• Develop causal estimates for long-term impact of short-term metric movements. • Build frameworks for estimating how the impact of product changes evolves over time. • Create frameworks and tooling to evaluate the heterogeneous impact of product changes • Develop and apply causal inference methods, including experimental, econometric regressions, and quasi-experimental methods to measure a wide-range of platform/product impacts.

What's the role? We are hiring a Principal Data Scientist to lead advanced generative AI and computer vision work across simulation-grounded visual systems, structured-control generation, and production-oriented AI products. This person will help define and build model capabilities that sit at the intersection of generative modeling, scene understanding, controllability, and applied computer vision. This is a senior technical role for someone who can move from early research ambiguity to production-quality model systems while maintaining a high bar for technical depth, rigor, and practical value. This role is intended for someone who can own a major technical pillar, set direction for that domain, and raise the standard for how advanced AI systems are built and shipped. What You Will Own - Own the model strategy and technical direction for advanced generative and multimodal vision systems - Set the research and engineering agenda for model adaptation, evaluation, and production readiness within this domain - Define how model inputs, controls, outputs, and interfaces are represented and integrated into downstream systems - Improve realism, controllability, temporal consistency, robustness, and deployment readiness over time - Work closely with evaluation, simulation, and platform engineers to define durable interfaces between research systems and production workflows - Drive model benchmarking, ablation studies, model-selection decisions, and the path from pre-trained baselines to production-ready capability - Raise the bar for technical quality, architecture, and execution within the model stack What You Will Do - Build and evolve end-to-end model workflows for complex vision and generative AI systems - Identify and reduce failure modes such as structural drift, temporal instability, inconsistent outputs, and production fragility - Define the fine-tuning, adaptation, or hybrid-model strategy when pre-trained models do not meet product requirements out of the box - Partner with the evaluation lead to establish quality metrics, release gates, and production-readiness criteria - Help shape the roadmap from initial prototypes to scalable AI capabilities across perception, generation, and related vision tasks - Contribute to deployment decisions around model packaging, inference optimization, and production performance tradeoffs - Mentor other scientists and act as the senior technical owner for the model domain Who are you? What We Are Looking For: - Deep experience in generative modeling for video, world models, diffusion models, multimodal systems, or adjacent advanced vision domains - Evidence of principal-level scope through technical leadership, research impact, architectural ownership, or shipped systems - Strong background in PyTorch and modern model training, fine-tuning, evaluation, and inference workflows - Experience adapting large pre-trained models to domain-specific use cases and hard production constraints - Good judgment on model quality, controllability, reliability, and deployment tradeoffs - Ability to work across research and engineering boundaries in a small, hands-on team Education & Experience - Master's or PhD in Computer Science, AI, Machine Learning, or related field. - 10+ years of experience in deep learning, computer vision, or multimodal AI. Nice To Have - Experience with synthetic data, robotics, perception systems, geospatial AI, autonomous systems, or advanced mapping products - Familiarity with conditioning mechanisms, structured control inputs, simulation-grounded models, or controllable generation - Experience running large-model inference, optimization, or fine-tuning on AWS GPU infrastructure - Experience taking research models into production environments The expected base salary range for this position is $195,000 to $210,000 per year. Actual compensation will be based on factors such as skills and experience. This position is also eligible for an annual performance bonus, which is subject to company and individual performance. Life at HERE comes with generous benefits to support your health and overall wellness. Benefits available to US-based HERE employees include health (Medical/Dental/Vision) insurance, retirement savings plans, paid time off & leave policies . As part of HERE Technologies employment process, candidates will be required to successfully complete a background verification process. Offers of employment and any related claims are subject to the successful completion of a background verification. Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, HERE will consider for employment qualified applicants with arrest and conviction records. 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. Under Section 503 of the Rehabilitation Act of 1973 and VEVRAA, we have developed an affirmative action program (AAP) for individuals with disabilities and protected veterans. Portions of the AAP are available for review by applicants and employees through our People Team. #LI-REMOTE 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.

• Own the evaluation framework and quality strategy for advanced AI and vision systems • Define pass/fail metrics for output quality, structural fidelity, temporal consistency, label quality, robustness, and operational repeatability • Own data and validation strategy for improving model quality and downstream usefulness • Lead artifact auditing, failure taxonomy development, release-quality reporting, and evidence-based prioritization • Measure whether outputs are suitable for perception, mapping, generative AI, and customer-facing use cases • Partner with model, simulation, and platform owners to drive quality improvements and production-readiness decisions • Build and evolve metric suites for output quality, fidelity, repeatability, and downstream usefulness • Define human-review protocols and product acceptance thresholds for complex AI systems • Evaluate whether outputs preserve the structure, semantics, and consistency expected by downstream applications • Translate evaluation findings into data strategy, experiment priorities, and applied modeling opportunities • Help define dataset design, validation slices, and quality-improvement loops across the product • Create experiment and release reports that turn technical output into clear product decisions • Help prioritize what the team should fix next based on evidence rather than intuition • Establish evaluation foundations that remain useful across future AI, perception, and mapping capabilities

• O(a) Cientista de Dados I atuará no desenvolvimento de soluções analíticas e projetos de dados, apoiando a construção de modelos estatísticos e de machine learning que contribuam para a tomada de decisão do negócio. • Em parceria com profissionais mais experientes da equipe, será responsável por apoiar etapas como coleta, tratamento e exploração de dados, validação de informações, análise de resultados e acompanhamento do desempenho dos modelos desenvolvidos. • O desafio é atuar em um ambiente dinâmico, contribuindo para a evolução da cultura data-driven e para a geração de valor por meio de análises e soluções baseadas em dados.