AI Technical Lead (Remote)
Role: AI Technical Lead (Remote) Location: Remote (Work from Anywhere) Payout: $600k–$2M/year Role Overview: This role is a full-time position focused on improving frontier AI models through rigorous evaluation, failure analysis, and iterative development. The work centers on designing and optimizing machine learning-oriented data systems, translating real-world problems into structured evaluation frameworks, and collaborating with researchers and domain experts to drive measurable improvements in model performance across domains such as finance, healthcare, and engineering. The role matters…
- Remote
- ["Canada"]
- Posted Jun 24, 2026
- 1 position
Job summary
Role: AI Technical Lead (Remote) Location: Remote (Work from Anywhere) Payout: $600k–$2M/year Role Overview: This role is a full-time position focused on improving frontier AI models through rigorous evaluation, failure analysis, and iterative development. The work centers on designing and optimizing machine learning-oriented data systems, translating real-world problems into structured evaluation frameworks, and collaborating with researchers and domain experts to drive measurable improvements in model performance across domains such as finance, healthcare, and engineering. The role matters because it directly shapes how AI systems learn, reason, and perform in high-stakes environments by connecting human expertise with advanced AI training pipelines. Key Responsibilities: • Own research and evaluation initiatives end-to-end—problem framing, data design, quality calibration, and signal validation. • Design machine-learning-oriented data systems that include task definitions, annotation schemas, rubrics, incentives, and pipelines optimized for downstream model performance. • Analyze model and system failures to identify root causes, edge cases, and opportunities for improvement. • Translate ambiguous, real-world behavior into structured evaluation frameworks and new data categories. • Iterate rapidly on evaluations, datasets, and feedback loops to improve model and system performance. Required Skills & Qualifications: • Experience in research signal judgment for AI/ML systems, including evaluation design and validation of experimental results. • Strong background in ML-oriented data design, with proficiency in defining annotation schemas and rubrics that align with model training objectives. • Ability to translate operations-to-research insights, bridging gaps between domain expertise and technical execution. • Hands-on experience with reinforcement learning environments or related ML evaluation systems. • Background in designing and iterating on data pipelines that maintain quality while achieving velocity in production environments. More About the Opportunity: This role offers the chance to contribute to the development of cutting-edge AI systems used by labs and enterprises to train foundational models and build reliable AI agents. You’ll work within a robust data engine that includes an AI recruiter agent, a high-performance data platform, and performance monitoring systems, enabling scalable impact across global expert networks. Equal Opportunity Employer: We hire based on skills and expertise. All qualified candidates are welcome regardless of background, experience, or prior employment history. Applications are reviewed solely on demonstrated technical ability and qualifications. Apply Now!
What you’ll do
The AI Technical Lead will own research and evaluation initiatives from problem framing to signal validation. They will design machine-learning-oriented data systems and analyze model failures to drive improvements.
Requirements
Candidates should have experience in research signal judgment for AI/ML systems and a strong background in ML-oriented data design. Hands-on experience with reinforcement learning environments and data pipeline design is also required.
Other relevant skills
Identified from the job description. Confirm important requirements above.
- Research Signal Judgment
- Evaluation Design
- Validation
- Machine Learning
- Data Design
- Annotation Schemas
- Rubrics
- Reinforcement Learning
- Data Pipelines
- Quality Calibration
- Signal Validation
- Failure Analysis
- Iterative Development
- Problem Framing
- Feedback Loops
- Domain Expertise
Additional details
- Minimum experience
- 10+ years
