Our prestigious client is an artificial intelligence (AI) biotech company founded in Silicon Valley with a proprietary technology platform that accelerates pharmaceutical drug development. Its exceptional team is an interdisciplinary fusion of top scientific talent from Harvard and Stanford, world-class technical engineers from Google and Intel, experienced medical professionals from Genentech and Kaiser Permanente, and successful serial entrepreneurs from Silicon Valley and international healthcare.
In support of an ambitious expansion plan in Asia, anchored by a new R&D and business center in Singapore, the company is seeking a Machine Learning engineer to join their interdisciplinary team developing tools that revolutionize pharmaceutical drug development. This role will design and build machine learning platforms to analyze medical image data, develop algorithms to address research questions, and engineer data pipelines to ingest and transform large scale biological data.
You should have experience implementing machine learning algorithms and building data infrastructure. You should also have the ability to collaborate cross-functionally to complete projects with minimal guidance, flourish in uncertainty and ambiguity, and strive for continued learning to build upon craft.
What we envision the role to be:
Design and develop systems for embedded and cloud platforms to solve complex and as-yet undefined machine learning problems
Build data infrastructure that enables data ingestion, cleaning, transformation, and feature extraction
Collaborate across research and engineering disciplines, and assess technology tradeoffs required to rapidly deliver software solutions
Work quickly and independently with minimal oversight in a workspace that encourages pairing and knowledge sharing
What we hope to find in you:
Machine Learning: Strong background implementing Machine Learning algorithms (deep learning, convolutional networks, recurrent networks / LSTMs, model ensembling) using current ML frameworks (Keras, Tensorflow, SciKit)
Data Engineering: Strong understanding of ML data pipelining and how to develop infrastructure and tools using current large-scale data processing technologies
Software Development: Proficient coding and software engineering skills in at least one of the following: Python, Go, or C/C++
Computer Vision / NLP: Familiarity with the fundamentals of computer vision and natural language processing approaches
Problem Solving Mindset: Ability to thrive in uncertainty and apply knowledge and experience to solve problems to novel areas – learning and applying new tools and technologies as needed
Collaboration: Successful track record of communication, collaboration, and ownership in challenging cross-functional projects