6 open roles

Senior Machine Learning Engineer

Taipei - NeihuFull-timePosted Jul 31, 2026

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 — Py. Torch or Tensor. Flow, 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 (Lang. Chain 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