Senior Machine Learning Engineer
- 4+ years of shown hands-on experience with design, implementation, and application of ML/AI/Deep Learning and OR solutions and techniques to build models that solve real problems.
- 2+ years of hands-on experience in optimization modeling, simulation, and analysis with Python or Matlab.
- Experience analyzing machine data (sensors, downtime log, machine states, etc) for IoT & predictive maintenance applications.
- Experience applying deep learning frameworks, such as PyTorch/ Torch, TensorFlow, Keras to real-world applications that solve problems.
- Knowledge of validated approaches for scale-ability, productionalizing models, and implementing machine learning applied to expansive and diverse datasets (storage GPUs, techniques for deep learning at scale).
- Strong software development skills with proficiency in Python.
- Experienced user of machine learning and statistical-analysis libraries, such as GraphLab Create, scikit-learn, scipy, and NLTK.
- High level of autonomy and influence to remove roadblocks and deliver results (evaluate and solve complex problems involving various teams ranging from data instrumentation to analytics tool development). A proven track record for self-study and self-exploration into new tools and techniques.
- Ability to explain and present analyses of machine learning concepts to a broad technical audience.
- Experience with image processing, Computer Vision, and using ML tools to identify patterns in images, specifically applied to industrial or manufacturing environments is a plus.
- Applied background in Hadoop, Spark, Hive, Cassandra, and knowledge of R is a plus.
- Experience in data analytics for manufacturing problems is a plus.
- Master’s or Ph.D. degree in Computer Science, Math, Statistics, Physics, Engineering, or related level of experience required.
- Collaborate with robotics and automation specialists, mechanical and quality engineers to apply machine learning to industrial problems and situations
- Seek opportunities in the production and development processes to utilize deep learning, algorithms, and other machine learning tools for improvements
- Implementation of machine learning (ML) and operations research (OR) tools, such as classical regression, classification, as well as neural networks and various optimization models for a wide range of prescriptive/predictive applications in dynamic production environments
- Develop a toolkit to guide the application of machine learning tools combined with statistical tools for common engineers
- Assemble large data sets for analysis either through direct SQL-based querying or development of scripts and code-modules to collate distributed and disparate data sources
- Analyze huge amounts of data-identifying anomalies (pattern detection) and variabilities in a measure of interest
- Develops software components in Python, R, and/or C/C++/ Objective-C towards the roll-out of a data automation system Qualifications
- Opportunity to work on bleeding-edge projects
- Work with a highly motivated and dedicated team
- Flexible schedule
- Medical insurance
- Benefits program
- Corporate social events
- Professional development opportunities