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ModelOps Engineer
About The Position
Trax is looking for a proactive Applied ML Engineer to join our ModelOps Team. This role serves as the Technical Gatekeeper for our AI solutions, sitting at the vital crossroads of Research, Engineering, Product, and Data. You will ensure that our cutting-edge computer vision models are not just theoretically sound, but optimized for production and perfectly aligned with business needs.
The ideal candidate possesses a unique blend of system-wide vision and coding ability. You won't just monitor performance; you will define the standards for deployment, leverage internal ML infrastructure to solve complex gaps, and ensure our AI translates into flawless real-world performance. We are looking for an initiator who can bridge the gap between high-level research and production-grade execution.
About Trax
Trax’s mission is to enable brands and retailers to harness the power of digital technologies to produce the best shopping experiences imaginable. Trax’s retail platform allows customers to understand what is happening on shelf, in every store, all the time so they can focus on what they do best – delighting shoppers. Many of the world’s top CPG companies and retailers use Trax’s dynamic merchandising, in-store execution, shopper engagement, market measurement, analytics, and shelf monitoring solutions at scale to drive positive shopper experiences and unlock revenue opportunities at all points of sale. As pioneers in computer vision, Trax continues to lead the industry in innovation and excellence through development of advanced technologies and autonomous data collection methods. Trax is a global company with hubs in the United States, Singapore and Israel, serving customers in more than 90 countries worldwide.
Requirements
Responsibilities:
- Define Deployment Standards: Determine the criteria for model success, ensuring every release meets the high bar for both technical precision and business logic.
- Drive Cross-Functional Orchestration: Serve as the primary technical interface between Research, Product, Engineering, and Data teams.
- Optimize the System: Use your coding ability and Trax’s internal ML infrastructure to creatively improve the "Recognition Factory."
- Champion Performance: Proactively identify production performance gaps and improve model behavior to better serve specific client use cases.
- Evolve Infrastructure: Leverage and improve internal tools to automate the model lifecycle and ensure data integrity at scale.
Requirements:
- Professional Experience: At least 3 years of experience in a relevant technical environment (e.g., Data, Research, or Engineering units).
- System-Wide Perspective: Ability to understand complex architectures and how model changes impact the entire solution and the end-user experience.
- Proactive Ownership: A natural initiator who identifies gaps before they become issues and proposes creative solutions to close them.
- Technical Foundation: BSc in Computer Science, Mathematics, or Engineering (MSc is an advantage).
- Coding Ability: Good programming skills (Python is preferred) with the ability to automate analytical processes and interface with internal ML infrastructure.
- Analytical Skills: Strong experience in model analysis and deep learning concepts. You should be comfortable driving value and identifying patterns within the data.
- Experience in System Engineering: Previous experience in system engineering or managing complex technical workflows is a strong advantage.
- Data Proficiency: Familiarity with MySQL, BigQuery, MongoDB, or PostgreSQL.
- Communication: Excellent ability to communicate technical ML concepts to diverse stakeholders across the business.
- Production Mindset: Enthusiastic about working in a high-growth, fast-paced production environment.