You're an engineer who has a science background or has worked with scientists running experiments, implementing production quality state-of-the art algorithms, and exploring large data sets. You are passionate about having a positive impact on the world. You are enthusiastic about working on some of the most challenging problems in AI and are a passionate builder of solutions that directly contribute to products. You want to advance the state of the art by working closely with scientists to implement novel approaches, and have the skillset to develop engineering solutions to solve real problems in production, at scale. You are excited by the opportunity and challenges posed by deploying AI at scale to track and understand events as they unfold.
Work as liaison between the AI Group and engineering teams across the company in defining the right production delivery channels and processes for state-of-the-art algorithms and models (via APIs, libraries, etc.).
Building an innovative and AI-centric platform to run streaming analytics at scale, using your familiarity with cloud and open source orchestration technologies.
Optimizing Deep Learning models for production, maximizing execution speed, while minimizing compute cost.
Work directly on all aspects of science problems with other members of the AI team: from inception, brainstorming, and reading of scientific literature, to data exploration and implementation of methods created in-house, and productization of these methods.
Work with the team in solving specific problems at scale in one or more of the following areas: Natural Language Processing, Information Retrieval, Complex Networks, Recommender Systems, Computer Vision, Machine/Deep Learning, etc..
Work closely with a diverse, interdisciplinary team to deliver value to customers (existing and new products).
Excel in placing a human-centered focus on the work (context, end-user impact, etc), finding solutions that work in practice and have significant impact.
M.S. in Computer science. Ph.D. studies in any field a plus.
Industry experience (5 or more years) as an Engineer required.
Experience optimizing and launching Deep Learning models
Experience in one or several of the following areas: Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Knowledge Representation and Management, etc.
Expertise in Python, Java, Scala or similar languages.
Expertise with machine learning and deep learning tools (TensorFlow, PyTorch, Scikit-learn, etc.) for experimentation and deployment.
Experience working with scientists, reading technical research papers, and implementing state-of-the-art methods in production.
Experience with Kubernetes, Kafka, Istio, Knative, Containers, FaaS