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Software Engineer, Core Technology
Инновационный продукт на стыке AI и гейминга, возможность удаленной работы и работа с современным стеком (PyTorch, C++). Компания имеет признание на международном уровне (TIME, Fast Company), что делает опыт здесь очень ценным.
Сложность вакансии
Роль требует глубоких знаний в специфических областях: либо в инфраструктуре машинного обучения (MLOps, пайплайны), либо в системном программировании (C++, реальное время). Высокая планка инженерной культуры и работа в распределенной команде добавляют сложности.
Анализ зарплаты
Предлагаемая позиция соответствует уровню Middle/Senior в международном стартапе. Рыночные оценки для Гонконга и удаленных ролей такого уровня в сфере ML/Systems Engineering обычно находятся в диапазоне $80,000 - $130,000 в год.
Сопроводительное письмо
I am writing to express my strong interest in the Software Engineer position at Nex. With a solid background in building high-performance systems and a deep passion for the intersection of machine learning and real-time interaction, I am excited by Nex's mission to redefine family entertainment through motion-based play.
In my previous roles, I have focused on developing robust technical foundations, whether through optimizing ML training pipelines or engineering cross-platform frameworks. I am particularly drawn to Nex's dual-track approach, as it aligns with my experience in creating scalable infrastructure that bridges the gap between research and production. I am eager to bring my expertise in Python and C++ to a team that values technical depth and innovation.
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Откликнитесь в nex уже сейчас
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Описание вакансии
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.
Location: Hong Kong or Remote
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. You will focus on either ML Engineering or Framework Engineering, two complementary specializations that drive research velocity and product performance.
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.
In the Framework Engineering track, you will own the design and development of our cross-platform software framework for detection, sensing, haptics, and ML inference. You will build the real-time systems, hardware abstraction layers, and developer APIs that enable multimodal interaction on Nex Playground.
Either path 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
ML Engineering Track
- 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
Framework Engineering Track
- Design and develop a robust, performant, cross-platform framework for sensing, detection, haptics, and ML inference
- Build efficient runtime execution systems and hardware abstraction layers
- Develop developer APIs that make complex sensing and inference accessible
- Optimize for real-time performance on multimodal data streams
- Work on camera, microphone, and haptics integration
- Implement code that runs reliably across different platforms and hardware configurations
- Improve framework stability, performance, and developer experience
Both Tracks
- 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
ML Engineering Track
- 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
Framework Engineering Track
- 3+ years of professional software engineering experience in building real-time software frameworks, cross-platform systems, or performance-critical applications
- Proficiency in C++/C#, additional experience with at least one other systems language (C++, C#, Java, Rust, or Go)
- Experience with cross-platform development
- Proven track record with performance optimization, real-time systems, or hardware abstraction layers
- Experience building developer APIs or software frameworks used by other engineers
Nice To Have
For ML Engineering Track
- 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
For Framework Engineering Track
- Experience building real-time systems or low-latency software
- Familiarity with computer vision, signal processing, or embedded systems
- Experience with cross-platform development or hardware abstraction
- Knowledge of performance optimization and profiling
- Experience with sensing technologies (cameras, microphones, or similar)
- Familiarity with CI/CD pipelines and automated testing
For Either Track
- 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 nex.inc/who-we-are.
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.
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Навыки
- Python
- C++
- PyTorch
- TensorFlow
- MLOps
- Docker
- Computer Vision
- Signal Processing
- Embedded Systems
- CI/CD
- Distributed Systems
Возможные вопросы на собеседовании
Проверка опыта работы с высоконагруженными данными для ML.
Расскажите о самом сложном пайплайне данных, который вы проектировали: как вы обеспечивали его масштабируемость и надежность?
Важно для трека Framework Engineering, где критична производительность.
Как бы вы подошли к оптимизации задержек (latency) в системе обработки видеопотока в реальном времени?
Оценка навыков интеграции моделей в продукт.
С какими основными трудностями вы сталкивались при переносе ML-моделей из среды исследования (research) в продакшн?
Проверка системного мышления и понимания кросс-платформенности.
Опишите ваш опыт разработки API или фреймворков, которыми пользовались другие разработчики. Как вы обеспечивали обратную совместимость?
Оценка соответствия культуре стартапа и самостоятельности.
Как вы расставляете приоритеты, работая в небольшой команде над несколькими критически важными задачами одновременно?
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