Machine Learning Software Engineer 1

Your Journey at Crowe Starts Here: At Crowe, you can build a meaningful and rewarding career. With real flexibility to balance work with life moments, you’re trusted to deliver results and make an impact. We embrace you for who you are, care for your well-being, and nurture your career. Everyone has equitable access to opportunities for career growth and leadership. Over our 80-year history, delivering excellent service through innovation has been a core part of our DNA across our audit, tax, and consulting groups. That’s why we continuously invest in innovative ideas, such as AI-enabled insights and technology-powered solutions, to enhance our services. Join us at Crowe and embark on a career where you can help shape the future of our industry.

About the Role· We work on medium-term machine learning projects (1-6 months) with a high degree of autonomy. In order to further the Data Science Team strategic imperative of having a “Culture of reuse: tools, products, and processes.”, the primary strategy of the Toolkit Team will be to develop generalizable tools for common problems and facilitate the sharing of code within/between projects. Work within the Data Science Team to define common problems and identify opportunities in the existing codebase to solve current and future problems Develop libraries and services to address common requirements for our machine learning products Collaborate with data scientists to get their machine learning models from prototype to production Champion the use and adoption of Toolkit libraries and services Required Skills - Programming experience. Your job will require you to code. We mainly write in Python, so experience in this language is preferred. Familiarity with another lower-level language (C/C++, Go, etc) is a plus Software engineering experience. You enjoy software engineering and know your way around algorithm design and data structures. You are comfortable working in Linux-based systems and are familiar with using Docker to develop and deliver RESTful APIs. Additional experience in areas such as cloud computing, distributed systems, CI/CD, etc., is a plus. Capacity for autonomy. You won’t be on your own on day one, but you will need the capacity to manage your time between research and tool development. Strong communication skills. You are comfortable adjusting how you discuss software engineering projects, from the technical details to high-level concepts, depending on your audience of software engineer teammates, data scientist collaborators, and non-technical business stakeholders. Adaptable. Toolkit problems will involve different areas of computer science and machine learning skills, sometimes within the same problem. You can context shift depending on the problem and can adjust if new problems and/or requirements arise requiring rethinking the approach to solving a problem. Detail oriented. Toolkit is focused on making tools that just work. You like to find edge cases and potential bugs in code, you go above and beyond developing use cases beyond the requested basics, and you are meticulous with following software engineering best practices, including writing maintainable code with clear documentation and comprehensive test cases. Skills to Stand Out. You like to code and have interest in learning new technologies. You find it exciting to explore and/or improve libraries to help you solve a problem. Lifelong engineering. You want to stay up to date with regards to practicing engineering and you strive for continuous learning and improvement. Curiosity about machine learning. You know a few things about learning algorithms, data preprocessing strategies, or some other interesting areas of machine learning that can help create more integrated and robust software solutions. Outside field. You have an educational, research, or hobby background in an interesting area of software engineering, computer science, or related field that you believe gives