Jobs at HERE Technologies

Lead design and deliver efficient perception models for road scenes, optimize models for edge devices (Jetson, Snapdragon, ARM), improve real-time inference via quantization/pruning/distillation, convert/deploy with TensorRT/ONNX, build scalable training/evaluation with PyTorch or TensorFlow, and collaborate with embedded and software teams to produce production-ready edge AI systems.

Design, develop, and deploy ML models (classification, forecasting, NLP). Build scalable data pipelines, integrate AI solutions into production, and operationalize LLM and generative AI use cases. Improve model performance, ensure monitoring, governance, and lifecycle management while collaborating with Data Ops, Product, and Engineering teams.

Support production management with quality monitoring, audits, data-driven analysis, process re-engineering, continual improvement, Lean Six Sigma methods, ISO-related auditing, and basic prompt engineering/AI deployments across cross-functional and international teams.

Design and implement backend and data-processing features, maintain and debug large-scale low-latency services, run benchmarks and experiments, build automation and data analysis tools, and create simple UIs and dashboards to present results.

Design, build, and deploy scalable machine learning and generative AI solutions. Develop data pipelines, optimize and monitor models, integrate AI into production, and collaborate with Product, Data Operations, and Engineering to deliver reliable, responsible AI systems.

Lead design, development, and production deployment of AI systems combining LLMs, agentic architectures, embedding-based retrieval, multimodal models, and deep learning. Build representation and retrieval layers, scale training and inference, implement RAG and agentic workflows, apply vision-language and spatial reasoning, mentor engineers, define KPIs, and translate research into production-quality Python code using PyTorch/TensorFlow and large-scale data tooling.

Lead design, develop, and operate scalable microservices on AWS using Java/Scala/Python. Own end-to-end feature delivery, write technical designs, mentor engineers, collaborate with global stakeholders, and drive engineering best practices and reliability.

Design, build, and operate end-to-end MLOps infrastructure and pipelines for training, evaluation, deployment, monitoring, and retraining. Own model-serving for batch and real-time workloads, implement CI/CD, containerized orchestration, model/data versioning, observability, and drift detection. Optimize GPU/compute utilization, enable reproducible research-to-production workflows, and evaluate emerging MLOps tools while partnering with researchers and platform teams.

Design, build, and productionize machine learning and generative AI solutions. Develop scalable data pipelines, deploy and monitor models, optimize performance, and collaborate with Product, Data Operations, and Engineering to translate business problems into reliable AI-powered applications.

Design, develop, and deploy ML models (classification, forecasting, NLP, LLMs); build scalable data pipelines; integrate AI into production; collaborate with Data Ops, Product, and Engineering; improve model performance and scalability; and implement model monitoring, governance, and lifecycle management.

Lead design and deployment of machine learning and analytics solutions for global-scale map-making. Build and test predictive and statistical models, drive process automation on Big Data infrastructure, mentor data science teams, and collaborate with researchers, developers, architects, and stakeholders to deliver scalable map-related ML systems.

Lead development and deployment of scalable machine learning and analytics solutions for global map-making. Design process automation, build predictive and statistical models, lead modeling teams, collaborate with researchers, developers, architects, and customers, and work with big-data pipelines and point-cloud/computer-vision methods.

Lead a global Quality Assurance & Testing organization to define and execute scalable quality strategy, automation-first validation, data-driven measurement, and independent end-to-end testing. Build quality measurement capabilities, drive AI-enabled testing and predictive quality models, partner with Product and Engineering to influence releases, and grow multidisciplinary teams to improve release confidence and customer-facing quality outcomes.

Design, build, and maintain scalable backend and full-stack systems; integrate AI capabilities (RAG, LLMs, vector search) into applications; collaborate across teams, participate in architecture and system design, resolve performance and reliability issues, write testable code, and improve development processes and tooling.

Design and build scalable systems, data pipelines, and full‑stack applications to validate product quality and produce KPI reporting. Integrate AI-driven features (RAG, agents, semantic search), automate statistical analysis, support CI/CD, conduct code reviews, and collaborate in Agile teams to translate business needs into technical solutions.