Staff Engineer, Software

Job Description:

We are seeking an experienced engineer who brings two distinct skill sets: AI/ML (Computer Vision) - owning the training testing and tuning of vision-based models for live camera-feed monitoring; and Native Android Development - developing and shipping a production Android app that talks to our cloud. You will partner with the deployment team to align models with their scaling/runtime constraints work with the maintenance team to maintain and upgrade models.

Key Responsibilities

• Design train evaluate and tune computer vision models (detection classification segmentation tracking) for live video and multi-camera use cases including dataset curation training/validation/testing pipelines and rigorous benchmarking on accuracy latency and throughput.

• Optimize models (architecture quantization pruning distillation) to meet deployment and scaling constraints provided by the deployment team; re-tune or re-architect when those constraints change.

• Partner with the operations team to maintain and upgrade models in production triage regressions refresh on new data address drift and ship improved versions.

• Design build and ship a native Android application (Kotlin Jetpack Compose MVVM/Clean Architecture Hilt Coroutines/Flow Room WorkManager) that interacts with our cloud backend.

• Build secure cloud integration: REST/gRPC OAuth2/JWT TLS FCM push and offline-first sync; handle Android runtime permissions and background execution correctly.

• Set up Android CI testing (unit instrumentation) crash reporting and Play Store release pipelines.

Required Qualifications

• 5 years of total professional software engineering experience.

• Proven experience in vision-based model training and testing with models shipped to production.

• Hands-on experience with cloud-based AI training/experimentation infrastructure (AWS SageMaker GCP Vertex AI Azure ML or equivalent).

• Experience building models for continuous monitoring via live video input from cameras and tuning them for efficient inference across multiple concurrent camera streams.

• Track record of collaborating with deployment/MLOps/operations teams - translating runtime constraints into model decisions and supporting models post-launch.

• Proficiency in Python and ML frameworks (PyTorch TensorFlow TensorFlow Lite ONNX); strong grasp of CNNs and modern detection/tracking architectures (YOLO DETR ByteTrack etc.).

• Strong proficiency in Kotlin and the modern Android stack (Jetpack Compose Coroutines/Flow Hilt Room WorkManager); demonstrated experience shipping production Android apps to the Play Store.

• Deep working knowledge of Android Wi-Fi and BLE APIs.

• Experience with cloud connectivity on Android (REST gRPC WebSockets or MQTT; OAuth2/JWT; TLS; FCM) and Androids security/permissions model (runtime permissions foreground services background execution limits).

• Comfort interfacing with embedded/IoT hardware over BLE and WiFi under real-world conditions (intermittent connectivity retries power constraints).

Preferred Qualifications

• On-device inference acceleration (Android NNAPI Qualcomm SNPE MediaPipe GPU delegates).

• Streaming protocols (RTSP WebRTC HLS) and video codecs; edge / IoT camera deployments.

• Model monitoring drift detection and active learning loops.

• Contributions to open-source ML or Android projects.

Education

• Bachelors or Masters degree in Computer Science AI/ML or related field or equivalent practical experience.

measurement bias

• Strong product mindset and bias for measurement - you instrument benchmark and optimize.

• Excellent collaboration skills with deployment and operations teams and a maintenance mindset toward models in production.

• Ability to switch effectively between two distinct domains (ML model development and Android app development) and deliver in both.