New opportunity

Principal Data Scientist-GenAI l LLMs l Computer Vision l Multimodal AI

HERE · Bengaluru, India

About this role

The Principal Data Scientist will be responsible for innovating and developing advanced machine learning and data analytics solutions to build industry-leading map technology. This role involves collaborating with developers and architects to define technical solutions and drive the development, deployment, and maintenance of applications across multiple environments. Responsibilities include defining and driving the technical direction for the next generation of process automation in HERE Core Map, leveraging highly scalable Big Data infrastructure and machine learning for global-scale digital mapping. The candidate will work on cutting-edge AI domains including Generative AI, LLMs, Computer Vision, and Multimodal AI.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AILarge Language ModelsComputer VisionMultimodal AIMachine LearningData ScienceBig DataProcess Automation

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Drive Process Automation in HERE Core Map
The primary objective is to define and lead the technical direction for automating map-making processes. This requires translating complex mapping requirements into scalable machine learning workflows that can operate on global-scale data.
Architect Scalable AI/ML Solutions
You must design and deploy advanced machine learning and analytics solutions across multiple environments. This involves leveraging Big Data infrastructure to ensure that Generative AI, LLMs, and Computer Vision models perform reliably at the scale required for industry-leading map technology.
Cross-Functional Technical Leadership
Success depends on collaborating effectively with developers and architects to define technical solutions. You will be expected to bridge the gap between high-level AI research and practical, maintainable software deployments.

Likely challenges to think through

Balancing Innovation with Global Scale
Implementing cutting-edge Generative AI and Multimodal models within a production-grade, global-scale mapping ecosystem presents a significant trade-off between model complexity and operational latency. Prepare to discuss strategies for optimizing model performance without sacrificing the accuracy required for map data.
Data Complexity and Infrastructure Integration
Integrating advanced AI into existing 'HERE Core Map' infrastructure likely involves navigating legacy data pipelines and massive datasets. Prepare to demonstrate how you handle data fragmentation and ensure that new AI-driven automation tools integrate seamlessly with established Big Data frameworks.

Practice your answers

Technical practice

Architecting for Global-Scale Mapping

What to coverExplain how you would design a machine learning pipeline capable of processing global-scale digital mapping data. Discuss your approach to handling data ingestion, distributed processing, and maintaining consistency across multiple environments.

Generative AI and LLM Integration

What to coverDescribe your experience integrating Large Language Models into existing data analytics workflows. Focus on how you ensure the reliability and accuracy of model outputs when applied to complex, domain-specific mapping tasks.

Computer Vision in Geospatial Contexts

What to coverDiscuss the challenges of applying Computer Vision to high-definition map data. Address how you handle variations in sensor data, environmental noise, and the need for high-precision feature extraction.

Multimodal AI Strategies

What to coverHow do you approach fusing disparate data sources—such as imagery, text, and sensor data—into a unified Multimodal AI model? Explain the trade-offs between early and late fusion techniques in a production environment.

Scalable Big Data Infrastructure

What to coverDetail your experience leveraging Big Data infrastructure to support machine learning at scale. Suggestion: Focus on how you optimize storage and compute resources to minimize latency in automated map update processes.

Process Automation in Core Map Development

What to coverDescribe a scenario where you used machine learning to automate a manual process within a large-scale data pipeline. How did you measure the success of the automation and ensure it did not introduce regressions?

Model Deployment and Maintenance

What to coverWhat is your strategy for the end-to-end lifecycle management of ML models in production? Discuss your approach to monitoring model performance, handling data drift, and managing versioning across distributed systems.

Technical Direction and Stakeholder Alignment

What to coverAs a Principal Data Scientist, how do you translate complex business requirements into a technical roadmap? Explain how you balance innovation in AI research with the practical constraints of product delivery.

Behavioural practice

Cross-Functional Collaboration

What to coverDescribe a time you had to collaborate with architects and developers to define a complex technical solution. How did you ensure alignment on the technical direction while addressing conflicting priorities?

Driving Technical Innovation

What to coverGive an example of a time you introduced a new AI technology or methodology to a team. How did you manage the transition and overcome resistance to change?

Handling Ambiguity in Technical Strategy

What to coverTell me about a time you were tasked with defining the technical direction for a project with significant ambiguity. How did you gather the necessary information to make an informed decision?

Managing Project Delivery and Quality

What to coverDescribe a situation where you had to balance the need for rapid development with the requirement for high-quality, maintainable code. How did you ensure the final output met both business and technical standards?

Sharpen your knowledge

Topics to prepare

Scalable Machine Learning Architecture
The role requires driving technical direction for global-scale digital mapping using highly scalable infrastructure. Review distributed computing patterns and system design principles for deploying machine learning models across multiple environments to ensure high availability and performance.
Generative AI and Multimodal Integration
The position focuses on cutting-edge AI domains including GenAI, LLMs, and Multimodal AI. Study the integration of these models into existing data pipelines and the conceptual challenges of aligning disparate data types (text, image, spatial) for map technology applications.

Skills to revise

Big Data Infrastructure Management
Practice working with large-scale data processing frameworks. Focus on optimizing data ingestion and transformation workflows that support machine learning models, as the role emphasizes leveraging Big Data infrastructure for global-scale mapping.
Computer Vision for Spatial Data
Develop hands-on experience with computer vision techniques relevant to mapping, such as object detection, segmentation, or feature extraction from imagery. This is essential for the role's focus on innovating advanced machine learning solutions for map technology.
Process Automation Engineering
Refine skills in automating complex data science workflows. The role specifically requires defining the technical direction for the next generation of process automation within the HERE Core Map ecosystem.

Your preparation checklist

Analyze HERE Core Map architecture
Research HERE's public documentation on their mapping technology and digital map infrastructure to understand how Big Data and AI are currently integrated into their global-scale mapping products.
Prepare a Generative AI and Computer Vision project walkthrough
Develop a presentation of a past project that demonstrates your ability to apply LLMs or Computer Vision to large-scale datasets, specifically focusing on how you handled scalability and process automation.
Formulate a strategy for multimodal AI integration
Prepare to discuss how you would architect a system that combines visual data from mapping sensors with textual or contextual data from LLMs to improve map accuracy or automation.
Draft technical questions for the interview
Prepare questions regarding the current challenges in HERE's map automation pipelines, the specific Big Data infrastructure stack used for model training, and how the team balances innovation with production-grade deployment.
Review AI deployment and maintenance practices
Be ready to explain your experience in the full lifecycle of AI applications, including how you manage model drift, deployment across multiple environments, and the maintenance of production-grade machine learning systems.

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