Senior Embedded Vision Engineer - Real-Time AI Pipelines

Luxoft

Poland

On-site

PLN 220,000 - 340,000

Full time

2 days ago

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Benefits offered by this job

Private Medical & Dental care & Life보험

Internal Mobility program - rotation

Job summary

Luxoft in Poland seeks a hands-on technical leader to design, optimize, and productize high-performance computer vision and AI pipelines for consumer devices. You will ensure real-time performance, power efficiency and production robustness across embedded platforms.

Collaborate with ISP, sensor, and tuning teams to optimize image quality for downstream AI/UX performance. Lead ML model deployment on edge devices and drive production-grade validation and performance profiling.

Qualifications

Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field.
7-10+ years of experience in computer vision/image processing.
Proven experience shipping at least one consumer product with embedded vision/AI.
Strong experience with OpenCV and ML frameworks (PyTorch, TensorFlow, ONNX).
Experience deploying ML models on embedded/edge devices.
Experience with model optimization (quantization, pruning).
Strong understanding of 2D/3D geometry and linear algebra.
Experience working on embedded Linux or Android systems.
Strong debugging and performance profiling skills.
Experience optimizing for power and thermal constraints.

Responsibilities

Design and implement advanced computer vision and image processing pipelines optimized for real-time consumer devices.
Collaborate with ISP, sensor, and tuning teams to optimize image quality for downstream AI and UX performance.
Develop and deploy ML models for visual recognition, enhancement, tracking, or scene understanding.
Optimize ML models for edge deployment (quantization, pruning).
Implement performance-critical algorithms in modern C++ for embedded platforms.
Optimize for latency, power consumption, memory footprint, and thermal constraints.
Work closely with Android/Linux platform teams to integrate camera and AI pipelines.
Define and track KPIs: FPS, power usage, memory, startup time, and accuracy.
Profile and optimize performance across CPU/GPU/NPU/DSP.
Drive debugging of complex system-level issues in production builds.
Ensure robust unit testing and contribute to automated validation pipelines.
Mentor engineers and review architecture/design proposals.
Support product bring-up and mass production readiness.