Research Engineer

Key Skills

Python Programming, Machine learning techniques, Data science techniques, Deep Learning, Keras software library, Natural Language Processing (NLP), PyTorch, TensorFlow, Continuous Integration & Continuous Delivery - CI/CD, Algorithms, Github, Computer Vision (CV), GitLab

Job Description

Would you like to be part of our team of Research Engineers in San Francisco, designing and implementing machine learning models and algorithms for real life problems?

Research Engineers must accomplish the following:

• Design and implement highly scalable algorithms based on state-of-the-art Machine Learning, Deep Learning, and general Data Science.
• Report and present development work clearly and efficiently, both internally and externally (clients, different audiences, etc.), both verbally and in writing.
• Apply knowledge of relevant research domains along with expert coding skills to develop industrial solutions (POC, MVP, and fully deployed solutions) and products (platforms and frameworks).
• Adapt machine learning and neural network algorithms and architectures to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).
• Upon request, collaborate with the business development team in their pre-sales activities, including but not limited to presenting the company to new prospective clients, and representing InstaDeep in conferences/events.

Requirements:

• A B.S, M.S, or Ph.D in Computer Science, Machine Learning, Reinforcement Learning, or in a related field.
• 2-5 years’ experience or relevant qualifications.
• Excellent knowledge of Machine Learning and Deep Learning algorithms and models.
• Experience in developing and debugging production-level Python or similar languages.
• Experience with autograd libraries such as TensorFlow, PyTorch, Keras, or JAX.
• Proficiency with version control (Gitlab, Github) and best coding practices.
• Experience with CI/CD tools to automate building, testing and deployment of ML projects.

Nice to haves:

• Research and software engineering experience demonstrated via previous work, contributions to open source, or coding competitions.
• Advanced data science, data visualisation, and clustering knowledge.
• Reinforcement Learning, Computer Vision, Representation Learning, or Natural Language Processing experience.