Our client helps organizations globally to build trust and speed into their business by matching the Speed of PrivacyOps to the Speed of Dev/BusinessOps. Clients include Patreon, Twilio, Coinbase, Robinhood, and Samara.
Design and implement end-to-end Machine Learning and Natural Language Processing models and systems to help construct the foundation on which our global data infrastructure will be built.
This role requires you to design and implement end-to-end Machine Learning (ML) and Natural Language Processing (NLP) models and systems to drive business impact. You partner with cross-functional stakeholders and customers to frame business problems as ML problems, prototype solutions effectively, and implement production-grade ML systems and the backend software systems they support to provide end-to-end five-star user experiences. Given you are constructing the foundation on which our global data infrastructure will be built, you need to pay close attention to detail and maintain a forward-thinking outlook as well as scrappiness for the present needs. You thrive in a fast-paced, iterative, but heavily test-driven development environment, with full ownership to design features from scratch to impact the business and the accountability that comes along.
- Scoping: Actively participate in customer engagements and partner with cross-functional stakeholders (legal product managers, customer success) to scope technical requirements for high impact business problems; determine whether ML is the right tool for the job and, if it is, how to frame the problem as an ML task
- Prototyping: Investigate different options quickly and thoroughly to identify the simplest, most pragmatic tool that drives business impact
- End-to-end System Design and Implementation: Gather training data; train, deploy, evaluate, and iteratively improve production-grade machine learning systems; implement and test the backend software systems they support to provide end-to-end five-star user experiences
- Follow and promote software engineering and machine learning best practices across the organization; keep up to date with the state of the art developments in NLP research, open-source frameworks, and MLOps
- Shape the direction of machine learning and build a cohesive team culture of ownership, growth, transparency, and customer focus
You are a good fit if you:
- Have a track record of delivering production-grade ML/NLP models and systems, specifically in text classification, entity and relation extraction, summarization, question-answering, and knowledge base construction (2+ years)
- Have strong software engineering skills, and set examples by writing modular and maintainable code considering design principles and applying sound testing practices
- Are comfortable with Python, and have experience with ML/NLP tools and libraries such as scikit-learn, PyTorch, TensorFlow, spaCy, Hugging Face, Gensim, etc.
- Have a systematic and goal-directed approach to project management
- Are comfortable dealing with ambiguity and ruthlessly prioritizing and managing your time with a sense of urgency
- Thrive in a self-directed environment with full ownership to design features from scratch to impact the business and the accountability that comes along
- Are proactive about continuous improvement and excited about learning at breakneck speed in a fast-growth environment; are eager to candidly and directly give and receive feedback to improve together as a team
- Are customer and mission-driven, motivated by bringing the most value as possible to users and shaping an industry from the ground up
- Are the ultimate team player: collaborate effectively with others, consistently make time to help your teammates, and are ego-less in the search for the best ideas