Applied AI Engineer II (Audio)
Reality Defender - Global Remote | Remote
Posted Jun 10, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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Market context
- U.S. role benchmark (BLS OEWS)
- $116,543 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +9.8% - Much faster than average
Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Role
Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Applied AI Engineer II (Audio) Global Remote | Remote WHO WE ARE. Reality Defender is an award-winning cybersecurity company helping enterprises and governments detect deepfakes and AI-generated media. Utilizing a patented multi-model approach, Reality Defender is robust against the bleeding edge of generative platforms producing video, audio, imagery, and text media. Reality Defender's API-first deepfake detection platform empowers teams and developers alike to identify fraud, disinformation campaigns, and harmful deepfakes in real time. Backed by world class investors including DCVC, Illuminate Financial, Y Combinator, Booz Allen Hamilton, IBM, Accenture, Rackhouse, and Argon VC, Reality Defender works with leading enterprise clients, financial institutions, and governments in order to ensure AI-generated media is not used for malicious purposes. Youtube: Reality Defender Wins RSA Most Innovative Startup https://www.youtube.com/watch?v=TKOZmwyNUNM THE APPLIED AI ENGINEER II (AUDIO) ROLE. We are seeking an Applied AI Engineer II (Audio) to build, tune, and deploy state-of-the-art audio deepfake detection models in real-world client environments. You will be responsible primarily for model tuning and deployment, ensuring robustness, reliability, and performance under diverse real-world test conditions - including adversarial and edge-case scenarios. The role requires deep hands-on expertise in model building, training, and benchmarking. WHAT YOU'LL DO - Tune and optimize ML/DL models for production-scale audio deepfake detection. - Investigate failure cases in the client environment, build custom evaluation frameworks, and implement mitigation strategies for model robustness. - Drive model iteration for performance under a variety of real-world environments, e.g. compression artifacts, noise, telephony, and streaming pipel
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