Senior Engineer Human Machine Teaming
Shield AI is a U.S. defense-technology company developing mission-autonomy software and autonomous aircraft for military and allied operations.
Funding history
Investors
About Shield AI
Founded in 2015, Shield AI builds Hivemind autonomy software and V-BAT and X-BAT aircraft for operations in contested, GPS- and communications-denied environments. Its current site also presents Aechelon synthetic-reality simulation and Vision Systems detection and tracking products.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design and run human-machine teaming experiments, assess collaboration under varied and degraded conditions, and analyze operator and system data. You will develop performance measures and research protocols, translate findings into design recommendations and feature requests, contribute evidence to assurance and risk assessments, and present results to engineering and customer audiences. You will also travel to test, demonstration, and customer locations as needed.
Requirements
- Bachelor's degree in human factors engineering, psychology, cognitive systems engineering, applied cognitive science, industrial and systems engineering, or a related field
- 3–5 years of related experience with a Bachelor's degree, 2–4 years with a Master's degree, or 2 years with a PhD for the Senior Engineer level
- Knowledge of human-machine teaming, human factors, and human performance theory and measurement
- Knowledge of cognitive task analysis and knowledge elicitation
- Experience with experimental design, human-participant research, and multivariate statistical analysis
- Proficiency with SPSS, R, or Python
- Experience working in multidisciplinary and ambiguous problem spaces
- Written and verbal communication skills
- Eligibility for a U.S. DoD Secret clearance and ability to obtain and maintain higher-level access
- U.S. citizenship
Responsibilities
- Design and run human-machine teaming experiments
- Conduct robustness and resilience testing
- Characterize context-sensitive collaboration requirements
- Extend operator-in-the-loop and live-virtual-constructive testing
- Define performance measures and assessment batteries
- Prepare research protocols and analyze human-machine system data
- Translate findings into requirements revisions and feature requests
- Contribute to maturity, risk, and assurance assessments
- Report results to engineering and customer audiences
- Travel to project, test, demonstration, and customer locations
Benefits
- Bonus
- Benefits
- Equity