HERE Technologies Research Scientist Interview Guide 2026

What is a Research Scientist at HERE Technologies?

A Research Scientist at HERE Technologies sits at the intersection of cutting-edge academic research and large-scale industrial application. You are responsible for developing the core intelligence that powers the world’s leading location platform. This role is not just about writing papers; it is about building the algorithms that enable autonomous driving, optimize global supply chains, and redefine how millions of users interact with the physical world through HD Maps and real-time spatial data.

The impact of this position is immense. At HERE, we deal with petabytes of data from sensors, satellites, and vehicle probes. As a Research Scientist, you will tackle challenges in Computer Vision, Machine Learning, Graph Theory, and Spatial Optimization. Your work directly influences the safety of self-driving features and the efficiency of urban mobility. You will be expected to transform ambiguous location-based problems into elegant, scalable mathematical models that can be deployed into our production environment.

What makes this role uniquely compelling is the sheer scale and complexity of the HERE ecosystem. You aren't just working on a standalone app; you are contributing to a "digital twin" of the earth. This requires a mindset that values both theoretical rigor and practical performance. You will collaborate with elite engineers and product designers to ensure that your research doesn't just stay in a lab but becomes a critical component of the global location infrastructure.

Common Interview Questions

Expect a mix of theoretical deep dives and practical coding challenges. The following questions are representative of the patterns seen in recent Research Scientist interviews.

Technical & Research Domain

These questions test your fundamental knowledge and your ability to apply research to location-specific problems.

How do you handle outliers and noise in GPS probe data when training a traffic prediction model?
Explain the difference between various clustering algorithms (e.g., K-Means vs. DBSCAN) and when to use them for spatial data.
How would you design a system to automatically detect road sign changes using vehicle camera imagery?
Describe a time you had to implement a paper from scratch. What challenges did you face in making it work with real-world data?

Coding & Algorithms

These are typically conducted on a whiteboard or a shared coding environment to test your implementation skills.

Write a function to determine if two geographic bounding boxes overlap.
Given a list of map coordinates, find the "K" closest points to a user’s current location.
Implement a thread-safe cache for storing frequently accessed map tiles.
How would you efficiently compress a polyline representing a road path without losing significant detail?

Behavioral & Leadership

These questions focus on how you work within a team and handle the ambiguity inherent in research.

Tell me about a time a research project failed. What did you learn, and how did you pivot?
How do you handle a situation where your research findings contradict the current product direction?
Describe a time you had to explain a complex technical concept to a stakeholder who had no background in your field.
How do you stay updated with the latest trends in Machine Learning and Spatial Data?

See every interview question for this role

Practice questions from our question bank

Curated questions for HERE Technologies from real interviews. Click any question to practice and review the answer.

Medium

Model Evaluation

Evaluate Cross-Validation Impact on Model Performance

Analyze how cross-validation affects the performance metrics of a regression model predicting housing prices.

Supervised Learning

Cross-Validation

Easy

Coding

Clean Maintainable Test Code

Explain how to write automated tests that stay readable, isolated, and easy to update as code changes.

Arrays

Strings

Hash Tables

Easy

Coding

Choosing Data Structures at Scale

Explain which data structures work best for large datasets based on access patterns, memory use, and update costs.

Arrays

Hash Tables

Heap

Medium

NLP

Compare Positional Encodings in Transformers

Implement and compare sinusoidal vs learned positional encodings in a Transformer for legal clause classification where word order changes meaning.

Tokenization

Language Models

Deep Learning

Hard

Model Evaluation

Evaluate MIMO Channel Estimation Errors

Assess how rising channel estimation error in a 4x4 MIMO system drives BER, outage, and throughput degradation, and recommend fixes.

Accuracy

Calibration

Medium

Statistics & Probability

Root-Cause a Failing QC Assay

Use normal/t-tests and a lot-comparison Welch test to decide if a QC assay failure indicates a true mean shift or a bad reagent lot.

Hypothesis Testing

Statistical Significance

Experimentation

Easy

NLP

Classify RL-LLM Research Interests

Build a transformer-based multi-label classifier to categorize candidate statements about RL and LLM research interests into recruiting themes.

Tokenization

Language Models

Deep Learning

Medium

Machine Learning

Predict Turbine Failures from Sensor Data

Build a gradient-boosted classifier to predict turbine failures 7 days ahead from sensor, alarm, and maintenance history data.

