- Design and implement end-to-end machine learning pipelines covering data ingestion, training, evaluation, deployment, monitoring, and retraining.
- Develop scalable infrastructure that enables consistent and repeatable movement of models from research to production.
- Own model-serving architectures for both batch and real-time inference workloads.
- Establish CI/CD practices for machine learning, including automated testing, model packaging, version control, and deployment automation.
- Build and maintain containerized and orchestrated environments using technologies such as Docker and Kubernetes.
- Optimize infrastructure utilization for compute and GPU-intensive workloads while balancing performance and cost efficiency.
- Implement model and data versioning, reproducibility standards, and rollback mechanisms.
- Develop monitoring, alerting, and observability frameworks for production ML systems.
- Implement mechanisms for detecting data drift, model degradation, latency issues, and operational risks.
- Support continuous feedback loops, human-in-the-loop workflows, and retraining processes that improve model quality over time.
- Translate complex operational challenges into scalable, secure, and maintainable platform solutions.
- Evaluate emerging MLOps technologies, orchestration frameworks, and industry best practices to guide tooling decisions.
- You have a Bachelor or Master's degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or a related field, along with demonstrated experience delivering machine learning systems into production environments at scale.
- 4+ years of experience in MLOps, ML platform engineering, or ML infrastructure engineering.
- Deep expertise across the machine learning lifecycle, including training, evaluation, deployment, monitoring, and retraining.
- Strong experience implementing CI/CD pipelines, automated testing, model packaging, and release management for ML systems.
- Hands-on proficiency with Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP, including infrastructure-as-code practices.
- Experience building scalable model-serving solutions supporting both batch and real-time inference workloads.
- Strong knowledge of observability, monitoring, data drift detection, model validation, and operational excellence.
- Proficiency in Python and strong software engineering fundamentals, including testing, code quality, and version control.
- Experience with workflow orchestration and MLOps platforms such as MLflow, Kubeflow, Airflow, DVC, or comparable technologies.
- Practical experience supporting large-scale data processing environments, distributed computing, streaming architectures, or Spark-based systems.
- Familiarity with LLMOps practices, retrieval infrastructure, vector databases, and operationalization of AI-powered systems.
- The ability to make informed architectural decisions, lead technical initiatives, and collaborate effectively across multidisciplinary teams.
- Excellent communication, mentoring, and stakeholder engagement skills.
- Exposure to geospatial platforms, spatial data infrastructure, edge AI, TinyML, LiDAR, drone data processing, Go, Java, or C++ is valuable in helping accelerate impact within HERE’s innovation ecosystem.
What we offer
HERE offers an opportunity to work in a cutting-edge technology environment with challenging problems to solve! You can make a direct impact on delivery of company´s strategic goals and the freedom to decide how to perform your work. We will support you in delivering your day-to-day tasks and achieving your personal goals and developing your skills. Personal development is highly encouraged at HERE. You can take different courses and training at our online Learning Campus and join cross-functional team projects within our Talent Platform.
HERE is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, age, gender identity, sexual orientation, marital status, parental status, religion, sex, national origin, disability, veteran status, and other legally protected characteristics.
Who are we?
HERE Technologies is a location data and technology platform company. We empower our customers to achieve better outcomes – from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely.
At HERE we take it upon ourselves to be the change we wish to see. We create solutions that fuel innovation, provide opportunity and foster inclusion to improve people’s lives. If you are inspired by an open world and driven to create positive change, join us. Learn more about us on our YouTube Channel.
Skills Required
- Bachelor’s, master’s, or Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or a related field
- 4+ years of experience in MLOps, ML platform engineering, or ML infrastructure engineering
- Experience delivering machine learning systems into production environments at scale
- Deep expertise across machine learning training, evaluation, deployment, monitoring, and retraining
- Strong experience implementing CI/CD pipelines, automated testing, model packaging, and release management for ML systems
- Hands-on proficiency with Docker, Kubernetes, cloud platforms, and infrastructure as code
- Experience building scalable model-serving solutions for batch and real-time inference workloads
- Strong knowledge of observability, monitoring, data drift detection, model validation, and operational excellence
- Proficiency in Python and strong software engineering fundamentals, including testing, code quality, and version control
- Experience with workflow orchestration and MLOps platforms such as MLflow, Kubeflow, Airflow, or DVC
- Practical experience with large-scale data processing, distributed computing, streaming architectures, or Spark-based systems
- Familiarity with LLMOps, retrieval infrastructure, vector databases, and operationalizing AI-powered systems
- Ability to make architectural decisions, lead technical initiatives, and collaborate across multidisciplinary teams
- Excellent communication, mentoring, and stakeholder engagement skills
- Exposure to geospatial platforms, spatial data infrastructure, edge AI, TinyML, LiDAR, drone data processing, Go, Java, or C++
HERE Technologies Compensation & Benefits Highlights
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Leave & Time Off Breadth — Time off is a standout, with generous vacation policies, discretionary/unlimited PTO in the U.S., and options like sabbaticals and volunteer time off. Employees also highlight hybrid-friendly practices that make taking time off practical.
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Healthcare Strength — Health coverage is often characterized as solid, with mentions of decent plans and, in some cases, low premiums. Ancillary benefits such as vision, HSA, life insurance, and AD&D are also cited positively.
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Flexible Benefits — Work flexibility is consistently emphasized through hybrid schedules and remote options. Formal “Flexi Work Options” and remote-work allowances in some regions reinforce this strength.
HERE Technologies Insights
What We Do
HERE Technologies is a location data and technology company that created the first digital map over 35 years ago. Today we are the world's leading location platform company with a global footprint across 52 countries. Although our strongest presence is in the automotive industry, we also work with leading companies across a wide range of industries, including transport and logistics, mobility, manufacturing and retail and the public sector.
Why Work With Us
At HERE, we're always excited about discovering people who share our passion for building innovative solutions that make the world easier to navigate. We believe our success is powered by our team's diversity, creativity and collaboration and we're always looking for opportunities to grow it further.
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HERE Technologies Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.

