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Latent AI is seeking an experienced AI/ML Engineer to join their team building edge AI solutions for defense and commercial customers. The company delivers a developer platform that enables rapid deployment of AI capabilities on any device, with a focus on perception and autonomy for physical AI at the edge.
In this role, you will own meaningful ML engineering scope across the full pipeline: transforming real-world data into trainable datasets, selecting appropriate model approaches (classical classifiers, fine-tuned LLMs/VLMs, or custom architectures) based on cost-latency-accuracy tradeoffs, and optimizing inference pipelines for edge deployment. You'll partner with customers to build field-deployable solutions, improve the physical AI inference pipeline, and mentor teammates as the company's ML capability matures.
The ideal candidate is a builder with a track record of shipping CV/ML systems to production at scale. You've stood up ML infrastructure from scratch—training pipelines, serving infrastructure, evaluation harnesses, and monitoring—rather than inheriting a mature platform. You thrive in startup or small/mid-size company environments where you owned scope and made pragmatic tradeoffs about what to build, buy, or defer.
You have deep hands-on experience with perception and autonomous systems (computer vision), strong Python skills, and fluency with PyTorch, scikit-learn, and similar frameworks. You understand MLOps fundamentals: CI/CD for ML, model versioning, experiment tracking, drift detection, and production monitoring. You've designed inference systems with explicit latency and throughput targets on edge hardware (CPU/GPU) and can independently make informed tradeoffs between model complexity, cost, and performance. AWS experience is a plus.
Latent AI is based in Princeton, NJ and prefers in-office collaboration. US citizenship is required due to the nature of their defense and government projects. The company offers health, vision, and dental benefits, plus a 401(k) plan.
REQUIREMENTS:
- 3–5+ years of machine learning engineering experience on a small team, with a strong track record of shipping CV/ML systems to production
- Real, hands-on experience building solutions for perception and autonomous systems (e.g., computer vision)
- Experience standing up training pipelines, serving infrastructure, evaluation harnesses, and monitoring from scratch
- Startup or small/mid-size company experience with meaningful scope ownership and pragmatic tradeoff decisions
- Deep fluency with ML fundamentals: feature engineering, leak-aware train/test splits, metric selection (precision/recall/F1/AUC), cross-validation, and principled hyperparameter tuning
- Strong Python skills and hands-on experience with PyTorch, scikit-learn, and similar frameworks
- Solid MLOps foundation: CI/CD for ML, model versioning, experiment tracking, drift detection, and production monitoring
- Experience designing inference systems with explicit latency and throughput targets on edge hardware (CPU/GPU)
- US citizenship required
- AWS experience is a plus
