Senior Machine Learning Engineer

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. We're hiring a Senior ML Engineer to own model development end-to-end and turn business requirements into production systems. What you'll work on Drive iteration across our AI/ML stack — object detection and tracking, person/vehicle Re-ID, and video understanding running on edge. Build efficient algorithms for resource-constrained hardware — implement lightweight architectures and optimization techniques within latency and compute budgets. Turn business asks into algorithmic problems — design the metrics, run experiments, and drive each iteration with ablation studies and error analysis. Partner with product engineers to ship and monitor ML systems in production — deployment paths and feedback loops that catch data drift and failure modes. Improve data and labeling quality — sampling strategy, annotation guidelines, and tooling that keeps a long-lived dataset healthy. You're a strong fit if you have 5+ years building production ML systems, with deep hands-on experience in computer vision, especially in object detection and multi-object tracking. Strong ML/DL fundamentals — statistics, classical methods, and modern deep learning, with a clear grasp of model internals, training dynamics, and common failure modes. Python and DL framework experience — PyTorch or TensorFlow, including custom training loops, distributed training, and end-to-end model debugging. A track record of driving research independently — picking the metric, designing the experiment plan, and interpreting noisy results honestly. Production ML sensibility — comfortable with inference engine (ONNX, OpenVINO, TensorRT), performance profiling, and porting models to real hardware. Bonus points MLOps experience — experiment tracking, model and data versioning, reproducible training workflows (MLflow, DVC, or similar). ML pipeline / workflow orchestration — Dagster or similar tooling for training, evaluation, and deployment pipelines. LLM, RAG, or agentic AI experience — fine-tuning (LoRA/PEFT), retrieval pipelines (vector stores, rerankers), agent frameworks (LangChain or similar), or vision-language models. Our engineering culture 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. ============================ Interview Process Online (Google Meet) Engineer / Team Lead Interview (0.5 - 1 hr) Onsite Technical Interview (2.5 hrs) CEO & VP Interview (1.5 hrs) Peer Interview (0.5 hr) HR Interview (0.5 hr)