ML Detection Team Leader

We know the title says - "About Us". But really, it's all about you.

At Evinced, we believe the way to make a true difference starts with our team, people passionate about solving complex technical challenges and creating a positive impact.

Forget about the buzzwords, want to work on products that break barriers and build an inclusive digital space?

At Evinced, you'll be developing disruptive new ways for developers to build accessible web and mobile apps that work for everyone, by developing AI-enabled tools and frameworks that find, cluster, and track accessibility problems automatically, saving developers time and reducing risk.

Ready to make your impact on different products, the roadmap, and most importantly - on billions of people worldwide who experience disabilities?

It's more than a job, it's a mission.

Your Team:
The ML Detection team is a new, cross-functional team at the core of our product strategy. Its mission is to own and advance the machine learning-based validations that form the foundation of our accessibility testing solutions. This includes everything from data collection and research to deploying, monitoring, and maintaining models in production. You will lead a multidisciplinary team of Data Scientists, ML/Data Engineers, and Algorithm Developers to build foundational models and standalone validations that extend the reach and precision of our products, giving Evinced a critical competitive edge.

Your Impact:

• Lead, mentor, and grow a multidisciplinary team of Data Scientists, ML Engineers, and Algorithm Developers, fostering a culture of trust, collaboration, and professional growth.
• Own the end-to-end delivery of ML-based detection capabilities, from initial research and data collection to model deployment, monitoring, and algorithmic post-processing.
• Provide hands-on leadership, actively contributing to technical solutions, architecture, and design reviews for new algorithms and models.
• Collaborate closely with Product, DevOps, and R&D teams to align on priorities, manage integrations, and ensure efficient, impactful development cycles.
• Architect and oversee the development of the infrastructure required for the entire ML lifecycle, including data management, experimentation platforms, and model deployment/monitoring systems.
• Define and refine team processes, tools, and methodologies to optimize performance, quality, and stability, ensuring data science tasks have clear definitions of done.
• Drive the strategic vision for the evolution of our detection capabilities, including UI patterns, interactive elements and accessibility issues, to meet current and future needs.
• Act as the primary problem solver and technical resource for the team, guiding them through complex research and engineering challenges.

Requirements:

Your Qualities:

• 8+ years of experience in software development or data science, with a focus on delivering ML/AI-powered features or products.
• 4+ years of experience leading, mentoring, and managing development teams, preferably multidisciplinary teams that include Data Scientists and ML Engineers.
• Strong hands-on technical expertise in the ML project lifecycle, including data engineering, research, and production workflows.
• Experience with modern cloud environments (GCP/Azure/AWS) and MLOps practices (e.g., model monitoring, managed training pipelines, feature stores).
• Familiarity with Python and common data science/ML frameworks.
• Proven ability to architect and design scalable, reliable ML systems and the infrastructure to support them.
• Experience collaborating closely with product managers and other engineering teams in a fast-paced environment.
• Excellent problem-solving skills with a proactive, "can-do" attitude and the ability to navigate ambiguity.
• Bachelor's degree in Computer Science, Engineering, a related field, or equivalent experience.

We're based in Tel Aviv, 5 minutes from Hashalom train station, and Yehudit light train station, and we have our own parking lot. We work in a hybrid mode, with flexible working hours.