Staff Engineer, Software

Stryker Corporation; Gurugram District; Hybrid; INR 3,000,000 - 4,200,000; Full time

Job summary

Stryker Corporation in India is seeking an experienced engineer who blends AI/ML (computer vision) with native Android development. You will train, test, and tune vision-based models for live camera feeds and build a production Android app that communicates with our cloud backend.

You will optimize models for deployment constraints, partner with deployment and operations teams to maintain production systems, implement secure cloud integration (REST/gRPC, OAuth2/JWT), and set up Android CI

Qualifications

• 5+ years of 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 Android’s 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).

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.

Skills

AI/ML

Computer Vision

Android Development

Kotlin

Jetpack Compose

MVVM

Hilt

REST/gRPC

OAuth2/JWT

PyTorch

TensorFlow

Education

Bachelor's or Master's in CS/AI

Tools

AWS SageMaker

GCP Vertex AI

Azure ML

Android CI pipelines

Job description

Work Flexibility: Hybrid or Onsite 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 Android’s 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

• Bachelor’s or Master’s 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.

Travel Percentage: 10%

Stryker is one of the world’s leading medical technology companies and, together with its customers, is driven to make healthcare better. We offer innovative products and services in Orthopaedics, Medical and Surgical, and Neurotechnology and Spine that help improve patient and hospital outcomes. We are proud to be named one of the World’s Best Workplaces!

Work Flexibility Remote – Role allows you to work the majority to 100% of time from an alternate workplace. These roles could have travel expectations, and you must work within the country of the job requisition location.

Field-based – You can expect to regularly work a majority to 100% of time at customer facilities and has a set territory or expectation to travel within a set boundary. Almost all sales roles would likely be qualified as field-based.

Onsite – Role is 100% located at a Stryker facility. Some ad hoc flexibility may be available depending on role, level, and job requirements. Manufacturing roles and any role that requires physical presence at the office would qualify under this category.

Hybrid – You can expect to regularly work in both an alternate workplace and a Stryker facility. Roles that are partially remote or co-located would qualify as hybrid, and the expectation to be onsite would be defined and agreed upon by your manager/supervisor.