Algorithm Evaluation Manager

** Role Number:*
* ** Summary*
* We are looking for an experienced and highly motivated Engineering Manager to lead a dynamic team focused on machine learning algorithms or Large Language Model (LM) evaluation. In this role, you will guide a team responsible for the critical data and evaluation pipelines that ensure our models are accurate, robust, and performant. The ideal candidate will bring a strong mix of technical leadership, expertise in data curation and annotation processes, and deep analytical skills.

You will collaborate closely with cross-functional research and engineering teams, requiring exceptional communication and strategic thinking.

** Description*
* As the Engineering Manager for this team, you will be at the forefront of our AI/ML development lifecycle. Your day-to-day responsibilities will include:
- Team Leadership:
Lead, mentor, and grow a team of engineers and data specialists. Foster a culture of innovation, rigorous analysis, and continuous learning.
- Evaluation Strategy:
Define and execute the evaluation strategy for both CV and LM models. Build robust, scalable evaluation pipelines that accurately reflect real-world performance.
- Data Pipeline Management:
Oversee the end-to-end data lifecycle. This includes establishing data curation guidelines, managing data quality, and optimizing large-scale annotation workflows with external vendors or internal teams.
- Analytical Deep Dives:
Guide the team in performing rigorous data analysis to troubleshoot model regressions, uncover data quality issues, and identify opportunities for algorithmic improvements.
- Strategic Alignment:
Act as the primary point of contact for your team, communicating progress, bottlenecks, and strategic data needs to leadership and partner teams.

** Minimum Qualifications*
* +

Education & Experience:

BS and a minimum of 10 years relevant industry experience

+ Management

Experience:

2+ years of direct people management experience, with a track record of hiring, mentoring, and leading high-performing technical teams.

+ Domain Expertise:
Proven experience in model evaluation, benchmarking, and A/B testing methodologies for machine learning models (Computer Vision or Foundation Models).

+ Inference

Infrastructure: Familiarity with the design and architecture of machine learning inference pipelines and underlying infrastructure.

+ Data & Annotation:
Hands-on experience designing and managing data curation strategies and human-in-the-loop annotation processes.

+ Data Analysis:
Strong analytical skills with the ability to dive deep into datasets to identify trends, biases, and areas for model improvement.

+ Communication:
Excellent verbal and written communication skills, with the ability to translate complex technical concepts to both technical and non-technical stakeholders.

** Preferred Qualifications*
* + Advanced Degree:
PhD in Computer Science, Machine Learning, or a related field.

+ Deep Domain Knowledge:
Expertise in both Computer Vision (CV) algorithms and Large Language Model (LM) evaluation methodologies (e.g., RLHF, prompt evaluation).

+ Scale & Operations:
Experience scaling large data operations, managing complex annotation workflows, and working directly with external data vendors.

+ Technical Stack:
Familiarity with Python, SQL, and ML frameworks (e.g., PyTorch) to effectively review technical work and guide engineering decisions.

+ Cross-Functional Leadership:
Demonstrated ability to drive strategic alignment across downstream product teams, ML researchers, and platform engineers in a highly matrixed environment.