Machine Learning Team Lead

Berry AI builds AI-powered operations platforms for QSR restaurants — drive-thru analytics, loss prevention, and store management tooling deployed at thousands of locations across the US — and growing. Computer vision sits at the core of what we ship: models running on in-store edge devices, reading video in real restaurant conditions to understand vehicles, people, queues, and operational events. We're hiring a Machine Learning Team Lead to own that technical direction and lead the ML/CV team behind it.

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What you'll work on

■ Lead and grow the ML/CV team — set technical direction, own execution quality and delivery, mentor engineers, and own hiring — while staying hands-on.

■ Own the CV capabilities behind the product — object detection, multi-object tracking, person/vehicle Re-ID, camera calibration and geometric vision, and event recognition.

■ Work with Product to turn customer problems into well-defined ML problems — the success metric and a roadmap the team can actually execute.

■ Drive the full ML lifecycle — data collection, annotation strategy, training, evaluation, deployment, monitoring, and the iteration loop that keeps accuracy climbing after launch.

■ Push performance under real-world conditions — occlusion, low light, lens distortion, camera movement, viewpoint variation, and domain shift across thousands of stores.

■ Guide model optimization for constrained edge hardware — balancing accuracy against latency, throughput, memory, and operational reliability.

■ Build reusable pipelines and tooling that take manual effort out of data prep, store configuration, camera calibration, validation, and rollout.

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You're a strong fit if you have

■ 7+ years in machine learning, deep learning, or computer vision, with a track record of shipping production CV systems — not just research prototypes.

■ Experience leading an ML team, serving as technical lead, or owning the direction and delivery of a major ML product.

■ Real depth in one or more of object detection, multi-object tracking, person/vehicle Re-ID, segmentation, or video understanding.

■ A strong classical CV and image-processing foundation — feature extraction and matching, geometric transformations, interpolation, and image warping.

■ The judgment to evaluate CV systems under real-world conditions — occlusion, truncated objects, low light, motion blur, camera variation, domain shift — and to tell which failure modes actually matter to customers.

■ End-to-end ML product lifecycle experience, from dataset development and training through production deployment and monitoring.

■ Python and PyTorch or TensorFlow, with a solid grasp of model evaluation, error analysis, dataset quality, and experiment design.

■ The ability to turn ambiguous product needs into measurable objectives and executable plans, work across functions, and stay technically hands-on while leading the team.