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Shield AI

Senior Staff Software Engineer, Perception (R4985)

Shield AI is hiring a Senior Staff Software Engineer, Perception (R4985) in Washington DC, United States. It pays $234k-$351k a year and Level rates it ; you can apply on Shield AI.

Level's read on this job

  • Level rates this software engineer role Builds AI ●●●●: the job is building AI systems.
  • The listed pay starts 40% above the typical software engineer posting on Level.
  • Tools and skills the posting cites include PyTorch, TensorFlow and fine tuning.
  • Level classifies it as a lead role.
  • Level lists 657 other open roles at Shield AI.

AI in this role

PyTorchTensorFlow
fine-tuningai-evaluationcomputer-visionai-research
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.
The Hivemind Solutions Perception team develops the next generation of perception capabilities for autonomous systems by combining state-of-the-art machine learning with the proven foundations of computer vision. The team advances how autonomous platforms understand and interpret the world by developing vision, vision-language (VLM), and vision-language-action (VLA) models that tackle core perception challenges such as object understanding, scene interpretation, and mission-relevant environmental awareness. Working at the intersection of research and production, our engineers build the data pipelines, supervised fine-tuning (SFT) workflows, evaluation frameworks, and deployment infrastructure needed to transform cutting-edge AI research into reliable, mission-ready perception capabilities. In this role, you'll develop and deploy advanced machine learning models that enable autonomous systems to better understand, reason about, and interact with their environment. You'll partner closely with machine learning researchers, autonomy engineers, perception engineers, and platform teams to translate emerging AI capabilities into reliable, production-ready systems for U.S. and international defense customers. This is an ideal opportunity for engineers who enjoy building state-of-the-art AI systems while solving the practical challenges of deploying them on operational autonomous platforms.

What You'll Do:

    Model Development – Design, train, fine-tune, and maintain state-of-the-art vision, vision-language, and vision-language-action models that improve perception and decision-making for autonomous systems.

    Data Pipelines & Model Training – Build scalable data pipelines, supervised fine-tuning (SFT) workflows, and evaluation loops that continuously improve model performance on mission-relevant tasks.

    Model Deployment & Optimization – Deploy and optimize machine learning models for embedded hardware using technologies such as ONNX, TensorRT, and hardware-accelerated inference frameworks.

    Perception & Autonomy Applications – Apply modern machine learning techniques to solve challenging perception and autonomy problems across aerial and other autonomous systems operating in complex, real-world environments.

    Research-to-Production – Translate cutting-edge machine learning research into production-ready capabilities by balancing model performance, robustness, computational efficiency, and operational reliability.

    Cross-functional Collaboration – Partner closely with perception, autonomy, platform, and software engineering teams to integrate machine learning capabilities into mission-ready autonomous systems.

    Model Evaluation & Validation – Develop benchmarks, testing methodologies, and evaluation frameworks to measure model performance, identify failure modes, and guide future improvements.

    Continuous Improvement – Improve training infrastructure, developer tooling, deployment workflows, and model lifecycle management to accelerate experimentation and production delivery.

Required Qualifications:

    • Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 7 years with a PhD; or equivalent work experience.

    • Mastery of machine learning fundamentals.

    • Experience training an deploying ML models for computer vision in a production setting.

    • Strong understanding of 3D vision problems/algorithms.

    • Experience with machine learning frameworks such as PyTorch and TensorFlow.

    • Demonstrated expertise in deploying models using TensorRT and ONNX.

    • Proficiency in C++ and Python.

    • Strong analytical and problem-solving skills, with the ability to translate research into practical applications.

    • Ability to obtain a SECRET clearance

Preferred Qualifications:

    • Experience with developing autonomous systems for defense customers.

    • Experience with training/finetuning vision-language models, vision-language-action models, and/or world models.

    • Contributions to open-source projects in machine learning or computer vision.

    • Track record of publications in leading computer vision and robotics conferences and journals (e.g., CVPR, ICCV/ECCV, RAL, ICRA).

#LI-DS-1 #LE
Full-time regular employee offer package: Pay within range listed + Bonus + Benefits + Equity Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment) Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information. ### 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, color, 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

Senior Staff Software Engineer, Perception (R4985) at Shield AI rates 99 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.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningAI EvaluationComputer visionAI ResearchPyTorchTensorFlow

Also common in software engineer jobs at this level

Pay

This job starts at
$234k
Typical software engineer job
$167k

This posting starts 40% above the typical software engineer job. See software engineer jobs

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. How do you decide that one model's output is better than another's for a given task?
  3. Walk me through a computer vision problem you solved, from raw data to a deployed model.
  4. Tell me about a research question you investigated. What did you find?
  5. Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, AI Evaluation, Computer vision, AI Research, and PyTorch. 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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