Research Engineer - Applied Research & Technology

Acuity Inc•Boulder, CO

1d•$93,000 - $167,400•Hybrid

About The Position

As a Research Engineer on the Applied Research & Technology team at Acuity Intelligent Spaces, you will work at the intersection of applied machine learning research, software engineering, and spatial intelligent environments. You will architect, prototype, and deploy intelligent software components that make buildings and room environments responsive and context-aware. The role focuses on productionizing machine learning and computer vision models and integrating them with AV control infrastructure, including QSC/Q-SYS ecosystems, Distech Controls, and Atrius IoT platforms, to support building intelligence and smart room automation. The position requires on-site presence in Boulder, Colorado, and follows a hybrid work model. You will collaborate with hardware, firmware, software platform, and product management teams to turn research concepts into commercial solutions. The posted salary range is $93,000 to $167,400 USD; placement within the range may vary with experience and geographic location.

Requirements

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent practical experience.
At least 3 years of software development experience.
Strong expertise in Python and solid object-oriented design principles.
Experience developing, fine-tuning, and deploying machine learning models using frameworks such as PyTorch, TensorFlow, ONNX, or TensorRT for edge or real-time applications.
Practical understanding of computer vision fundamentals, image processing, object detection, or spatial sensing architectures such as OpenCV or spatial tracking.
Familiarity with AV/C technologies, particularly QSC/Q-SYS core architectures, Lua scripting, or control API integrations.
Experience with REST APIs, gRPC, MQTT, WebSockets, and network communication protocols used in AV and IoT devices.

Nice To Haves

Experience with multimodal AI models, speech processing, or audio analytics.
Familiarity with Docker, Kubernetes, edge deployment frameworks, or microservices architecture.
Experience integrating smart building protocols such as BACnet or Modbus, or commercial IoT frameworks such as Atrius or Distech Controls.
Exposure to sensor fusion, real-time spatial analytics, or speaker-tracking visual integrations.

Responsibilities

Design, build, test, and deploy efficient machine learning pipelines and models for real-time inference in edge and cloud-connected spatial environments.
Implement computer vision applications, such as occupancy analytics, spatial perception, and multimodal signal processing, to improve physical-space context and automated room control.
Develop software integrations connecting AI/ML pipelines with AV ecosystems, including QSC/Q-SYS control, audio signal flow, and smart room AV infrastructure.
Write clean, maintainable, modular, production-grade code for edge devices, middleware microservices, and API integrations.
Build end-to-end functional prototypes and proof-of-concepts, validate spatial intelligence algorithms, and transition them into scalable platform services.
Collaborate with hardware, firmware, software platform, and product management teams to translate research concepts into commercial solutions.