Genius Match

About Us 
We’re building an AI-driven hiring platform designed to transform how organizations evaluate talent. Our product delivers real-time candidate insights, identifies skill gaps, and recommends development pathways - while enabling recruiters to efficiently identify top performers through data-driven decision-making. 

About the Role 
We’re looking for a Psychometrician to join us as a part-time, ongoing consultant. This role combines strategic advisory with hands-on work, ensuring our assessment methodologies are scientifically sound, fair, and compliant with professional and legal standards. 
You’ll collaborate closely with product and engineering to validate and improve our AI-driven assessment systems. 

What You’ll Do 
 

• Evaluate and improve candidate assessment frameworks; 
• Design and run validation studies (criterion-related, construct, content validity); 
• Establish and monitor reliability metrics (e.g., internal consistency, test-retest); 
• Ensure AI-driven scoring aligns with psychometric best practices; 
• Audit algorithms for bias, fairness, and adverse impact; 
• Support calibration and continuous improvement of ranking models; 
• Ensure alignment with EEOC, APA, and SIOP standards; 
• Conduct adverse impact analyses and assess subgroup differences;  
• Recommend and implement bias mitigation strategies; 
• Define and track key measurement KPIs (validity, reliability, fairness); 
• Translate technical findings into clear, actionable insights; 
• Prepare documentation for audits, certification, and external validation. 
  
  Success Metrics 
• Assessment validity and reliability clearly established and documented; 
• Fairness and bias benchmarks defined, tracked, and improved over time; 
• Readiness for certification or third-party validation within agreed timelines. 

Core Requirements 
 

• Master’s or PhD in Psychometrics, Industrial-Organizational Psychology, Statistics, or a related field; 
• 5+ years of experience in assessment validation, ideally in hiring or talent assessment; 
• Strong knowledge of Item Response Theory (IRT) and/or Classical Test Theory (CTT); 
• Experience designing and executing validation studies; 
• Hands-on experience with adverse impact and fairness analysis; 
• Strong analytical and problem-solving skills; 
• Ability to work independently in a remote, flexible environment; 
• Clear communication skills, especially with non-technical stakeholders. 

Nice to Have 
 

• Experience with AI/ML-based hiring or assessment systems;  
• Background in HR tech or talent analytics; 
• Experience supporting audits, certification, or compliance processes;  
• Academic or research affiliation. 

Engagement Details 
 

• Part-time, ongoing consulting role; 
• Fully remote with flexible hours; 
• Mix of strategic advisory and hands-on work. 

About the Client

Our client is building advanced spatial computing and AI-powered solutions that transform large-scale visual data into accurate, high-quality 3D models. Their platform leverages cutting-edge computer vision, cloud infrastructure, and emerging technologies to solve complex real-world problems across industries.

About the Role

We are looking for a Senior Computer Vision & Photogrammetry Engineer to join the client’s team. This is a senior, high-impact role for someone who can bridge deep algorithmic expertise with scalable, production-grade infrastructure.

You will lead the design and deployment of high-performance 3D reconstruction pipelines, working closely with engineering and infrastructure teams to turn massive datasets into precise spatial outputs.

What You’ll Do

• Design and implement photogrammetry pipelines, including Structure from Motion (SfM), Multi-View Stereo (MVS), and bundle adjustment;
• Build and optimize high-performance 3D reconstruction workflows for large-scale datasets;
• Architect and manage compute infrastructure using Docker and Kubernetes;
• Develop data processing pipelines for ingesting, cleaning, and analyzing image and sensor data;
• Optimize algorithms for accuracy, performance, and scalability in production environments;
• Integrate multi-sensor data (e.g., IMU, GPS, LiDAR) for improved spatial accuracy;
• Apply state estimation techniques such as Kalman Filters for sensor fusion;
• Explore and integrate emerging approaches like Neural Radiance Fields (NeRF) and Gaussian Splatting;
• Optimize cloud-based processing workflows, particularly within AWS environments;
• Collaborate with cross-functional teams to productionize research-driven solutions;
• Monitor and improve system performance, reliability, and cost efficiency;
• Contribute to technical direction and best practices across computer vision and infrastructure.

Core Requirements

• 7+ years of experience in computer vision, photogrammetry, or spatial data engineering;
• Strong programming expertise in Python, including data processing with Pandas;
• Deep understanding of 3D geometry, camera models, and coordinate transformations;
• Proven experience with Structure from Motion (SfM) and Multi-View Stereo (MVS) pipelines;
• Strong experience with Docker and Kubernetes in production environments;
• Hands-on experience with large-scale data processing and optimization;
• Experience with at least one key 3D/vision library (e.g., Open3D, PCL, or Ceres Solver);
• Strong problem-solving skills and ability to work on complex technical systems;

Nice to Have

• Experience with SLAM (Simultaneous Localization and Mapping);
• Practical experience with NeRF or Gaussian Splatting;
• Experience implementing Kalman Filters or similar estimation techniques;
• Strong AWS experience (EC2 GPU, S3, Lambda, Batch, SageMaker);
• Background in real-time systems or sensor fusion;
• Experience with Kotlin.

What We Offer

• Competitive market salary;
• Fully remote work;
• Flexible working hours with overlap requirements;
• 28 days of paid time off per calendar year;
• Opportunity to work on impactful, cutting-edge technology;
• Collaboration with a highly professional, international team;
• Challenging projects with strong opportunities for growth and learning.

Success Metrics

• High-performance, scalable 3D reconstruction pipelines deployed to production;
• Measurable improvements in model accuracy and processing efficiency;
• Reliable and cost-optimized cloud infrastructure;
• Successful integration of advanced techniques into production workflows.