We are hiring a Lead Data Scientist to own applied AI quality, data strategy, and downstream usefulness across advanced AI, computer vision, and perception systems. This person will define how we determine whether a system is working, where it is failing, what quality bar is required for release, and how data, evaluation, and applied modeling should evolve to improve the product.
This is a senior role for someone who can bring rigor to ambiguous technical programs, establish evaluation systems, and translate model behavior into product decisions, data strategy, and concrete improvement loops.
What You Will Own
- 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
What You Will Do
- 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
Who are you?
What We Are Looking For
- Strong background in applied machine learning, computer vision, synthetic-data evaluation, or perception-system validation
- Experience designing metrics and evaluation frameworks for generative, simulation, or perception systems
- Experience connecting model behavior, data quality, and product outcomes in ambiguous AI systems
- Ability to translate research-quality experiments into practical engineering and release decisions
- Strong analytical judgment and clear written communication
- Comfort owning both strategy and execution in a small team
Education & Experience
- Master's or PhD in Computer Science, AI, Machine Learning, or related field.
- 5-8 years of experience in deep learning, computer vision, or multimodal AI.
Nice To Have
- Experience with simulation, autonomous systems, geospatial AI, or map-grounded perception tasks
- Familiarity with video quality metrics, structural similarity measures, temporal consistency checks, segmentation and detection evaluation, or label-quality assessment
- Experience assessing synthetic-to-real transfer, dataset usefulness for downstream models, data curation strategy, or production quality governance
The expected base salary range for this position is $160,000 to $170,000 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.
Skills Required
- Master's or PhD in Computer Science, AI, Machine Learning, or related field
- 5-8 years of experience in deep learning, computer vision, or multimodal AI
- Strong background in applied machine learning, computer vision, synthetic-data evaluation, or perception-system validation
- Experience designing metrics and evaluation frameworks for generative, simulation, or perception systems
- Experience connecting model behavior, data quality, and product outcomes in ambiguous AI systems
- Ability to translate research-quality experiments into practical engineering and release decisions
- Strong analytical judgment and clear written communication
- Comfort owning both strategy and execution in a small team
- Experience with simulation, autonomous systems, geospatial AI, or map-grounded perception tasks
- Familiarity with video quality metrics, structural similarity measures, temporal consistency checks, segmentation and detection evaluation, or label-quality assessment
- Experience assessing synthetic-to-real transfer, dataset usefulness for downstream models, data curation strategy, or production quality governance
HERE Technologies Compensation & Benefits Highlights
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Leave & Time Off Breadth — Time-away programs are broad, featuring Volunteer Time Off and a formal Sabbatical Policy. Generous vacation/holiday allowances and discretionary PTO in the U.S. are also highlighted.
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Flexible Benefits — Flexible work is a standout, with a hybrid ‘Flexi Work Options’ model, extra work-from-home days, a home-office allowance, and the ability to work from different offices or locations. These elements provide notable work–life balance support.
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Healthcare Strength — Core health coverage is comprehensive, including medical, dental, vision, life/disability insurance, and mental-health/EAP support. Healthcare offerings are presented as part of a well-rounded total rewards package.
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.

