AI/ML Engineer
About the job
- Have 3–5+ years of machine learning engineering experience on a small team and a track record of shipping computer vision and machine learning systems to production.
- Have hands-on experience building solutions for perception and autonomous systems, such as computer vision.
- Have experience establishing training pipelines, serving infrastructure, evaluation harnesses, and monitoring from scratch rather than inheriting a mature machine learning platform.
- Have startup or small/mid-size company experience owning meaningful scope and making pragmatic build, buy, and defer tradeoffs.
- Have fluency in feature engineering, leak-aware train/test splits, metric selection for imbalanced data (precision, recall, F1, and AUC over accuracy), cross-validation, and principled hyperparameter tuning.
- Have strong Python skills and hands-on experience with artificial intelligence and machine learning frameworks such as PyTorch and scikit-learn.
- Have a solid MLOps foundation, including CI/CD for machine learning, model versioning, experiment tracking, drift detection, and production monitoring.
- Have 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.
- Be a U.S. citizen due to the nature of the projects.
- Turn real-world data into model-ready data and select an appropriate model approach, such as a classical classifier or a fine-tuned large language model/vision-language model, based on cost, latency, and tradeoffs.
- Improve the physical AI inference pipeline by balancing performance, latency, and accuracy.
- Partner with the team to engage with customers and build solutions deployable in the field.
- Mentor teammates and raise the company's machine learning capability.
- Have experience with AWS.
- Experience training, evaluating, and serving models is a bonus.
About the company
Latent AI provides edge AI optimization through an edge MLOps workflow and an SDK called Latent LEIP to compress, secure, and deploy AI models for edge devices. Developers use LEIP to build and deploy optimized models for smartphones, sensors, and IoT, automating compression, security hardening, and performance tuning for real-time edge processing. The company differentiates itself by focusing on dedicated edge AI tooling and automated deployment for both federal and commercial clients in areas like autonomous vehicles, smart cities, and industrial IoT. Its goal is to make AI practical at the edge by simplifying deployment and reducing ongoing maintenance.
Company Size
11-50
Company Stage
Series A
Total Funding
$22.5M
Headquarters
Princeton, New Jersey
Founded
2018
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Simplify's Take
What believers are saying
- AUM Ventures invested March 3, 2026, validating Latent AI’s India and defense expansion.
- Rajant showcased fleet-scale DDIL deployments at the 2026 Partner Summit in Wickenburg.
- Project Linchpin cut deployment from weeks to minutes; Project AMMO sped updates 33%.
What critics are saying
- Latent AI depends on defense budgets; Army, Navy, and Air Force procurement cycles dominate revenue.
- OpenAI, Palantir, Anduril, and Shield AI compress Latent AI’s differentiation and pricing power.
- DoD standardization on bigger platforms in 2027 turns Latent AI into a niche toolkit.
What makes Latent AI unique
- Latent AI’s LEIP compresses and redeploys edge models in minutes, not weeks, for DoD.
- On March 18, 2026, Latent AI chaired EDGE AI Foundation’s first Defense Working Group.
- The May 2026 Rajant Health partnership combines mesh networking, compute, and over-the-air model updates.
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Benefits
- Remote Work Options
- Flexible Work Hours
- Health Insurance
- Dental Insurance
- Vision Insurance
- Paid Vacation
- Paid Holidays
- 401(k) Retirement Plan
- 401(k) Company Match
- Stock Options
- Company Equity
- Wellness Program