Flock Safety - Senior ML Engineer, Enrichment

• How you'll make an impact: • Frame open-ended, real-world problems into well defined ML problems • Make use of and improve on existing data acquisition and model training/evaluation pipelines to create appropriate datasets and obtain model feedback • Leverage cutting-edge research and technology to create custom solutions • Design and run experiments to test new ideas or improvements to existing models • Build visualization and monitoring tools to evaluate the quality of our data and models • Collaborate across teams and product to deliver solutions that fit within business and organizational requirements • Review code of other Machine Learning Engineers • BS/MS in Computer Science, Mathematics, Physics, Engineering, or proof of equivalent software engineering experience (PhD’s welcome) • Experience solving problems using Machine Learning frameworks (Tensorflow, PyTorch, scikit-learn, etc.) • Good understanding of Deep Learning and Traditional ML (supervised and unsupervised) algorithms • Experience writing Python in a team environment • Able to take on complex problems, learn quickly, iterate, and persist towards a good solution • Effectively communicate, at the level of your audience, and seek to understand and be understood • Basic SQL knowledge • Basic Git knowledge • Experience with linear algebra, probability, and statistics preferred • Feeling uneasy that you haven’t ticked every box? That’s okay; we’ve felt that way too. Studies have shown women and minorities are less likely to apply unless they meet all qualifications. We encourage you to break the status quo and apply to roles that would make you excited to come to work every day. • 90 Days at Flock • We are a results-oriented culture and believe job descriptions are a thing of the past. We prescribe 90 day plans and believe that good days lead to good weeks, which lead to good months. This serves as a preview of the 90 day plan you will receive if you were to be hired in this role at Flock Safety. • The First 30 Days • Learn our tech stack, internal toolkit, and standard operating procedures for training, evaluating, and deploying our machine learning models • Get to know the technical teams and stakeholder groups you’ll be working closely with • Begin making a meaningful impact by adding an incremental improvement to our model evaluation reports • The First 60 Days • Curate our holdout data to improve representation and address certain challenging strata • Improve statistical reporting metrics in our evaluation reports • Get acquainted with downstream repositories that support our predictive capabilities. • 90 Days & Beyond • Improve understanding of the impact of data augmentations in our training pipeline. • Introduce new augmentations within our modeling framework • Gauge model robustness by building holdout datasets for various adversarial conditions • Become fully independent within our end-to-end training and deployment pipeline