默克药厂
Internship - Wearable Software Engineering (m/f/d)
Internship - Wearable Software Engineering (m/f/d)
发布于 4 天前实习/见习
Eysins, Vaud, Switzerland
无经验要求
实习生
仅现场办公
学历未注明
实习与临时职位
WatchOS
ONNX
Wear OS
TensorFlow Lite
Edge Ai
Core Ml
AI 估算 · 3k–4k
Internship in Switzerland; typical monthly stipend for engineering interns ranges CHF 3000-4000.
职位详情
关于这个职位
This internship focuses on porting AI models to smartwatches (Wear OS and watchOS) and evaluating their performance and battery impact. You will work with provided models, conduct experiments, and deliver practical guidelines for edge deployment.
最低要求
Must-Haves:
You are currently studying Software Engineering, Computer Science, Electrical/Computer Engineering, or a related field.
Basic familiarity with mobile development (Android/Wear OS or iOS/watchOS).
Comfortable writing clean, testable code and documenting findings clearly.
Strong analytical mindset and attention to detail; able to compare configurations and interpret trade-offs.
Interest in edge AI and on-device inference.
工作职责
Port existing server-side AI models to Wear OS and watchOS.
Design and run experiments to measure prediction performance and battery impact under different usage patterns.
Deliver practical guidelines for correctly porting models to edge devices.
优先资格
Nice-to-haves:
Experience with on-device inference frameworks or model conversion (e.g., TensorFlow Lite, Core ML, ONNX).
Understanding of optimization techniques (e.g., quantization).
Exposure to sensor data pipelines or streaming workloads on mobile/wearables.
Prior projects that involve benchmarking, parameter sweeps, or porting models to constrained devices.
AI 洞察
优缺点分析
优点
- Work with cutting-edge edge AI technology on wearables.
- Gain experience with model conversion and optimization.
- Internship at a global healthcare and science company (Merck).
- Hands-on experimentation and clear deliverables.
- Limited duration (3-6 months) may require quick ramp-up.
- Working with constrained devices and battery trade-offs can be tricky.
- No new model development
- focus on porting and measurement.
- Engineering students interested in edge AI, mobile development, and hands-on experimentation with wearables.
缺点 / 挑战
暂无明显挑战项
角色解读
- Gain hands-on experience in edge AI and wearable technology.
- Develop skills in model optimization and deployment on constrained devices.
- Potential to pursue a career in embedded AI, mobile engineering, or IoT.
- Port existing server-side AI models to smartwatches (Wear OS and watchOS).
- Design and run experiments to measure prediction performance and battery impact.
- Analyze trade-offs and deliver practical guidelines for edge deployment.
- Basic mobile development (Android/Wear OS or iOS/watchOS).
- Familiarity with on-device inference frameworks (TFLite, Core ML, ONNX).
- Analytical mindset for benchmarking and interpreting results.
- Clean coding and documentation skills.
申请策略
- Customize your CV to emphasize edge AI and mobile experience.
- Demonstrate curiosity about wearable technology and its challenges.
- Highlight any mobile development projects (Android/Wear OS or iOS/watchOS).
- Show experience with TensorFlow Lite, Core ML, or ONNX conversion.
- Include benchmarking or parameter sweep projects.
- Emphasize analytical skills and attention to detail.
- Familiarize yourself with Wear OS and watchOS development environments.
- Practice converting models using TFLite Converter or Core ML Tools.
面试指南
- Use STAR method: Situation, Task, Action, Result for experience-based questions.
- For technical how-to questions, outline clear steps and trade-offs.
- Show analytical thinking by comparing alternatives and justifying choices.
- What experience do you have with mobile development on wearables?
- How would you measure battery impact of an AI model on a smartwatch?
- Explain the steps to convert a TensorFlow model to TensorFlow Lite.
- Describe a project where you had to optimize performance on a constrained device.
- How do you ensure reproducibility in benchmarking experiments?
匹配度报告
59
综合匹配度
Short-term internship with strong learning potential in edge AI, modest compensation, and on-site work.
适合人群
Students motivated by skill development in cutting-edge edge AI and wearable technology.
最强匹配
成长发展匹配
最弱匹配
薪资福利匹配
薪资福利40
成长发展85
工作生活60
使命价值50
薪资福利匹配
40较低
Internship stipend is modest and typical for the region; no full-time benefits. Compensation is not a core strength.
薪资信号未披露（AI估算：3K-4K/月）
成长发展匹配
85较高
Strong development opportunity: edge AI, model optimization, and wearable tech. Skills gained are highly relevant for future careers.
技术前沿前沿/新兴技术
技术栈Wear OS、watchOS、TensorFlow Lite、Core ML、ONNX、Edge AI
成长机会personal development、career advancement
业务类型ambiguous
工作生活匹配
60中等
On-site work in Switzerland; typical office hours. No mention of flexibility or remote work.
工作模式仅现场办公
办公地点市区核心地段
加班情况未提及（无法判断）
使命价值匹配
50较低
Work in healthcare technology can be meaningful, but the role is technical and focused on feasibility studies; direct social impact is indirect.
行业发展稳定成熟行业
社会影响中性/一般
使命信号enrich people's lives、help patients
创新程度积极采用新技术
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