Our Firm



Temporary Machine Learning Engineer

Entry Level

MINDBODY | Products & Engineering

As a Machine Learning Engineer, you will be responsible for helping to design, develop, and debug the MINDBODY software infrastructure at the heart of MINDBODY’s platforms.  You will apply machine learning algorithms to develop new models as well as enhance existing ones, ushering solutions all the way from concept to production.  You will have the opportunity to work closely with experienced co-workers in a fast-paced work environment.  Machine Learning Engineer responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and programming. The Machine Learning Engineer’s ultimate goal will be to shape and build efficient self-learning applications.


  • Study and transform data science prototypes into productionized systems
  • Design and develop machine learning and deep learning systems
  • Research and implement appropriate Machine Learning algorithms and tools
  • Develop machine learning applications according to requirements
  • Select and help design appropriate datasets and data representation methods
  • Run machine learning tests and experiments
  • Perform statistical analysis and fine-tuning using test results
  • Collaborate with other development teams to support the integration and deployment of Machine Learning products and services
  • Train and retrain systems when necessary
  • Extend existing Machine Learning libraries and frameworks
  • Keep abreast of developments in the field
  • All other duties as assigned
Skills & Requirements

  • BS in Computer Science, Mathematics or similar field preferred; technical certification or equivalent accepted
  • Master’s degree is a plus
  • Understanding of all stages of developing machine learning-based systems: labeling data, selecting algorithms, training, model development, visualization, regression, updates and debugging
  • Understanding of data structures, data modeling and software architecture
  • Deep knowledge of math, probability, statistics and algorithms
  • Ability to write robust code in Python or R, and one or more of Java, C#, C++, or Go
  • Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
  • Familiarity engineering products with big data infrastructure platforms like Hadoop and Spark
  • Good communication skills
  • Ability to work in a team
  • Outstanding analytical and problem-solving skills
  • Understanding of the challenges surrounding applying machine learning to rich media, including automatic speech recognition (ASR), natural language processing (NLP), object detection and image recognition