AI Engineer

Deque is seeking an AI Engineer to join our Scan & Results Team. This position will play a key role in applying state-of-the-art machine learning and AI techniques to enhance automated accessibility testing, drive intelligent analysis of scan results, and develop algorithms that add value to our product suite. You will work to build scalable ML systems that integrate into our core products, and ensure they are robust, ethical, and aligned with Deque’s mission of digital equality.

The ideal candidate would have experience deploying ML models in production environments, preferably in the domains of computer vision, and NLP. An understanding of supervised and unsupervised learning, model evaluation, data science, and responsible AI principles is essential. Prior work in accessibility or web technologies is a plus.

• Process and collect data for AI systems, create and maintain data pipelines for developing and deploying AI algorithms at scale
• Research the state of the art in AI/ML and creatively apply it to our mission of digital equality
• Design and develop algorithms using AI/ML to enhance our product suite and increase automation
• Contribute to technical documentation and share findings with stakeholders through demos, presentations, and reports
• Evaluate algorithm performance using rigorous statistical and domain-relevant metrics, iterating for continual improvement
• Participate in code reviews, team planning, and cross-functional collaborations to ensure high engineering standards

• 3+ years of experience in machine learning or AI engineering, with a strong focus on deploying models in production environments.
• Proficiency with Python and common ML libraries such as TensorFlow, PyTorch, Scikit-learn, or Hugging Face Transformers.
• Experience building and maintaining scalable ML infrastructure
• Strong back-end engineering skills, including designing APIs, integrating with cloud services, and building infrastructure to support ML workflows.
• Hands-on experience with data engineering tools and workflows (e.g., AWS Glue, Athena, Spark, or similar).
• Strong understanding of model lifecycle management, including training, evaluation, deployment, and monitoring in real-world systems.
• Familiarity with web technologies (HTML, CSS, JavaScript) and an interest in digital accessibility.
• Excellent communication skills, with the ability to explain complex ML concepts to both technical and non-technical audiences.

• Computer Vision, NLP
• Docker & AWS
• Accessibility knowledge