We are looking for a Jr. Machine Learning Engineer to join an established team of Machine Learning professionals who are designing and building cutting-edge machine learning applications. You will bring professional experience, scaling and deploying robust production code and data pipelines. You will be working with a team of brilliant researchers and engineers from across the globe, dedicated to bringing our ML and AI research to production, to keep Neurog at the forefront of the DevOps vendor landscape. You will be paired with experienced researchers/engineers, and given increasingly difficult tasks to stretch your skills and build your talents. We offer an inclusive learning environment, with a balance of tasks you should know how to do, and stretch goals to improve your understanding.
What will be the main focus of this role?
You will come in and help design and build machine learning solutions that will be used for commercial data science work at scale. In addition, you will help bring in best practices for engineering, including research and adoption of new tools, open-source technologies, and cloud platforms where they fit business needs. This Jr. ML Engineer role will make use of your computer science expertise as well as ML Ops skills.
If you were working for this team, here are some of the things you might have done in the last 6 months:
- Build and deploy deep learning pipelines
- Assist cross-functional data squads in improving performance, deployment times, scalability, and reliability of machine learning models
- Automate or improve existing code, data science work, testing, model validation, optimization, difficulties with scalability, or speed up run-times
- Develop APIs to help other analytics or technology teams in the business consume and use data science products
- Read and implemented state-of-the-art publications
What skills and experience do you need to apply?
- A minimum of 1 year of professional experience in Machine Learning Engineering with a modern stack
- Advanced programming skills (particularly in Python and C/C++)
- Proven ability to build, train, optimize and evaluate deep learning models
- Experience in producing meaningful insights from large structured and unstructured datasets
- Strong data pipeline and machine learning pipeline experience
- Up to date knowledge of recent advances in the field of deep learning
- Good communication skills and confidence in sharing and presenting ideas with senior engineers and data scientists
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