Machine Learning Engineer
What We Do
Uptake helps industrial companies digitally transform with open, purpose-built software that delivers outcomes that matter. Built on a foundation of data science and machine learning, our vision is to create a world that always works — one where the machines and equipment we depend on daily don’t break, and industrial companies are once again the creators of economic growth and opportunity.
Why Work Here
Uptake is a values-driven organization, and we are excited about what we do. We’re flexible, honest, hardworking, and collaborative. As a team, we bring our diverse backgrounds, beliefs, and experiences together to solve tough, important problems. We support and challenge one another to bring out the best in each of us, and we might have a little fun along the way. We’re also proud to be one of Chicago’s best places to work in 2018 according to Forbes and Great Place to Work Institute!
We offer generous benefits including health, dental, vision, parental leave, 401K match, and unlimited vacation. We are lifelong learners, and our Uptake University program offers training and professional development on a wide variety of topics. We also have employee-led community groups including [email protected], [email protected], [email protected], [email protected], and many more. Learn more at https://www.uptake.com/careers.
What Machine Learning Engineers Do Here
Data science is at the core of what we do at Uptake. Machine Learning Engineers are part of the Data Science team working to build the core Machine Learning and AI tools that will power Uptake’s future.
Typical day to day tasks for a Machine Learning Engineer might include:
- Develop Machine Learning Tools to power the Industrial World by partnering with data scientists to identify common usage patterns and build tools to accelerate ML model deployment lifecycle
- Collaborate with Data Science, Product, and Engineering stakeholders to define and implement new data powered products, and deploy and maintain them in production
- Evangelize software engineering best practices and proactively identify technical debt and other issues to harden our products
- Seeking out ways to automate and accelerate development of data driven products, and then implementing them
- Learning new skills and technologies through Uptake University courses, reading industry papers, or pair-programming with coworkers on other teams, to ensure the team is always cutting edge
For more on what we look for on the data science team, visit https://upt.ac/16ee15fc.
What We Are Looking For
- 4+ years of applied software engineering experience
- Applied experience using Scala, Java, or similar JVM based languages
- Applied experience using big data technologies for production, such as Spark, and HDFS
- Applied experience with RDBMS systems for OLTP workloads, such as Postgres or MySQL
- Applied experience working with web based APIs
- Familiarity with dynamic languages, such as Python or R
- Familiarity with data science workflows, and concepts like EDA, training, and scoring
- Candidates must be authorized to work in the US.
Nice to Have
- Have made substantive contributions to open source projects in the areas of data science or machine learning
- Experience deploying and maintaining Machine Learning models into production
- Experience in predictive modelling; for example, in Kaggle or other Data Science Competitions
- Experience with stream processing with tools like Kafka or Flink
- Experience with container technology, like Docker
- Experience working on concepts like AutoML or end-to-end learning
- Experience working in one of the following industry areas:
- Industrial technology
Uptake welcomes and encourages applications from all individuals, without regard to any prohibited ground of discrimination, including from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.