QA Engineering Lead

Fusemachines•New York, NY

1d•Hybrid

About The Position

About Fusemachines Fusemachines is a 10+ year old AI company, dedicated to delivering state-of-the-art AI products and solutions to a diverse range of industries. Founded by Sameer Maskey, Ph.D., an Adjunct Associate Professor at Columbia University, our company is on a steadfast mission to democratize AI and harness the power of global AI talent from underserved communities. With a robust presence in four countries and a dedicated team of over 400 full-time employees, we are committed to fostering AI transformation journeys for businesses worldwide. At Fusemachines, we not only bridge the gap between AI advancement and its global impact but also strive to deliver the most advanced technology solutions to the world. Position Overview We are seeking an experienced QA Engineering Lead with a background in testing machine learning (ML) web applications to join our team. Bonus: If you have worked with LLM's. The ideal candidate will lead the quality assurance efforts with a very hands-on attitude for our ML-driven projects, ensuring the highest standards of quality and reliability are met. The QA Engineering Lead will collaborate closely with cross-functional teams, mentor junior QA engineers, and drive the adoption of best practices in ML Application testing methodologies. Hybrid. Must be able to meet in Hicksvile, Long Island twice a week

Requirements

Bachelor's degree in Computer Science, Engineering, or related field; or equivalent work experience.
5 years of experience in quality assurance or software testing, with a some experience working on machine learning applications.
Familiarity with basic machine learning concepts
Familiarity in programming languages commonly used in ML development (e.g., Python, R, etc.).
Experience in designing and implementing effective test strategies for ML models including LLM's
Excellent analytical and problem-solving skills, with a keen attention to detail.
Strong communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams.
Hands-on experience with automated testing tools and frameworks (e.g., Selenium, JUnit, TestNG, etc.).

Nice To Haves

Experience with Agile development methodologies is preferred.
Familiarity with LLM evaluation frameworks

Responsibilities

Lead the QA team in developing and implementing comprehensive test plans, test cases, and strategies specific to machine learning applications.
Drive the adoption of automated testing tools and frameworks to increase testing efficiency and coverage.
Create strategies and tests for new product features, in addition to executing regression testing of existing features.
Work with development teams to develop test plans, test suites, and test cases to ensure that the software stack is thoroughly verified and validated through manual and automated methods.
Submit, track, and verify defects through the software development lifecycle, ensuring timely resolution.
Write documents, tests, scripts, and tools to help facilitate testing and improve efficiency.
Define and establish QA metrics, measuring the effectiveness of testing processes and identifying areas for improvement.
Collaborate with product owners, machine learning engineers, and software engineers to ensure alignment on quality objectives and priorities.
Design and execute testing for ML models, including functional, performance, and scalability testing.
Mentor and provide guidance to junior QA engineers on testing methodologies and best practices.
Stay current with industry trends and advancements in ML testing techniques and tools.
Participate in release planning and deployment activities for ML-driven features, ensuring smooth integration and deployment.
Investigate and troubleshoot issues related to ML model performance or behavior, working closely with data scientists and engineers to resolve them.
Conduct functional, regression, performance, and security testing as needed.
Drive the adoption of automated testing tools and frameworks to increase testing efficiency and coverage.
Define and establish QA metrics to measure the effectiveness of testing processes and identify areas for improvement.
Participate in release planning and deployment activities, ensuring a smooth and successful release process.
Investigate and troubleshoot issues reported by customers or internal stakeholders, identifying root causes and driving resolution.