Machine Learning QA Engineer

Description

Derq is an MIT spinoff building AI-powered traffic safety and smart infrastructure. We’re a team of passionate innovators, leveraging the latest in AI and technology to transform the future of mobility. Our platform enhances road safety and traffic management by turning real-time data into actionable insights for cities and road operators. Our patented technology collects and analyzes data from connected sensors like cameras, radar, and traffic signal controllers to help predict and prevent road incidents. We deploy edge and cloud solutions that make intersections and highways safer and smarter.

Role Overview As a ML Quality Assurance Engineer at Derq, you will play a critical role in ensuring the reliability, accuracy, and performance of our computer vision and machine learning based products. You will work closely with our development team to design and execute comprehensive test strategies, identify and report defects, and help improve the overall quality of our customer facing AI products, using data analytics.

Key Responsibilities Collaborate with AI development and product teams to understand product requirements and design effective test plans and test cases for AI based products

Strategize evaluation methodologies, gathering data and evaluation reporting

Perform data analytics on large datasets

Monitor and track anomalies within product’s content and statistics

Train and deploy ML models to help boost product performance

Create and maintain detailed documentation of test processes, methodologies, and findings

Keep up to date with advancements in the field to optimize internal processes and workflows

Interface and coordinate with Engineering team

Foster a collaborative, proactive team environment that values shared success.

Requirements

Bachelor’s degree in an analytical domain such as Machine Learning, Computer Science, or a related discipline

Python, Javascript and SQL Knowledge/Experience

Familiarity with classical Machine Learning Models such as Random Forest, SVM, Naive Bayes

Core understanding of mathematical concepts for statistical inference

Hands on with spreadsheet based querying and analytics

Proficient with annotation tools such as CVAT

Excellent knowledge of word processing tools and spreadsheets (MS Office Word, Excel etc.)

Good command of English both oral and written and reporting skills

Knowledge of basic statistical principles coupled with logical thinking, coherent reasoning and a detail-oriented attitude