Senior Machine Learning Engineer

Department: Engineering

Location: Taipei - Neihu

Employment Type: FullTime

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.

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Interview Process

1. Online (Google Meet)

   1. Engineer / Team Lead Interview (0.5 - 1 hr)

2. Onsite

   1. Technical Interview (2.5 hrs)

   2. CEO & VP Interview (1.5 hrs)

   3. Peer Interview (0.5 hr)

   4. HR Interview (0.5 hr)