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Latent AI

Edge AI optimization and deployment software

AI/ML Engineer

Applied Machine LearningPosted on Nov 11
Full-timeNo salary listed

About the Job

Requirements

  • 3–5+ years of machine-learning engineering experience in an industry setting, with a strong track record of shipping computer vision or machine-learning systems to production.
  • A Master's or PhD in computer science or equivalent.
  • Experience building ML infrastructure from scratch, including training pipelines, serving infrastructure, evaluation harnesses, and monitoring.
  • Startup or small-to-mid-size company experience involving ownership of meaningful scope and pragmatic build-versus-buy decisions.
  • Deep fluency in feature engineering, leak-aware train/test splits, metric selection for imbalanced data (precision, recall, F1, and AUC rather than accuracy), cross-validation, and principled hyperparameter tuning.
  • Strong Python skills and hands-on experience with AI and machine-learning frameworks such as PyTorch and scikit-learn.
  • A solid MLOps foundation, including CI/CD for ML, model versioning, experiment tracking, drift detection, and production monitoring.
  • Experience designing inference systems with explicit latency and throughput targets on physical edge hardware (CPU/GPU), and making informed tradeoffs between model complexity, cost, and performance.
  • U.S. citizenship is required due to the nature of the projects.

Responsibilities

  • Build, optimize, and deploy production-grade machine-learning systems directly onto edge hardware.
  • Turn real-world data into model-ready data and select appropriate modeling approaches, including classical classifiers or fine-tuned LLMs/VLMs based on cost and latency tradeoffs.
  • Improve the physical AI inference pipeline by balancing performance, latency, and accuracy.
  • Partner with the team and customers to build solutions deployable in the field.
  • Mentor teammates and raise the company's machine-learning capabilities.

Desired Qualifications

  • Experience with computer vision and robotics, including experience with modalities such as EO/IR, acoustics/SONAR, RF, or SAR.
  • Experience training, evaluating, and serving models.
  • Experience with AWS.
  • Candidates based in New Jersey or Washington, DC are preferred.

About the Company

Latent AI

Edge AI optimization and deployment software

Latent AI helps organizations run AI on edge devices by providing an edge-focused MLOps workflow and a software development kit called Latent LEIP. The product compresses, secures, and deploys AI models so they can run efficiently on devices like smartphones, sensors, and other IoT gear, enabling real-time processing near the data source. Its edge MLOps workflow automates optimization and deployment, reducing the need for ongoing manual configuration. Latent AI differentiates itself by focusing specifically on edge AI optimization and automated deployment with a dedicated SDK and workflow, serving both federal and commercial clients in fast-moving environments like autonomous vehicles, smart cities, and industrial IoT. The company aims to make AI easier to use and more efficient at the edge, lowering deployment complexity and enabling reliable edge inference.

Company size
11-50
Company stage
Series A
Total funding
$22.5M
Headquarters
Princeton, NJ
Founded
2018

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