Sr. Machine Learning Engineer Job Description Template
Our company is looking for a Sr. Machine Learning Engineer to join our team.
Responsibilities:
- Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders;
- Develop scalable algorithms and methods to provide real-time recommendations;
- Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver;
- Work with engineering teams to implement new models while considering functionality for recommendation output;
- Collaborate with Data Scientists to prototype new algorithms and design experiments for evaluation to enhance our marketing efficiency;
- Contribute to a culture of continuous improvement, and data driven results;
- Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models;
- Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your models;
- Discover data sources, get access to them, import them, clean them up, and make them “machine learning ready”;
- Participate in design discussions about new features and approaches to implementing new services;
- Take end to end ownership of Machine Learning systems – from data pipelines and training, to real-time prediction engines;
- Partner with data scientists to understand, implement, refine and design machine learning and other algorithms.
Requirements:
- Bachelor’s degree or higher (completed and verified prior to start) from an accredited university;
- Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark);
- Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code;
- Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering);
- BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience;
- Understand machine learning principles (training, validation, etc.);
- Knowledge of data query and data processing tools (i.e. SQL).