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# AI/ML Engineer

- **Company:** LATENT RISE AI LLC
- **Location:** Princeton, NJ, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Continuous Integration, Python (Programming Language), Machine Learning, Feature Engineering, Pytorch, Large Language Models, Model Validation, Scikit Learn, Low Latency, Machine Learning Operations, Drift Detection, Model Inference, Software Version Control
- **Published:** October 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2c5743d89ac95a59

## About the Role

* 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)
 * Thrived in environments where the ML infrastructure wasn't already built for you: you've stood up training pipelines, serving infrastructure, evaluation harnesses, and monitoring from scratch rather than inheriting a mature platform.
 * Startup or small/mid-size company experience where you owned meaningful scope and had to make pragmatic tradeoffs about what to build, what to buy, and what to defer.
 * Deep fluency with the fundamentals: thoughtful feature engineering, leak-aware train/test splits, metric selection on imbalanced data (precision/recall/F1/AUC over accuracy), cross-validation, and principled hyperparameter tuning.
 * Strong Python skills and hands-on experience with AI & ML frame works: (PyTorch, scikit-learn, etc)
 * Solid MLOps foundation: CI/CD for ML, model versioning, experiment tracking, drift detection, and production monitoring. Bonus if you have experience with training, evaluating and serving models
 * Experience designing inference systems with explicit latency and throughput targets on physical edge hardware (CPU/GPU), and independently making informed tradeoffs between model complexity, cost, and performance.
 * Experience with AWS is a plus.

Location & Benefits

## Description

* Turn real-world data into something a model can actually learn from, then decide what model approach fits: a classical classifier when it wins on cost and latency, a fine-tuned LLM/VLM when the tradeoff is worth it.
 * Improve our physical AI inference pipeline - balancing performance, latency, and accuracy deliberately.
 * Partner with team to engage with customer to build solutions that are deployable in the field.
 * Mentor teammates and raise the ML bar across the company as Latent AI's ML capability matures.