Engineer II, Deep Learning (R5178)

Shield AI is hiring an Engineer II, Deep Learning (R5178) in Melbourne, Australia. Level rates it ; you can apply on Shield AI.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include PyTorch, TensorFlow and computer vision.
Level classifies it as a mid-level role.
Level lists 663 other open roles at Shield AI.

AI in this role

pytorchtensorflow

computer-vision

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

Job Description:

Join Our Team: Shape the Future of Perception Technology ! 🚀

Are you ready to revolutionize the world of perception capabilities for both autonomous and non-autonomous platforms?

At the forefront of innovation, we are pushing the boundaries of what’s possible, turning cutting-edge insights into real-time, deep-learning-based solutions to solve practical perception challenges on the edge. Your skills will play a key role in driving transformative solutions that redefine the future.

Be part of a dynamic team where innovation meets impact. Let’s shape the future together!

What you'll do:

Research, design and implement state-of-the-art perception capabilities, taking ideas from conception into world-class field solutions
Work with and deploy our AI stack to edge devices
Work in collaboration with the other deep learning engineers to architect and develop tools help to scale up our deep learning operations
Stay abreast with the literature and actively involve in various R&D project(s)

Required qualifications:

Demonstrable experience in delivering deep-learning-based solutions to solve computer vision problems with industry-based experience between < 1 year
Strong understanding of using convolutional neural networks and/or transformers for object classification, recognition or segmentation
Experience with implementing novel deep learning network architectures using existing frameworks (TensorFlow, Caffe, PyTorch or similar)
Relevant tertiary qualifications (Bachelors/Master/PhD in Computer Science or related fields)

Help us redefine what’s possible in AI-driven perception — apply today! Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.

How we rate this

Engineer II, Deep Learning (R5178) at Shield AI rates 89 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Engineer II, Deep Learning (R5178) is an open job at Shield AI.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Melbourne.
The job was posted on June 11, 2026.
Seniority: mid.
Applications are handled by Shield AI. Apply on Shield AI

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Computer visionPyTorchTensorFlow

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of TensorFlow that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer vision, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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