Software Engineer, Core Technology

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.

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.

• 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

• 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

• 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

• 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

• 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

• 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 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

• 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