AI及云端训练前瞻科学家_XC at Bosch Group

Suzhou, Jiangsu, China -

Full Time

Start Date

Immediate

Expiry Date

03 Jun, 26

Salary

0.0

Posted On

05 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Cloud Native, Kubernetes, Docker, Microservices, AWS, Azure, AliCloud, Data Lakes, Vector Databases, RAG, Data Pipelines, Kafka, Airflow, MLOps, Kubeflow, MLflow

Industry

Software Development

Description

Job Description AI Strategy: Scouting and incubating next-gen algorithms (LLMs, E2E Driving). 2. Cloud Architecture: Defining the data infrastructure that powers the "Data Closed-Loop"—from vehicle data ingestion and auto-labeling pipelines to cloud-based simulation and model training. Key Responsibilities: • Strategic Scouting (AI & Cloud Trends): o Monitor global trends in Cloud-Native Automotive Architectures, focusing on Hybrid Cloud solutions (Edge-to-Cloud synergy) and Sovereign Cloud compliance in China. o Scout emerging technologies in Cloud Simulation (e.g., World Models) and Data Management (Vector Databases for RAG, Synthetic Data Generation). o Build the "AI & Cloud Tech Landscape Map," identifying opportunities to optimize computing costs while maximizing intelligence. • Cloud & Data Architecture Design (The Backbone): o Data Closed-Loop: Design the high-level architecture for automated data pipelines: Ingestion → Cleaning → Auto-Labeling (via Foundation Models) → Training → OTA. o Hybrid Compute Strategy: Define what workloads run on the car (Edge NPU) vs. what workloads offload to the cloud (e.g., Shadow Mode data filtering logic vs. heavy model training). o Simulation Infrastructure: Lead the technical definition for cloud-based large-scale simulation platforms needed for validating L3/L4 algorithms (ISO 8800 compliance). • Deep-Dive Analysis (Feasibility & FinOps): o Author "Deep-Dive Reports" on Cloud ROI & FinOps: Analyze the cost implications of scaling large model training and storage. Provide "Make vs. Buy" recommendations for cloud services (e.g., AWS vs. Azure vs. Private Cloud). o Evaluate the feasibility of Vehicle-Cloud Collaborative Computing (e.g., RCP - Remote Control Protocol latency analysis). • Incubation Leadership: o Guide the Innovation Squad to build Cloud-Native PoCs (e.g., a RAG-based Knowledge Base hosted on cloud with vehicle connectivity, or a Shadow Mode data trigger mechanism). o Ensure all cloud PoCs adhere to Cybersecurity and Data Privacy regulations. Qualifications: Qualifications Education: Master’s or PhD in Computer Science, Cloud Computing, AI, or related fields. Experience: o 8+ years in Software Engineering, with at least 3+ years focused on Cloud Architecture or Data Infrastructure. o Proven experience in the Automotive, IoT, or Tech industry handling large-scale device data. • Technical Depth (Cloud & AI Hybrid): o Cloud Native: Expert knowledge of Kubernetes (K8s), Docker, and Microservices architecture. Familiarity with major public clouds (AWS/Azure/AliCloud). o Data Infrastructure: Experience with Data Lakes (e.g., Snowflake, Databricks), Vector Databases (e.g., Milvus, Pinecone for LLMs), and Data Pipelines (Kafka, Airflow). o AI Integration: Understanding of MLOps pipelines (e.g., Kubeflow, MLflow) and how to serve models in the cloud. • Business Acumen: Ability to calculate Cloud TCO (Total Cost of Ownership) and optimize resource usage (Spot instances, GPU utilization). • Language: Fluent in English and Mandarin. Legal Entity: Bosch Automotive Products (Suzhou) Co., Ltd.

Responsibilities

The role involves scouting next-generation AI algorithms, such as LLMs and E2E Driving technologies, and defining the cloud architecture for the 'Data Closed-Loop' system, covering data ingestion, auto-labeling, simulation, and model training.