AI Engineer

Requirements

• Software Engineering: Production-level mastery of Python alongside working knowledge of C++ or Swift, emphasizing clean code, modular design, and execution speed.

• Computer Vision & 3D Spatial Processing: Hands-on experience with OpenCV, Open3D, or PCL (Point Cloud Library) for point cloud filtering, spatial segmentation, feature extraction, and 2D/3D coordinate transformations.

• ML & Deep Learning Frameworks: Deep experience with PyTorch or TensorFlow, alongside

proficiency in Scikit-learn for traditional machine learning and statistical data analysis.

• High-Throughput Cloud Serving: Proven experience building low-latency serving infrastructur using NVIDIA Triton Inference Server and managing end-to-end model workflows on AWS SageMaker.

• Model Optimization & Acceleration: Familiarity with model quantization, pruning, and target

compilers (e.g., ONNX Runtime, TensorRT) to hit production latency targets.

• Applied AI & Domain Math: Solid foundation in linear algebra, 3D geometry, coordinate

systems, multi-sensor fusion, and awareness of modern LLM/multimodal applications.

Preferred Technical Qualifications

• Wireless Domain Knowledge: Basic understanding of RF environment simulation, indoor spatial coverage modeling, or wireless network planning principles.

• Mobile Edge Integration: Experience optimizing or running vision models on iOS devices

(CoreML, ARKit, Metal).

Compensation Range

Ookla provides a range for the base pay. Factors that may be used to determine your actual pay may include your specific job related knowledge, skills, experience, and geographic location. The salary compensation for this role is x - x. Individual pay within the compensation range for this business unit specific role is determined based on a variety of factors including experience, scope of the role, capabilities to perform the role, education and training, as well as business and company performance.

The Opportunity

We are looking for an AI Engineer to join our Ekahau team. Ekahau enables IT professionals to take control of their Wi-Fi, making it easier than ever before to proactively monitor, maintain, and optimize their networks. Organizations of every size-including the world's biggest brands and events-use our software and hardware products for full Wi-Fi lifecycle management and the highest levels of performance and connectivity. Our award-winning design, site survey, and troubleshooting solutions create fast, reliable networks that businesses can trust for their mission-critical Wi-Fi needs.

In this role, you will work as part of our research team, exploring and pioneering innovative technologies and methodologies related to wireless communication systems planning, optimization, spatial mapping, and network troubleshooting. You will bridge cutting-edge spatial AI (LiDAR/camera floorplan extraction and 3D point cloud analysis) and modern AI, ML, and LLM approaches with high-performance edge and cloud infrastructure (AWS SageMaker, NVIDIA Triton). By conducting experiments, building proof-of-concepts, and translating theoretical concepts into production-grade systems, you will directly shape how indoor environments are captured, analyzed, and optimized for global connectivity.

Expectations for Success

• 3D Spatial & Computer Vision Engineering: Design, build, and maintain real-time 3D spatial

processing pipelines, leveraging sensor fusion (LiDAR, camera feeds, spatial telemetry) for point cloud filtering, segmentation, and 3D layout analysis.

• Computer Vision & Layout Detection: Develop computer vision models and downstream post-processing algorithms to extract structural features, recognize building geometry, and generate precise 2D/3D floorplans from raw visual and spatial data.

• Research to Production: Translate research findings, algorithmic prototypes, and modern

AI/ML/LLM concepts into high-performance, maintainable production code in Python, taking

direct ownership of core product implementations.

• Scalable Cloud Inference Architecture: Architect, deploy, and manage multi-model inference

pipelines on AWS SageMaker and NVIDIA Triton Inference Server, ensuring low-latency

processing and reliable high-throughput serving.

• MLOps, Data Engineering & System Observability: Build end-to-end data and MLOps

pipelines-encompassing synthetic data generation, active annotation, dataset versioning,

continuous integration/deployment (CI/CD), and real-time telemetry-to continuously evaluate, deploy, and monitor model performance, latency, and spatial accuracy.

• Cross-Functional Technical Collaboration: Work directly alongside software engineering,

research, and product management teams to transition prototype features into scalable,

market-ready releases.