Human Factors Engineer

As a Human Factors Engineer at Morse, your primary responsibilities include performing Human-Machine Teaming Test and Evaluation (HMT T&E) for Artificial Intelligence systems, leading user engagement and knowledge elicitation studies, and identifying human performance metrics to evaluate system effectiveness. You will engage with end users and stakeholders to understand user requirements and recommend best practices that enhance operational effectiveness.

To qualify for the Human Factors Engineer role at Morse, candidates must be US citizens and able to obtain a U.S. Security Clearance. A Master's or PhD in Cognitive Psychology, User Experience, or a related field is required, along with a minimum of 5 years of professional experience in human factors. Candidates should also have experience in Human-Machine Interaction and a background in Python programming.

At Morse, the work environment is collaborative and enjoyable. As a Human Factors Engineer, you will have the flexibility of a hybrid work model, allowing you to balance your professional commitments with personal life. Morse values its employees and offers a comprehensive benefits package to support you and your family.

Morse emphasizes professional development and provides opportunities for growth as a Human Factors Engineer. You'll work closely with data scientists, software developers, and analysts, and have the chance to lead small to mid-sized teams. There’s a strong focus on mentorship and continuous feedback to foster your skills in human performance assessment and AI design.

User engagement is critical in the Human Factors Engineer role at Morse. Engaging with end users and stakeholders allows you to gather valuable insights into their experiences, ensuring that AI solutions are designed with real-world applications in mind. This process helps identify user needs and drives iterative design improvements for the AI systems being developed.

In your response, focus on specific projects where you've engaged with end users to assess their interaction with systems. Share details about metrics used and outcomes achieved, emphasizing how your involvement improved user experience and system performance.

Discuss your methods for gathering feedback, such as surveys or usability testing sessions. Explain how you analyze this feedback, highlight the changes made to designs based on it, and demonstrate the positive impact this had on user satisfaction.

Mention specific software or programming languages, such as Python, that you've used for statistical analysis and performance evaluation. Talk about your experience with both qualitative and quantitative tools and how they enhance the testing process.

Share a specific example of a project where Human Factors principles were pivotal in overcoming challenges. Focus on how you identified user needs, the methods used, and the outcome that benefited end users, illustrating your problem-solving skills.

Discuss your strategy for planning user engagement sessions, including defining objectives, selecting formats (interviews, workshops), and recruiting participants. Highlight how you ensure that sessions yield valuable insights and your approach to analyzing the data collected.

Talk about your approach to UI mockup development, including the tools you use and how you incorporate user feedback in your designs. Describe a project where your mockups led to positive changes in the user interface and enhanced operational effectiveness.

Explain your understanding of usability standards and frameworks relevant to Human Factors Engineering. Discuss how you incorporate these standards into your work and share an example of how it has guided your design decisions in a project.

Provide a clear example where you employed statistical methods to analyze human performance data. Discuss the context, types of statistics used, and how your analysis informed decisions or improvements in AI systems.

Explain your techniques for effectively communicating technical information to non-technical stakeholders, including using visuals, summarization, and tailoring information based on audience needs. Provide an example of successful stakeholder engagement.

Share your personal motivations, highlighting your passion for improving user experiences and solving real-world problems. Discuss how your work contributes to making a meaningful impact, especially in critical areas like AI safety and efficacy.