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# Principal Data Scientist, Gen AI and Vision

- **Company:** HERE
- **Location:** Burlington, MA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $195,000.0 - $210,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Systems Theories, Machine Learning, Visual Systems, Pytorch, Deep Learning, Information Technology, Hardware Infrastructure
- **Published:** July 4, 2026
- **Apply:** https://www.disabledperson.com/jobs/73478548-principal-data-scientist-gen-ai-and-vision

## About the Role

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

## Description

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


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