Job Description

Role Description: We're seeking an experienced ML Engineer with a strong specialization in computer vision. You're skilled at every stage of the ML lifecycle—from defining problems and exploring data, to developing, deploying, and iterating models. You're adept at balancing rigorous modeling with practical software engineering, seamlessly integrating ML solutions into production systems. Complex visual data challenges excite you, and you are driven by tangible impacts, owning projects from initial prototypes to production deployment.

Responsibilities:

• Design, develop, and deploy computer vision models (e.g., semantic segmentation, point-of-gaze tracking, keypoint detection).
• Select optimal architectures and training strategies, leveraging semi-supervised and few-shot learning techniques when necessary.
• Develop robust, production-grade ML services integrated into critical workflows.
• Monitor and optimize model performance in production, establishing real-world evaluation frameworks.
• Conduct exploratory data analyses, driving model selection and feature engineering.
• Collaborate closely with platform and infrastructure teams to optimize ML pipelines.
• Translate ambiguous business requirements into actionable technical roadmaps.
• Stay current on emerging computer vision research and technologies, implementing them effectively.

Requirements

Qualifications:

• 3+ years of applied ML experience with computer vision models deployed in production.
• Deep expertise in computer vision, particularly with video or camera-based systems.
• Strong statistical and deep learning modeling skills.
• Expert proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX).
• Experience translating research into scalable ML solutions.
• Familiarity with large-scale data systems and MLOps practices.
• Excellent communication skills, comfortable bridging technical and business stakeholders.

Extra Points:

• Traditional computer vision techniques (feature detection, optical flow, stereo vision).
• Signal processing fundamentals and related experience.
• Ownership and maintenance of computer vision model pipelines in production.
• Experience in healthcare, regulated industries, or SaMD.
• Expertise in probabilistic modeling or Bayesian methods.
• Complementary experience with edge computing, embedded systems, or real-time inference optimization.

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