Supervised Learning

Cross-Validation

Feature Engineering

Medium

Machine Learning

Rank Human Preferences for RLHF

Train a pairwise preference model for RLHF that predicts which LLM response humans prefer and produces deployable reward scores.

Supervised Learning

Regularization

Deep Learning

Medium

Machine Learning

Harden Vision Models Against Adversarial Attacks

Train a CNN for traffic-sign classification, measure adversarial robustness with FGSM, and improve deployed-model resilience with adversarial training.

Regularization

Neural Networks

Deep Learning

Hard

Machine Learning

Conditioned Reverse Diffusion Process for User Prompts

Develop a reverse diffusion model conditioned on user editing prompts for text generation tasks.

Neural Networks

Gradient Descent

Deep Learning

Easy

Coding

Explain Solution Time Complexity

Explain how to analyze a coding solution's time complexity by counting operations, loops, and data structure costs.

Arrays

Sorting

Searching

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Machine Learning

Build a Multimodal LLM for Real-Time Video Editing

Develop a multimodal large language model to enhance user experience in real-time video editing applications.

Supervised Learning

Deep Learning

Feature Engineering

Easy

Statistics & Probability

Explain P-Value in Marketing Test

Use a two-proportion z-test to assess a banner A/B test, then explain the resulting p-value clearly to a non-technical stakeholder.

Hypothesis Testing

Confidence Intervals

P-Values

Easy

Statistics & Probability

P-Value and Power for Button Redesign

Use a two-proportion z-test and power analysis to explain whether a 1-point signup lift from a button redesign is statistically credible.

Hypothesis Testing

P-Values

Power Analysis

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Model Evaluation

Choose RMSE or MAE

Explain RMSE vs MAE using two rent prediction models and recommend which metric and model better fit a business sensitive to large errors.

Regression

RMSE

MAE

Easy

Statistics & Probability

Confidence Interval for Email CTR Test

Construct and interpret a 95% confidence interval for the CTR lift in an email A/B test to communicate uncertainty in the experiment result.

Confidence Intervals

Statistical Significance

Experimentation

Easy

Statistics & Probability

Explaining P-Value and Power

Compute a two-proportion z-test and explain p-value and statistical power for an onboarding experiment with an inconclusive result.

Hypothesis Testing

P-Values

Power Analysis

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Getting Ready for Your Interviews

Success in the HERE Technologies interview process requires a balance of deep technical specialization and the ability to communicate complex ideas to diverse stakeholders. We look for scientists who are not only masters of their domain but also pragmatic problem solvers who understand the constraints of a production environment.

Technical Depth and Domain Expertise – You must demonstrate a profound understanding of your specific research area, whether it is Deep Learning, Computational Geometry, or Signal Processing. Interviewers will push you to explain the "why" behind your methodology and how you handle edge cases in noisy, real-world spatial data.

Algorithmic Problem-Solving – Beyond theoretical knowledge, you must be able to translate logic into efficient code. We evaluate your ability to select the right data structures and optimize algorithms for both time and space complexity, often through whiteboard coding exercises.

Scientific Communication – As a researcher, you will often need to influence product roadmaps. We assess how well you can distill complex technical concepts for non-experts, including product managers and cross-functional team leads.

Collaborative Mindset – HERE is a highly collaborative environment. We look for candidates who are "cordial and welcoming" even under pressure and who can engage in constructive technical debates without ego.

Tip

When discussing your past research, focus on the "so what?"—clearly articulate the business or product impact your findings had in previous roles or academic settings.

Interview Process Overview

The interview process for a Research Scientist at HERE Technologies is designed to be rigorous and comprehensive, reflecting the high bar we set for our research team. While the specific stages may vary slightly depending on the location—such as our hubs in Berlin, Chicago, or Boulder—the core philosophy remains the same: we want to see how you think, how you code, and how you collaborate.

You can expect a multi-stage journey that typically begins with a talent acquisition screen followed by several technical deep dives. The process is known for being "difficult but fair," often involving multiple 1-on-1 sessions with potential peers, team leads, and cross-functional partners. We prioritize a positive candidate experience, ensuring that even the most challenging technical discussions are handled with professional courtesy.

The visual timeline above illustrates the typical progression from your initial application to the final offer. Candidates should prepare for a significant "On-site" or "Final Loop" stage, which often consists of four to six 45-minute sessions. This stage is designed to test your endurance and the consistency of your technical depth across different interviewing groups.

