You will design, develop, and optimize ML model pipelines, and scale our models from research to production.
RESPONSIBILITIES
Building and improving our engineering infrastructure to enable and scale our applied algorithmic research and development. On a day to day, some of your responsibilities will include:
Design, Develop, Test and Maintain ETLs and services for integrating with our customers various data systems.
Design, Develop, Test and Maintain ETLs and services for building our own data processes to accommodate applied research needs as well as production needs.
Finding performance bottlenecks in our data pipelines and our machine learning pipelines and resolving them
Develop end-to-end algorithmic solutions for complex ML problems from research and training models, through design, development, evaluation and optimization.
Develop train and inference engine pipelines in a large scale distributed system.
Transform NLP and data related ML/DL algorithmic approaches into efficient and optimized production-ready solutions.
Design, Implement and Optimize ML/DL and research pipelines to improve algorithms performance.
Transform high-level product requirements into technical requirements
Brainstorm and prototype algorithmic improvements.
Work in an ambiguous environment and collect requirements from different personas in the company (Product, FE, Research, etc.)
Advise and collaborate with researchers on DL software engineering aspects (such as tools and practices).
Requirements: M.Sc. (Phd preferred) in Computer Science, Engineer, or equivalent, ideally with a thesis in deep learning
Proven track record in MLOps, ML/DL engineering
High proficiency in Python and its data science stack (Pandas, sklearn, etc.).
Deep understanding of artificial deep neural networks architectures, algorithms, infrastructure, tooling and practices, ideally in NLP/NLU/NLG.
Hands-on experience with design, implementation and optimization of deep learning models using common frameworks (TensorFlow, PyTorch, HuggingFace, etc.)
Model optimization techniques familiarity with testing and hyperparameter optimization tools and frameworks.
3+ years of hands-on experience in engineering in production environments
2+ years of experience in ML
Experience in the following technologies: Dockers, Kubernetes, Aws, MongoDB
End to end experience owning feature from an idea stage, through design, architecture, coding, integration, deployment, and monitoring stages
ADVANTAGES
NLP/NLU/NLG experience in document classification, text generation, summarization, NER
Proven ability of conducting reproducible applied research in ML
Working with medical data on healthcare data projects
Experience in the following technologies: Spark/Hadoop, Airflow, Redis, Kafka
Go programming language proficiency
This position is open to all candidates.