Snaga Solutions

We are looking for a Trainee Embedded Vision Integration Engineer to join our sensor integration team and work alongside a Senior Engineer on precision turret systems for defense applications.

This is a hands-on technical role — not an internship with slides and reports. From day one you will assist with real hardware bring-up, camera integration, pipeline testing, and calibration tasks. You will learn by doing: reading real device datasheets, running GStreamer pipelines on NVIDIA Jetson, and debugging sensor output under actual field constraints.

If you have a solid foundation in C++ and Linux, genuine curiosity about embedded systems and computer vision, and want to grow fast in a defense-critical environment — this role is for you.

What We Offer

• Direct involvement in real defense projects for Ukraine — no toy tasks, no simulated environments.
• Mentorship from a Senior Embedded Vision Integration Engineer with hands-on daily guidance.
• Fast growth path: structured onboarding, real ownership of subtasks from month one.
• Exposure to the full embedded stack: hardware, drivers, pipelines, and system integration.
• Competitive trainee compensation with clear performance-based review milestones.
• Temporary military service exemption for the duration of work on defense-critical projects.

Requirements

Technical Background — Required

• Degree in progress or completed in Computer Science, Electrical Engineering, Physics, or similar.
• Working knowledge of C++ (C++11 minimum); comfortable writing and reading real code, not just tutorials.
• Linux command-line confidence: navigating the filesystem, running processes, reading logs, using ssh.
• Python scripting for basic automation and data processing tasks.
• Understanding of what a camera pipeline is — even at a conceptual level (capture → process → output).
• Ability to read technical documentation in English: datasheets, API references, driver guides.
• Readiness to work in a fast-paced, hardware-in-the-loop environment with real deadlines.

Will Be a Plus

• Any hands-on experience with cameras: USB webcam, CSI camera, IP camera — even in a hobby or university project.
• Familiarity with OpenCV — even basic image processing (read, resize, threshold).
• Exposure to GStreamer, V4L2, or RTSP streams — any level of familiarity.
• Experience with NVIDIA Jetson, Raspberry Pi, or any ARM-based Linux board.
• Basic understanding of computer vision concepts: image formation, color spaces, resolution vs. FOV.
• ROS or ROS2 exposure — even from a course or personal project.
• Any experience with sensor data: reading output from a rangefinder, IMU, or GPS module.
• Participation in robotics clubs, hackathons, or embedded systems competitions.

Responsibilities

• Assist the Senior Engineer with camera and sensor bring-up: connecting hardware, checking output, validating signal integrity.
• Location: our hardware lab and team are based in Kharkiv. We expect on-site presence — relocation support can be discussed.
• Run and modify GStreamer pipelines under supervision — test latency, diagnose frame drops, validate stream parameters.
• Support camera calibration workflows: capture calibration frames, run calibration scripts, verify results.
• Write and maintain test scripts in Python to automate sensor validation and regression checks.
• Document integration steps, hardware configurations, and test results in Confluence.
• Assist with sensor-to-sensor alignment tasks — follow established procedures and report anomalies.
• Support field testing preparation: pack and configure hardware, validate pipelines before deployment.
• Reproduce and isolate bugs reported from field tests — provide structured debug logs to the Senior Engineer.
• Research component datasheets, driver documentation, and integration notes as assigned.
• Gradually take ownership of defined subtasks as skills and context grow.

What We Expect From You

• Intellectual honesty: say what you know, flag what you don't, ask early rather than late.
• Structured thinking: when you hit a problem, describe what you tried, what you observed, and what you think the cause is.
• Ownership mindset: a trainee who asks "is there anything else I can do?" beats one who waits for the next task.
• Consistency over brilliance: we value engineers who show up, deliver, and communicate — every day.

We are looking for a Senior Embedded Vision Integration Engineer to build and integrate sensor pipelines for precision turret systems operating under strict real-time and hardware constraints.

You will be responsible for connecting and synchronizing cameras, rangefinders, and optical sensors into a cohesive embedded platform — ensuring reliable, low-latency data delivery to targeting and control systems. This role is ideal for someone who owns the full integration layer: from hardware bring-up and driver configuration to pipeline architecture and system-level testing. You will work directly on defense projects for Ukraine, seeing a short path from implementation to field testing.

What We Offer

• Direct contribution to defense projects for Ukraine.
• Collaboration with top experts in vision hardware, robotics, and embedded systems.
• Ownership of key architectural and integration decisions.
• Competitive compensation based on experience.
• Temporary military service exemption for work on defense-critical projects.

Requirements

Core Technical Skills — Required

• 5–8 years of commercial embedded software development.
• Degree in Computer Science, Electrical Engineering, Physics, or similar.
• Strong Linux embedded development experience (NVIDIA Jetson, ARM SoCs).
• Solid C++14/17/20; Python for tooling and scripting.
• Hands-on experience with camera integration: MIPI CSI, USB3, GigE Vision, RTSP streams.
• Low-latency video pipeline development: GStreamer, V4L2, DeepStream.
• Experience integrating rangefinders (laser, stereo-based) and reading/fusing sensor output at the system level.
• Understanding of optical systems: FOV characteristics, wide-angle vs. narrow-angle lenses, zoom control interfaces.
• Experience with hardware bring-up, device trees, kernel modules, or BSP customization.
• Solid understanding of real-time constraints, latency budgeting, and system timing.
• Ability to work independently with minimal supervision.

