Machine Learning Engineer

8 Months ago • 2 Years + • Research Development

Job Summary

Job Description

Reality Defender is seeking a Machine Learning Engineer to train and fine-tune deep learning models in PyTorch on new datasets and client requirements. Responsibilities include model monitoring, quality assurance, ML workflow automation, CI/CD for client-facing models, adopting model optimization methods, implementing model obfuscation, and collaborating with AI and Engineering teams. The ideal candidate will have a Masters or PhD in Computer Science with a specialization in machine learning/deep learning, 2+ years of coding experience in Python, and 2+ years of industry experience with model training/fine-tuning in PyTorch. Experience with large foundation models, automated testing, CI/CD, and strong communication skills are preferred.
Must have:
  • Train/finetune deep learning models in PyTorch
  • Model monitoring and quality assurance
  • ML workflow automation and CI/CD
  • 2+ years coding experience in Python
  • 2+ years industry experience with model training/finetuning in PyTorch
  • Strong foundation in ML and data science
Good to have:
  • Experience finetuning large foundation models
  • Experience with automated testing and CI/CD in ML workflow
  • Good communication and inter-personal skills
Perks:
  • Offers Equity
  • Offers Bonus

Job Details

About Reality Defender

Reality Defender provides accurate, multi-modal AI-generated media detection solutions to enable enterprises and governments to identify and prevent fraud, disinformation, and harmful deepfakes in real time. A Y Combinator graduate, Comcast NBCUniversal LIFT Labs alumni, and backed by DCVC, Reality Defender is the first company to pioneer multi-modal and multi-model detection of AI-generated media. Our web app and platform-agnostic API built by our research-forward team ensures that our customers can swiftly and securely mitigate fraud and cybersecurity risks in real time with a frictionless, robust solution.

Youtube: Reality Defender Wins RSA Most Innovative Startup

Why we stand out:

  • Our best-in-class accuracy is derived from our sole, research-backed mission and use of multiple models per modality

  • We can detect AI-generated fraud and disinformation in near- or real time across all modalities including audio, video, image, and text.

  • Our platform is designed for ease of use, featuring a versatile API that integrates seamlessly with any system, an intuitive drag-and-drop web application for quick ad hoc analysis, and platform-agnostic real-time audio detection tailored for call center deployments.

  • We’re privacy first, ensuring the strongest standards of compliance and keeping customer data away from the training of our detection models.

Role and Responsibilities

  • Train/finetune deep learning models in PyTorch on new datasets and per client requirements

  • Model monitoring and quality assurance for deployed models

  • ML workflow automation and continuous integration/continuous delivery (CI/CD) for client-facing models

  • Adopt standard model optimization/compression methods for inference speed-up

  • Implement model obfuscation and vulnerability checks

  • Collaborate with both AI and Engineering teams for model/infrastructure needs and performance guidance

About You

  • Masters or PhD in Computer Science with specialization in machine learning/deep learning (ML/DL)

  • 2+ years coding experience in Python; Strong programming skills required

  • 2+ years industry experience with model training/finetuning in PyTorch

  • [Preferred] Experience finetuning large foundation models, e.g. wav2vec, HuBERT for downstream classification

  • Experience with automated testing and CI/CD concepts in machine learning workflow

  • Strong foundation in machine learning and data science

  • Good communication and inter-personal skills, comfortable with client-facing responsibilities

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United States (Remote)

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New York, New York, United States (On-Site)

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