the following packages: TensorFlow/Keras, PyTorch\n• Strong programming proficiency in Python and eagerness to help customers who are primarily users of ML/DL frameworks and tools be successful\n• Excellent communication and presentation skills, both written and verbal\n• Ability to effectively manage multiple conflicting priorities, respond promptly, and manage time effectively in a fast-paced, dynamic team environment\n• Ability to break down complex problems and resolve them through customer consultation and execution.\n• Experience with cloud platforms (AWS, GCP, Azure)\n• Experience with Linux/Unix\n• Applied machine learning experience in the industry: training, tuning, debugging, and deploying machine learning models integral to a product or service, in a collaborative team environment\n• Familiarity with a range of ML frameworks and domains (computer vision, natural language processing, reinforcement learning, statistics, etc)\n• 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\n• Caring deeply about the user experience for what you build: thinking through the details, anticipating and testing the edge cases, and considering future applications\n• Writing easy-to-follow code and effective documentation for your projects\n• Ability to clearly communicate your ideas to folks across a range of backgrounds and levels of technical knowledge\n• Strong writing and data visualization skills\n\nStrong Plus\n\n• Proficiency with one or more of the following packages: TensorFlow/Keras, PyTorch, HuggingFace, Fastai, scikit-learn, XGBoost, Jupyter, \n• Experience with hyperparameter optimization solutions\n• Experience with data engineering, MLOps, and tools such as Docker and Kubernetes\n• Experience with data pipeline tools\n• Experience as an ML educator and/or building and executing customer training sessions, product demos,\n and/or workshops at a SaaS company\n• You have thought deeply about your role in the future of artificial intelligence/technological advancement and want to help make machine • learning more accessible, transparent, and collaborative\n• Experience contributing to architecture and systems design\n• Teaching experience\n\nWhy join us? \n\n• Top-tier machine learning teams rely on our tools for their daily work\n• This role gives you first-hand experience talking with leading researchers in the field, understanding their problems, and directly shaping the product direction.\n• Customers genuinely benefit from our tool. Over 500K users have used Paperspace, and over 22% of signups are from referrals.\n• A best-in-class product in one of the fastest-growing and largest market segments\n\nBenefits\n\n• Multiple health care insurance options with premium plans in addition to vision and dental insurance plans\n• 401(k) Plan with employer matching\n• Commuter benefits with a contribution from the company \n• Responsible Time Off Policy \n• Generous and flexible parental leave\n• Fitness & wellness benefit\n• Remote friendly and hybrid office environment for New York team members\n\nWe are an equal opportunity employer that values and welcomes diversity. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.\n\n#LI-Remote \n\n#Salary and compensation\n No salary data published by company so we estimated salary based on similar jobs related to Cloud, Machine Learning, Senior, Marketing, Sales, Engineer, Front End, Teaching, Python and SaaS jobs that are similar:\n\n $70,000 — $120,000/year\n
\n\n#Benefits\n 💰 401(k)\n\n🌎 Distributed team\n\n⏰ Async\n\n🤓 Vision insurance\n\n🦷 Dental insurance\n\n🚑 Medical insurance\n\n🏖 Unlimited vacation\n\n🏖 Paid time off\n\n📆 4 day workweek\n\n💰 401k matching\n\n🏔 Company retreats\n\n🏬 Coworking budget\n\n📚 Learning budget\n\n💪 Free gym membership\n\n🧘 Mental wellness budget\n\n🖥 Home office budget\n\n🥧 Pay in crypto\n\n🥸 Pseudonymous\n\n💰 Profit sharing\n\n💰 Equity compensation\n\n⬜️ No whiteboard interview\n\n👀 No monitoring system\n\n🚫 No politics at work\n\n🎅 We hire old (and young)\n\n
\n\n#Location\nNew York City