Job Summary:

Cerity is a spinoff that is building a new technology innovation center in Austin. The innovation team is a group of thought leaders who are designing, building and implementing new and creative SaaS solutions.

As a Machine Learning Engineer, you will be responsible for developing solutions and deploying production code to address the current business needs. You will also be tasked with research and development activities that are forward thinking and somewhat unconventional.

Your responsibilities will range from building the systems/platforms needed to host the Machine Learning/Deep Learning/Reinforcement Learning algorithms to production hardening the algorithms created by our Data Scientists.

To do this job successfully, you will have needed prior work experience deploying ML/DL/RL algorithms into a production environment.

Responsibilities

• Transform Data Science prototypes to production-grade solutions at scale
• You will be expected to lead from the front in following best practices in development and CI/CD methods.
• You will also be expected to be a strong independent contributor that is not afraid to implement/deploy the solutions (even if that means making mistakes). At Cerity, we believe that the only way to really learn something is to stumble a few times.
• Design, code and deploy machine learning systems capable of processing large volumes of data
• Research and implement appropriate ML algorithms and tools
• Develop machine learning applications according to requirements
• Establish reusable data processing pipelines to enable rapid re-training and iteration through different ML methodologies
• Select appropriate datasets and data representation methods
• Design experiments and analysis methodologies that are statistically rigorous
• Run machine learning tests and experiments
• Perform statistical analysis and fine-tuning using test results
• Train and retrain systems when necessary
• Extend existing and Cerity Proprietary ML libraries and frameworks
• Keep abreast of developments in the field

Requirements

• Proven experience as a Machine Learning Engineer. Must have held a Machine Learning Engineer Title in a previous role.
• Understanding of data structures, data modeling and software architecture
• Experience deploying ML and DL algorithms for regression and classification use-cases
• Deep knowledge of math, probability, statistics and algorithms
• Ability to write robust code in Python, Java and R
• Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
• Experience setting up systems/frameworks (e.g., Docker and Kubernetes) within AWS
• Excellent communication skills
• Ability to work in a team
• Outstanding analytical and problem-solving skills
• BSc in Computer Science, Mathematics or similar quantitative field; Master’s degree is a plus
• Experience with Falcon is a bonus!