Want to solve the huge problem of shortage in AI and Data Science talent? Passionate about democratizing AI by bringing AI and Data Science education to under-resourced communities worldwide? Interested to augment the skills of professionals by integrating AI into corporations’ core training programs? Come talk to us!
Fusemachines (http://www.fusemachines.com) is a rapidly growing startup that provides world class AI education in underserved communities using it’s proprietary content and learning platform, and provides the graduates opportunities to work with global clients solving fascinating AI problems..
PhD degree in computer science or a related field.
Overall 5+ years of experience in the technology industry with 5-7 years working on Machine Learning & Statistics projects.
Proven track record of research skills with peer reviewed papers published
Experience leading agile development and engineering efforts from initial envisioning and epic construction to product deployment.
Strong demonstrated experience leading diverse teams through rapid development cycles and producing high-quality software products and services at scale.
Knowledge of Machine Learning pipelines - data ingestion, feature engineering, modelling, predicting, explaining, deploying and monitoring ML models.
Experience leading teams working with Python, or R languages and general software development skills (source code management, debugging, testing, deployment, etc.)
Knowledge of one or more toolkits such as sklearn, MXNet, Keras, Tensorflow, PyTorch, NLTK, OpenCV, spaCy, etc.
Knowledge and project-level experience with Azure, GCP and/or AWS.
Drive detailed-level design sessions and prioritize and organize work across releases, iterations, and sprints.
Report and analyze engagement outcomes and for continuous improvement and cycle-time reduction
What you’ll do:
Invent new algorithms to solve various problems. New algorithms need to be publication worthy
Design and build models, data pipelines and production-level machine learning (ML) infrastructure, using tools such as sklearn, TensorFlow, PyTorch, Kubernetes, Kubeflow Pipelines. Leverage your experience to drive best practices in ML systems and data engineering.
Bring ML experiments from Notebooks to production. Deploy ML models under the constraints of scalability, correctness, and maintainability.
Collaborate with cross functional agile teams of machine learning engineers, data engineers, and others in building machine learning systems.