Deep Dive into Evaluation Areas

Algorithmic Foundations & Coding

This area is critical because our research must eventually live in code. We aren't just looking for a "correct" answer; we are looking for clean, efficient, and maintainable implementations. You will likely face whiteboard coding tasks that require you to think on your feet and explain your logic as you go.

Be ready to go over:

Data Structures – Proficiency with trees, graphs, and hash maps is essential for spatial data.
Complexity Analysis – You must be able to provide Big O analysis for every solution you propose.
Optimization – How to refine a brute-force approach into a production-ready algorithm.

Example questions or scenarios:

"Implement an algorithm to find the shortest path between two points in a dynamic graph where edge weights change in real-time."
"Given a set of GPS coordinates, how would you efficiently cluster them to identify frequent stay points?"
"Design a data structure that supports fast spatial queries for points within a specific bounding box."

Note

During whiteboard sessions, don't stay silent. Thinking out loud allows the interviewer to help you if you get stuck and shows your collaborative problem-solving style.

Domain-Specific Research

This is where you demonstrate your "Scientist" credentials. Depending on the team, this could focus on Computer Vision, Natural Language Processing (for address parsing), or Reinforcement Learning. We want to see that you are familiar with the state-of-the-art in your field.

Be ready to go over:

Model Selection – Why choose one architecture over another for a specific location-based task?
Validation Metrics – How do you measure success when ground truth data is sparse or noisy?
Scalability – How does your research model perform when applied to a global dataset?

Advanced concepts (less common):

Simultaneous Localization and Mapping (SLAM)
Point cloud processing and segmentation
Differential privacy in location data

System Design & Integration

Research at HERE does not exist in a vacuum. You must understand how your models fit into a larger software architecture. This involves thinking about data pipelines, latency, and how different services communicate.

Be ready to go over:

Data Pipelines – How to ingest and preprocess massive streams of probe data.
API Design – How other teams will consume the outputs of your research models.
Latency vs. Accuracy – Making trade-offs in real-time navigation scenarios.

Key Responsibilities

As a Research Scientist, your primary responsibility is to drive innovation within your specific domain. You will spend a significant portion of your time exploring new methodologies and conducting experiments to improve the accuracy and richness of the HERE map. This involves staying abreast of the latest academic literature and determining how new breakthroughs can be applied to our specific spatial problems.

Collaboration is a daily requirement. You will work closely with Software Engineers to ensure that research prototypes are robust enough for production. You aren't just handing over a paper; you are often involved in the initial implementation and scaling phases. You will also interface with Product Managers to align your research goals with the strategic needs of the business, such as improving estimated time of arrival (ETA) accuracy or enhancing lane-level guidance for automated vehicles.

Beyond technical tasks, you are expected to contribute to the intellectual property of the company. This includes writing internal white papers, filing patents, and occasionally representing HERE at major international conferences like CVPR, ICML, or KDD. You serve as a technical mentor within the organization, helping to raise the bar for scientific excellence across the engineering teams.

Role Requirements & Qualifications

To be competitive for a Research Scientist position, you should possess a blend of high-level academic training and practical software development skills.

Technical Skills – Expert-level proficiency in Python, C++, or Java is required. You should be comfortable with machine learning frameworks like PyTorch or TensorFlow and have experience with large-scale data processing tools (e.g., Spark, Hadoop).
Experience Level – Most successful candidates hold a PhD or a Master’s degree with significant industry experience in a quantitative field like Computer Science, Mathematics, or Physics.
Soft Skills – Strong communication skills are a "must-have." You must be able to explain your research to both technical and non-technical audiences and remain receptive to feedback during the iterative design process.
Nice-to-have skills – Experience with GIS (Geographic Information Systems), knowledge of Cloud Computing (AWS/Azure), and a track record of peer-reviewed publications are highly valued.

Interview Guides

HERE Technologies

What is a Research Scientist at HERE Technologies?

Common Interview Questions

Technical & Research Domain

Coding & Algorithms

Behavioral & Leadership

See every interview question for this role

Practice questions from our question bank

Sign up to see all questions

Getting Ready for Your Interviews

Tip

Interview Process Overview

Deep Dive into Evaluation Areas

Algorithmic Foundations & Coding

Note

Domain-Specific Research

System Design & Integration

Key Responsibilities

Role Requirements & Qualifications

Tip

Frequently Asked Questions

Note

Other General Tips

Summary & Next Steps

See every interview question for this role