Why this roleWe're looking for a builder. Someone who has taken models from messy data to production at scale, and who has stood up ML infrastructure from scratch rather than inheriting it fully-built. What matters most is judgment: knowing the smallest, most efficient, most reliable model for the job, and understanding both ML methods deeply enough to make that call yourself. You're someone who has understands the data modality (audio, vision, speech) as much as the models to arrive at the best solution.
What you'll do- 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.
Who we're looking for- 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 & BenefitsUS Citizen requirement due to nature of our projects. We're based in Princeton, NJ . We believe in working together in person and prefer local in-office. We offer health, vision & dental benefits, a 401(k) plan.
