About the job
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 at Relyance 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
- Have strong software engineering skills, and set examples by writing clear, concise, 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, 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 deeply curious, 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