About PFF PFF is a world leader in the collection, analysis, and application of sports data. With clients spanning the majority of the professional and college football landscapes, as well as many media entities and other sports fans at large, our employees have the opportunity to drive changes in the way sports are played and consumed by coaches, athletes and consumers. We are looking for a Computer Vision (CV) Engineer/Scientist to join our foundational team and help develop high-performance object detection and tracking models tailored for football sports footage. You will be responsible for deployment of the current architectures within our framework and further improving the accuracy of the models with training on the go. You’ll work closely with a cross-functional team of engineers, analysts, and football experts to push the boundaries of what’s possible in sports tech. What You’ll Do ? Maintain a CI/CD pipeline for CV models and workflows Handle training and fine-tuning of CV models for object detection, tracking, homography, multi-modal analysis, etc., Be proactive in researching on latest CV developments and bring proof of concept projects (POC) Build scalable data engines for evaluation of models and their integration to existing framework Contribute towards development of custom datasets for training and validation Collaborate cross-functionally with data scientists, football analysts, and engineers to deploy CV systems in production Evaluate models with custom dataset Use internal and external APIs including AWS platform Maintain efficient documentation in Git and Confluence Minimum Qualifications 5+ years of experience in Computer Vision or Applied Machine Learning roles. Hands-on experience with modern detection and tracking Strong understanding of projective geometry, camera calibration, and video homography. Proficiency in Python and CV/ML tools such as PyTorch, OpenCV, MMDetection, Ultralytics and relevant platforms. Knowledge of hybrid inference strategies (e.g., cascade models, frame-skipping, multi-stage inference). Experience in deploying models in real-time systems or stream-processing frameworks. Strong Python and CV tooling background: OpenCV, MMDetection, Ultralytics, NumPy, SciPy. Comfort designing evaluation pipelines tailored to real-world use cases and edge cases. Experience with large-scale video datasets and temporal training techniques. Experience in AWS for training and deploying models Preferred Qualifications B.E/B.Tech/M.Sc/M.Tech in relevant field Prior experience with sports video, broadcast, or ball/player tracking. Experience in training and deploying models using AWS The Pay Range For This Role Is 190,000 - 220,000 USD per year (Remote (United States)) 280,000 - 330,000 CAD per year (Remote (Canada))
You will be joining PFF, a world leader in sports data collection, analysis, and application. As a Computer Vision (CV) Engineer/Scientist, your role will involve developing high-performance object detection and tracking models specifically tailored for football sports footage. Here's what you will be responsible for: - Maintain a CI/CD pipeline for CV models and workflows - Handle training and fine-tuning of CV models for object detection, tracking, homography, multi-modal analysis, etc. - Proactively research the latest CV developments and bring proof of concept projects (POC) - Build scalable data engines for evaluating models and integrating them into the existing framework - Contribute to the development of custom datasets for training and validation - Collaborate cross-functionally with data scientists, football analysts, and engineers to deploy CV systems in production - Evaluate models with custom datasets - Utilize internal and external APIs including the AWS platform - Maintain efficient documentation in Git and Confluence Qualifications Required: - 5+ years of experience in Computer Vision or Applied Machine Learning roles - Hands-on experience with modern detection and tracking - Strong understanding of projective geometry, camera calibration, and video homography - Proficiency in Python and CV/ML tools such as PyTorch, OpenCV, MMDetection, Ultralytics, and relevant platforms - Knowledge of hybrid inference strategies (e.g., cascade models, frame-skipping, multi-stage inference) - Experience in deploying models in real-time systems or stream-processing frameworks - Strong Python and CV tooling background: OpenCV, MMDetection, Ultralytics, NumPy, SciPy - Comfort designing evaluation pipelines tailored to real-world use cases and edge cases - Experience with large-scale video datasets and temporal training techniques - Experience in AWS for training and deploying models Preferred Qualifications: - B.E/B.Tech/M.Sc/M.Tech in a relevant field - Prior experience with sports video, broadcast, or ball/player tracking - Experience in training and deploying models using AWS This role offers a competitive salary range: - 190,000 - 220,000 USD per year (Remote - United States) - 280,000 - 330,000 CAD per year (Remote - Canada),
The Opportunity Re-architecting Our Core Data. We are embarking on a multi-year product modernization effort culminating in the 2027 season. We are seeking a skilled Backend/API Engineer with strong data expertise to contribute to the redesign and performance optimization of our core analytical data warehouse and related APIs. About the Role This is a hands-on role where you will design and build high-performance APIs and data infrastructure features, working directly with our engineering team to implement our future-state architecture. Your work will directly impact PFF's ability to deliver high-performance, cutting-edge analytics to our global client base. Responsibilities API Development: Design, develop, and maintain high-performance, low-latency APIs (e.g., REST, GraphQL) that serve analytical data to client-facing applications and internal tools. Data Modeling and Implementation: Collaborate with Senior Engineers to implement new data models by translating architectural designs into efficient PostgreSQL schemas, focusing on balancing normalization with analytical read performance. Database Optimization: Implement and maintain advanced PostgreSQL strategies, including robust indexing, partitioning, caching, and query optimization, to ensure fast data access for APIs. Data Pipeline Contribution: Design, implement, and maintain scalable data pipelines and ETL/ELT functions across distributed systems to ensure data reliability and freshness. System Reliability: Contribute to the overall stability and scalability of our backend services and data infrastructure through monitoring, testing, and continuous deployment practices. Qualifications 5+ years of professional experience in a Backend Engineering, API Development, or Data Engineering role working with high-scale, read-heavy data systems. Required Skills Strong proficiency in backend development in a modern programming language (e.g., Python, Go, Node.js, Ruby, Java, etc.) and deep experience building and consuming robust APIs. Expert-level proficiency in PostgreSQL: Strong practical experience with schema design, query optimization, and performance tuning (e.g., analyzing query plans, configuration tuning, index management). Solid understanding of data warehousing concepts: Familiarity with the principles of transforming highly normalized Online Transaction Processing (OLTP) data into efficient analytical/Online Analytical Processing (OLAP) models. Experience with data ingestion: Practical experience building and managing data ingestion pipelines and ETL/ELT processes in a modern cloud environment. Effective Communication: Clear, articulate, and collaborative communication style, comfortable working with engineering and product stakeholders. Preferred Skills Familiarity with Elixir/Phoenix for API development. Prior professional experience working with data systems in sports analytics, betting, or digital media. Experience operating within a globally distributed team spanning the US and UK time zones.