Job Description
As a machine learning engineer, a large portion of your time and energy will be focused on a cross-functional team building scientific models for the modern era. The organizations that you collaborate with will range from high tech, global life sciences companies to leading research institutions. You will be a vital part of a world-class team that combines the product design and engineering expertise of the world s top software companies with the scientific expertise of the world s top research institutions. You ll be providing technical feasibility assessments to the Product Manager and Designer to ensure that we can build what we say we re going to build. Machine Learning Engineers at Attinad Technologies work on a variety of problems, connecting our customers desired outcomes to concrete deliverables. Every project is different, from reinforcement learning to computer vision, and you ll experience a breadth of industries. Our teams are highly collaborative, and you ll work closely with customers, data and software engineers, and product managers. We are highly collaborative, oriented towards building and learning, and keenly aware of the responsibility of helping our customers deploy software products for real end users. This opening is for Machine Learning Engineers of all levels, and our interview process will evaluate your background and experience to assess your current level and enable you to be successful in your role here.
You will have a high degree of autonomy as you work with a nimble, growing team, but some core responsibilities may look like the following.
- Engage with our customers to understand the challenges they are facing, and work with them to produce a strategy and execution plan for their AI goals.
- Define the work that you and other members of your team will execute on, and be able to break down and organize the work appropriately.
- Build and design machine learning pipelines, both consolidating existing databases and building new databases using tools like Python, AWS, SQL, MLFlow, PyTorch, Spark, and more.
- Create design documents that lead to MVPs, and continue iterating on the MVPs into fully developed products.
- Choose machine learning models and evaluate performance in production.
- Expose your machine learning predictions through APIs and Applications developed with other team members.
- Share your knowledge with other team members.
- Learn about new areas of machine learning and other parts of our product development stack as suits your career goals.
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