Will Be a Plus

• Experience with thermal/IR cameras and night-vision imaging.
• PTZ or stabilized gimbal integration.
• IMU integration and sensor fusion basics.
• ROS/ROS2 experience.
• Multi-sensor time synchronization (PPS, hardware triggers, PTP).
• MLOps or model deployment pipelines on edge devices.
• Mission-critical or safety-critical systems experience.
• Remote telemetry or monitoring tooling.

Responsibilities

• Integrate and configure EO/IR cameras, laser rangefinders, and optical sensors into the turret platform.
• Build and maintain low-latency video pipelines supporting wide-angle (search/acquisition) and narrow-angle (tracking/engagement) channels.
• Implement FOV switching logic, inter-channel synchronization, and sensor alignment.
• Interface with rangefinder hardware and expose distance data to the targeting subsystem.
• Perform camera calibration and sensor-to-sensor geometric alignment.
• Collaborate with the CV team to ensure clean, well-timed data delivery to detection and tracking modules.
• Integrate sensor outputs with turret control and stabilization systems.
• Participate in hardware selection and system architecture decisions.
• Ensure robustness and reliability of deployed sensor systems under field conditions.
• Support field testing, debugging, and system maintenance.

We are looking for a Technical Project Manager who is a genuine bridge between our software and hardware teams — not just a coordinator, but someone who understands both worlds well enough to add value in each direction. You will translate field test results into engineering tasks, help the Lead Architect research and validate solutions, and champion AI tooling across the team.
 

What We Offer

• A central role in mission-critical autonomous systems development for national defense.
• Direct collaboration with top-tier CV, embedded, and robotics engineers.
• Significant influence on technical direction and team culture.
• Freedom to explore and introduce AI tooling across the team's workflows.
• Competitive compensation aligned with expertise.
• Temporary exemption from military service for the duration of work on defense-critical projects.

Requirements

     Technical Background — Required

• 4+ years in software engineering; (CV/ML, technical lead, or architect role)
• Solid understanding of CV pipelines: detection, tracking, SLAM/visual odometry — enough to evaluate accuracy and latency trade-offs from field results.

• Python proficiency; C++ reading-level minimum.

   

  Will be a Plus

• Hands-on experience with ONNX, TensorRT, or quantization workflows.
• Experience with deep learning frameworks (PyTorch or TensorFlow) and model optimization workflows.
• Working knowledge of ROS — nodes, topics, services, inter-module communication.
• Familiarity with edge platforms (NVIDIA Jetson or similar) and real-time constraints.
• Familiarity with autopilot ecosystems (ArduPilot, Betaflight) — enough to read architecture docs and contribute to technical decisions.
• Sensor fusion experience (Camera + IMU + GPS).

Project & Communication Skills

• Proven ability to manage complex technical projects across multi-disciplinary teams.
• Able to take polygon/field test results and convert them into clear, prioritized engineering tasks.
• Comfortable working directly with a Lead Architect — receiving high-level requirements, researching solutions, presenting structured proposals.
• Proficient with Jira and Confluence — task tracking, sprint management, structured documentation.
• Strong written and verbal communication; comfortable leading design reviews and justifying trade-offs.

AI Tools & Mindset

• Active daily user of AI tools (Claude, Cursor, Copilot, or similar).
• Genuine drive to become an internal AI champion — introduce tools, identify new use cases, raise the bar for the whole team.

Responsibilities

• Own end-to-end delivery across multiple workstreams (autonomous drone, turret control, and future platforms): planning, tracking, risk management, reporting.
• Primary technical liaison between software (CV/ML, ROS) and hardware/embedded teams — translating requirements, surfacing blockers, resolving misalignments.
• Attend polygon and field tests; translate results — including HIL/SIL outcomes — into structured engineering feedback and task descriptions.
• Work with the Lead Architect to research new approaches; prepare structured proposals for architectural decisions.
• Coordinate module integration (optical guidance, servo control, ROS layers) — interfaces, latency requirements, milestones.
• Maintain project documentation: requirements, test reports, decision logs, vendor deliverable tracking.
• Introduce and evangelize AI tooling across engineering workflows.

Nice to Have

• Hands-on experience with autonomous drones, FPV, or UAS platforms.
• ArduPilot or Betaflight ecosystem experience.
• MLOps or model deployment pipeline experience.
• Experience managing vendor/partner relationships — RFP processes, deliverable tracking.
• Systems thinker — holds the full picture while diving into details when needed.
• High ownership; comfortable with ambiguity and fast-moving field environments.
• Surfaces problems early with proposed solutions, not just issues.