Nex is on a mission to help families rediscover the joy of movement. Created by parents for parents, Nex combines technology and play to deliver fun, social, and interactive experiences powered by natural body motion, encouraging kids and adults to move more, play more, and have fun together. Nex Playground, the company’s award-winning active play system, is purpose-built to get families moving year-round, with safety and privacy as core considerations in its intentional design. It is certified kidSAFE+ COPPA compliant and built to support healthy, active play for all ages and abilities. Nex Playground features a growing library of 50+ experiences, including motion and dance games, fitness and educational experiences, and Nex Originals. Content includes collaborations with partners like Hasbro, Sesame Workshop, and NBCUniversal. Nex has been recognized by Fast Company’s Most Innovative Companies, TIME’s Best Inventions, and Parents’ Best Entertainment System for Families, and has earned Red Dot, IDEA, and Core77 international design awards. We encourage you to explore Have Fun and Is Motion Gaming Back?, as they offer a deeper look into our culture, values, and explain how our approach to motion gaming differs from previous generations. Important Notice: ⚠️ To protect yourself from recruitment scams, please note that Nex communicates through official company emails ending in @nex.inc. If you receive a suspicious email or message, please do not respond and report it immediately to us. Location: Remote, or Hong Kong based Type: Full Time The Role As a Software Engineer at Nex, you will contribute to building the technical foundations that power our platform's most demanding capabilities. In the ML Engineering track, you will build the infrastructure that accelerates machine learning research: training pipelines, data workflows, model integration systems, and the tools that enable rapid experimentation. Your work ensures researchers can iterate reliably and move experiments toward production readiness. The role offers the opportunity to work on deeply technical problems in machine learning systems, data infrastructure, sensing technologies, and real-time inference. You will be part of a small, highly technical team that values both specialization and collaboration, with clear ownership of core technology areas. The Mindset You are drawn to solving complex technical challenges at the intersection of research and production engineering. You care deeply about building systems that are reliable, performant, and maintainable. You thrive in environments where technical depth matters, where your expertise in ML systems, distributed computing, or real-time software directly shapes what the platform can do. What You’ll Do - Design and build training pipelines, data workflows, and model integration systems - Develop infrastructure that accelerates research iteration and reduces turnaround time - Build systems for data collection, curation, and preprocessing at scale - Create tools and automation that move experiments toward production readiness - Optimize data pipelines for reliability, performance, and observability - Collaborate with ML researchers to understand their needs and remove technical blockers - Work on model serving infrastructure and integration with the production framework - Write clean, well-tested code that maintains high engineering standards - Participate in code reviews and help raise the engineering bar across the team - Contribute to shared tools, infrastructure, and cross-role projects (20% Time) - Work with the dual-leadership model (Engineering Manager and Tech Lead) to understand priorities and technical direction - Document systems and decisions to support team knowledge sharing Must Have - 3+ years of professional software engineering experience in building production ML systems, training infrastructure, or research platforms - Proficiency in Python, additional experience with at least one other systems language (C++, C#, Java, Rust, or Go) - Hands-on experience with PyTorch or TensorFlow in production or research environments - Experience building or maintaining ML training pipelines or data workflows - Familiarity with model deployment, inference optimization, or MLOps practices Nice To Have - Experience with distributed training systems or GPU-accelerated computing - Knowledge of data versioning, experiment tracking, or ML metadata management - Familiarity with containerization (Docker) and orchestration tools - Contributions to open-source ML projects or research publications - Experience working in small, high-performance technical teams - Background in startups, high-growth environments, or consumer product companies - Passion for pushing technical boundaries and deep problem-solving We Offer - Competitive compensation package. - Flexible working hours and vacation policy. - Product-driven culture that treasures talents and individual growth. - Front-row seat and hands-on experience with cutting edge technologies in the evolving gaming field Nex is located in San Jose, California, USA and Hong Kong. Learn more about us at https://www.nexplayground.com/about. We encourage applications even if you don’t meet more than 50% of the requirements — we believe that experience comes in many forms! Nex is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, gender identity, and sexual orientation), national origin, ancestry, age, physical or mental disability, medical condition, genetic information, marital status, military or veteran status, or any other characteristic protected by applicable law. This policy applies to all individuals at every stage of the employment relationship, including all current and prospective employees, and covers all employment decisions including recruitment, hiring, job assignment, promotion, compensation, benefits, training, discipline and termination. We are committed to providing a workplace free from discrimination and harassment and to fostering an inclusive environment for all employees.

Software Engineer, ML Engineering

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