Angible is a Taipei-based AI startup founded in 2025, dedicated to accelerating AI adoption in the retail industry through computer vision and edge computing. In our first year, we successfully deployed in-house AI solutions across multiple retail environments spanning Europe, North America, and Southeast Asia, proving our ability to solve real business pain points at scale. The company is now in a critical phase of rapid business expansion and continuous product refinement, focused on building intelligent solutions that improve retail operational efficiency, reduce shrinkage, and enhance the customer experience.

Our team brings deep expertise across AI, edge computing, hardware design, and retail operations, working in a highly collaborative and fast-iterating environment. Driven by the mission to make retail smarter and more efficient, Angible leverages AI and edge computing to help businesses boost profitability and maintain a competitive edge in a rapidly evolving market — making AI a seamless part of everyday operations.

At Angible, we value transparency, clarity of goals, and mutual trust, and we place great importance on each team member's impact and growth. You will have the opportunity to participate directly in building products from 0 to 1 and scaling them beyond, working alongside a team that is both visionary and action-oriented to rapidly validate hypotheses and bring technology to life in real-world scenarios.

You're not just someone who trains models. You'll be involved in defining problems, breaking down scenarios, and designing solutions — so that AI doesn't just produce outputs, but actually makes valuable decisions in real-world retail environments. We're looking for a Senior ML Engineer who can turn model capabilities into real business solutions — not just building models, but using model results to answer critical questions.

You will work with the ML team to advance detection / tracking / re-identification model training and evaluation, collaborate with backend and platform teams to integrate model outputs with internal evaluation tools, and partner with product and field teams to align technical outcomes with actual business scenarios.

• ML: PyTorch, ONNX, OpenVINO
• CV Tasks: Detection, Tracking, Re-Identification
• Retrieval: FAISS
• Experiment / Data Management: ClearML / MLflow / DVC
• Infrastructure: Docker, GitHub Actions, Linux

• Drive continuous evolution of core AI capabilities: Improve overall capabilities through model training, optimization, and validation to support product performance in real-world environments
• Ensure stable deployment of technical outcomes: Help transition models from development to real-world applications, ensuring solutions are stable and usable
• Establish reliable evaluation and iteration methods: Enable consistent and effective comparison of different model and solution versions, supporting the team in making better technical decisions
• Strengthen the system's ability to make critical judgments: Improve the quality of decisions the system makes after integrating multiple signals, enabling the product to better answer core questions in real business scenarios
• Take ownership of key development and validation work: Drive the iteration of models, experiments, and related tools to help the team keep moving forward
• Make decisions driven by data and experimental results: Adjust model direction based on benchmarks, error analysis, and deployment validation results, balancing accuracy with deployability

• Python as primary language, with good software engineering habits (readability, maintainability, basic testing practices)
• Deep learning / computer vision hands-on experience: Proficient with PyTorch, with practical experience in at least one or two of detection, tracking, and re-identification; understanding of common CV evaluation metrics
• Model export and validation skills: Experience with ONNX export, understanding of potential accuracy or behavioral differences when converting models from PyTorch to ONNX / OpenVINO
• Basic troubleshooting ability: Able to identify common issues in model training, inference results, data quality, or evaluation pipelines
• Willingness to handle model-adjacent engineering work: Not averse to maintaining benchmarks, evaluation pipelines, internal tools, or data pipelines
• Proficient in using AI tools to accelerate development: Able to leverage AI coding tools to boost efficiency while taking responsibility for output quality

• Experience with VLM (Vision-Language Model) development or applications, or experience with temporal / video-based visual detection (e.g., multi-frame / multi-camera signal integration)
• Experience with OpenVINO, model quantization (FP16/INT8), or benchmark / evaluation pipeline maintenance
• Familiarity with retail / manufacturing / smart venue computer vision application scenarios, or experience building shared AI coding development environments for teams

1. Resume Screening
2. Online Interview
• Technical Initial Discussion (1 hour): Align on motivation, discuss past ML product deployment experience (architecture, trade-offs, outcomes)
3. On-site Interview
• Technical Deep Dive (1.5–2 hours): In-depth discussion of hands-on exercises, system design, and scenario-based questions
4. Final Interview
• Culture & Collaboration Interview (1 hour): Understanding collaboration style, communication approach, and conflict resolution
• CEO Interview (1 hour): Assessing personality traits and team culture fit
5. Offer & Onboarding

‍The entire process is expected to be completed within two weeks.