This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Data Scientist based in the United States.
This role sits at the intersection of applied machine learning, computer vision, and AI system evaluation, with a strong focus on defining what “good” looks like for complex AI outputs. You will be responsible for building the frameworks that determine model quality, reliability, and readiness for production across perception and generative AI systems. The position blends strategic ownership with hands-on analytical work, translating ambiguous model behavior into clear, measurable performance standards. You will work closely with engineering, modeling, and platform teams to guide data strategy, evaluation design, and continuous improvement loops. A key part of the role involves turning experimental results into actionable product and release decisions. This is a high-impact opportunity for someone who thrives in shaping how AI systems are measured, validated, and ultimately trusted in real-world applications.
Accountabilities:
- Own and define the end-to-end evaluation and quality strategy for advanced AI, computer vision, and perception systems.
- Design and implement metrics to assess output quality, structural fidelity, temporal consistency, robustness, and downstream usability.
- Develop data validation strategies and quality frameworks to improve model performance and real-world applicability.
- Lead failure analysis, artifact auditing, and taxonomy development to identify system weaknesses and improvement opportunities.
- Establish release readiness criteria, human review protocols, and acceptance thresholds for complex AI systems.
- Translate evaluation results into data strategy decisions, experiment prioritization, and modeling improvement roadmaps.
- Partner with cross-functional teams to ensure model outputs align with product requirements and operational constraints.
- Build reporting and experiment frameworks that enable evidence-based decision-making across AI development cycles.
Requirements:
- Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
- 5–8 years of experience in applied machine learning, computer vision, multimodal systems, or perception-focused AI.
- Strong experience designing evaluation frameworks, metrics, and validation systems for AI or data-driven products.
- Ability to connect model behavior, data quality, and product outcomes in ambiguous or evolving technical environments.
- Experience translating research experiments into production-ready insights and engineering decisions.
- Strong analytical thinking with the ability to define structured evaluation approaches for complex systems.
- Excellent written and verbal communication skills, with the ability to clearly explain technical findings to diverse stakeholders.
- Exposure to areas such as synthetic data, simulation systems, geospatial AI, or autonomous perception is a plus.
Benefits:
- Competitive base salary with eligibility for annual performance-based bonus
- Comprehensive medical, dental, and vision insurance coverage
- Retirement savings plans and long-term financial security options
- Generous paid time off and flexible leave policies
- Remote-friendly work environment across the United States
- Opportunities to work on advanced AI, computer vision, and perception technologies
- Inclusive, innovation-driven culture focused on real-world AI impact