Lead Machine Learning Engineer

Lead Machine Learning Engineer
About US
We are looking for an incredibly bright Lead Machine Learning Engineer to help architect and grow the Lifion Platform Machine Learning capabilities.
If you have passion and hands-on experience solving challenges using machine learning, a strong record of accomplishment architecting and deploying scalable production systems and want to make a substantial positive impact in people's lives worldwide. In that case, this is an opportunity for you.
The role involves application of machine learning and other related techniques in areas of natural language processing, predictions and recommendations for better decision making, customer service, security, and more. If you are the kind of person who thrives in a challenging environment and has creative expertise and a thirst for pushing the limits, we are interested in you!
About You

• 5+ yrs experience as part of a data science, machine learning, team.
• Design, build, evaluate, ship, and refine the Lifion product by hands-on ML development.
• Collaborate with a cross functional agile team spanning data science, software development, product management, SRE and engineering to build new product features that advance our mission to create HR software that's global, scalable, and mobile.
• Advanced proficiency in Python and supporting numeric libraries including NumPy and Pandas.
• Experience in machine learning frameworks & toolkits such as Tensorflow, Pytorch, Sklearn.
• Strong experience building applied machine learning products, including taking a product through design, implementation, and production.
• Experience in delivering products with large-scale and productizing machine learning algorithms using SDLC
• Fluent in writing efficient SQL queries
• Strong coding standards with focus on writing high quality production grade code
• Experience with building real-time inference systems

Preferred Qualifications

• Demonstrated knowledge and ability working with AWS, Google Cloud, or other cloud-based solutions to train models, set up data pipelines, and set up inference engines.
• Experience with microservices and deployment of ML models using Kubernetes.
• Experience working with AWS tools like AWS Sagemaker is a plus.
• Understanding/Knowledge of the following concepts: feature stores, data lineage, A/B testing, model scoring/feedback
• Familiarity with concurrent programming such as threads and asyncio
• Familiarity with training pipelines and implementing automated (re)training for ML models

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