Shield AIStaff Engineer, Hivemind Reasoning (R6292)
Shield AI is hiring a Staff Engineer, Hivemind Reasoning (R6292) in San Mateo, United States. It pays $240k-$360k a year and Level rates it ; you can apply on Level.
AI in this role
Job Description:
Help shape the future of physical AI in defense by building reasoning models for unmanned autonomous systems across aerial, maritime, land, and space domains.
We’re looking for a deeply technical engineer who can turn emerging research into reliable autonomous capabilities and shares our conviction that AI can protect service members and civilians. As a Staff Engineer on the Hivemind Reasoning team, you’ll drive the design, training, adaptation, and evaluation of reasoning and cognition models intended for deployment on unmanned platforms.
You’ll take hands-on ownership of complex technical problems, from defining model architectures and training objectives to establishing rigorous evidence of mission effectiveness. Your work will support individual vehicles and coordinated teams of hundreds of agents, while accounting for limited compute, intermittent communications, incomplete information, and other constraints of operating at the edge in contested environments.
You’ll partner closely with teams in perception, MLOps, large-scale simulation, autonomy, and inference to deliver capabilities as part of Hivemind Enterprise.
How we rate this
Staff Engineer, Hivemind Reasoning (R6292) at Shield AI rates 85 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.
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. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- 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: ML Ops. 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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