Machine Learning (ML) Data Scientist
Who we are
AmplioAI is a motivated start-up operating at the bleeding edge of Artificial Intelligence. We use AI to enhance athletic performance by providing athletes and their coaches dynamic data tracking, performance projections, and insights. This detailed information generates the metrics needed to identify performance strengths as well as weaknesses. This means both the coaches and athletes can create optimal training regimens and better prepare for matchups and competitions. We are currently perfecting our craft in the team-based sector but are planning to expand to the mass market shortly. Allowing more individuals to make use of their athletic data insights.
Detailed information can be viewed here:
AmplioAI is part of the G11 Technology Partners (g11.tech) family. G11 helps to connect top notch engineers with quality firms.
What we need
We are currently seeking a ML engineer. Your primary function is working as a data scientist with a strong focus on applied machine learning for data pipeline development and maintenance. The ideal candidate will have experience writing in SQL and PHP for data pipelines. You will be working on ML problems that use state-of-the-art neural network architectures and take data from several multi-modal input sources.
In contrast to other "AI" companies whom often just run canned scripts or off-the-shelf models, you will be producing bleeding edge and dynamic applications. Instead of performing a mundane list of tasks, you will be required to think about the data sources deeply. This includes asking questions such as, what types of inferences are important? What type of representations are there to learn? Or, how does this all tie together into a final product that meets the consumer's needs? Bring your superb critical thinking and reasoning skills, not just your script running fingers.
You will be expected to start by February 2020. This is a full time, 100% remote position. The ideal candidate will have the ability to work in a fast-paced start-up environment. This is a Long-term commitment (1+ years).
Extensive experience in PyTorch, Python, and/or R
SQL and PHP
Experience working with neural networks
Ability to develop and maintain state-of-the-art models
Strong critical thinking and reasoning skills, including the ability to think about what learned representations are most useful to the final product
Help to identify and/or come up with new metrics to address needs even before they are known or voiced
Strong data and machine learning experience or capabilities
Able to process product/business vision and apply to development.
Ability to quickly utilize state-of-the-art Machine Learning models and incorporate them into production
Ability to quickly read, understand, and implement ideas from new machine learning papers
Ability to reason about ideas in new Machine Learning papers, and implement modifications to them that suit specific product needs
Ability to scale up and automate Machine Learning pipelines so that they run independently on remote servers (i.e. making use of Amazon Web Services or Google Cloud.)
General programming acuity (i.e. you may have to modify data pipelines, application code, C++ implementations)
Within 3 months, you will:
Help reason and develop good data representation, metrics, and models for:
Running nutritional models
Yoga pose algorithms
Be prepared for assignment to one or two of these projects/products. Clients will include top-tier university athletic programs and billion-dollar apparel companies.
Within 6 Months, you will:
In addition to continuing work listed above:
Help maintain and develop our adaptation and progression algorithms for each data modality/source
Ensure and discuss with athletic domain experts on what is/could be useful from a progression standpoint
Within 12 Months, you will:
In addition to all of the above:
Research and break down analytically what is most useful for users at scale
Be able to reason about the trade-offs between metric accuracy and computational speeds, and what computational bottlenecks are happening at scale
Make decisions that best balance these tradeoffs such that it gives our end-users the best experience and aligns with our future product vision and direction
Do the same sort of work that was done in the first 6 months for new products, projects, data modalities, and clients that we pick up