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.
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.
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.
Camera geometry and calibration — pinhole camera models, intrinsic and extrinsic parameters.
Edge AI and on-device inference — TensorRT, ONNX, OpenVINO, NVIDIA Jetson, DeepStream — plus quantization, pruning, distillation, or other inference optimization techniques.
MLOps experience — dataset and model versioning, experiment tracking, automated evaluation, model monitoring, and progressive rollout.
CV in uncontrolled physical environments — autonomous driving, robotics, security, industrial inspection, or smart retail.
Multimodal models, VLMs, or LLM-based workflows used in real production systems.
Startup or fast-scaling product org experience, or working with international teams and enterprise customers.
Small team, high ownership, fast feedback from customers — and the operational rigor to make that velocity sustainable. Modern AI tooling — LLMs, coding agents, agent-driven workflows — is a normal part of how we work, and you're encouraged to push on what these tools can do.
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Online (Google Meet)
VP of Engineering Interview (1 hr)
Take-Home Assignmen
Submission deadline: within 7 days
Estimated time required: 8 hours
Note: Deadlines can be extended upon request to accommodate current workloads or personal commitments.
Onsite
Technical Interview (2.5 hrs)
CEO & VP Interview (1.5 hrs)
HR Interview (0.5 hr)
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Berry AI is a company focused on developing AI solutions.