Lead Machine Learning Engineer

3 weeks ago


Canada Squint Cognition Full time

We are looking for a seasoned Machine Learning Engineer to lead the design, development, and deployment of scalable AI applications. In this role, you will move beyond simple model training to architecting full‑cycle ML solutions that power the backbone of our Squint Vision Studio product line. This position requires expertise and hands‑on experience designing, training, and debugging computer vision models using, amongst other things, state‑of‑the‑art explainable AI algorithms. As a senior member of the team, you will not only write production‑grade code but also mentor junior engineers, make high‑level architectural decisions, and define best practices for the product line. Key Responsibilities End‑to‑End ML Lifecycle: Own the full stack of ML development, from data ingestion and feature engineering to model training, deployment, and monitoring in production. System Architecture: Design and implement features from our cutting‑edge computer vision product roadmap, with the discipline and foresight required to maintain a scalable future‑proof architecture. Algorithmic Innovation: Research and adapt state‑of‑the‑art (SOTA) algorithms in machine learning and explainable AI. Code Quality & Leadership: Champion engineering excellence through code reviews, unit testing, and writing technical documentation. Mentor junior data scientists on writing production‑ready code. Cross‑Functional Collaboration: Partner with Product Managers and Backend Engineers to integrate AI capabilities seamlessly into the core product. Required Qualifications Experience: 5+ years of professional experience building customer‑facing machine learning applications with a focus on computer vision. Education: BSc in Computer Science, Mathematics, Statistics, or equivalent practical experience. Strong Coding Skills: Proficiency in Python (pandas, NumPy, SciKit) and familiarity with a compiled language like C, C++ . ML Frameworks: Deep understanding of modern frameworks such as PyTorch and TensorFlow. Production Engineering: Experience deploying models using Docker or Kubernetes. Data Infrastructure: Proficiency with SQL and NoSQL databases. Seniority level Mid‑Senior level Employment type Full‑time Job function Engineering and Information Technology Industries Technology, Information and Internet #J-18808-Ljbffr



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