AI/ML Engineer

About the Company

Pixels on Target builds high-performance thermal imaging systems that push the limits of what's possible at the edge. We combine advanced sensing, embedded systems, and AI/ML to deliver real-time intelligence in the most constrained environments. Our focus is speed, performance, and delivering systems that work—reliably—when it matters most.

About the Role

We're looking for an AI/ML Engineer to build the intelligence behind our real-time edge systems. You will take models from research to deployment: you'll curate datasets, train and evaluate deep learning models, and make them run in real time on edge hardware with tight size, weight, and power (SWaP) limits. You'll work closely with our embedded, hardware, and systems teams to put ML at the core of our products. This is a hands-on role in a fast-moving environment with aggressive timelines, rapid iteration cycles, and evolving mission requirements.

Responsibilities

• Design, train, and evaluate deep learning models for classification, prediction, anomaly detection, and sensor data analysis.
• Build end-to-end ML pipelines: data collection, labeling, cleaning, augmentation, training, evaluation, and versioning.
• Optimize models for real-time inference on edge hardware (e.g. NVIDIA Jetson) using quantization (FP16/INT8), pruning, distillation, TensorRT, and ONNX.
• Profile and reduce latency, memory, and power across the full inference pipeline to meet onboard compute budgets.
• Integrate ML models into embedded and production systems in close collaboration with the embedded and hardware teams.
• Develop multimodal approaches that combine data from multiple sensors and sources.
• Explore and apply emerging techniques such as transformers, large language models, generative AI, reinforcement learning, and foundation models where they improve mission performance.
• Build synthetic data generation and data replay tools for model training and validation.
• Set up evaluation benchmarks, metrics, and regression tests to track model performance across environments and edge cases.
• Support field testing, data capture, and debugging of deployed systems.
• Contribute to system architecture, performance analysis, validation testing, and deployment (MLOps) pipelines.
• Drive rapid prototyping and move promising concepts into production-ready systems.

Qualifications

• 3+ years of experience developing machine learning systems (industry experience or relevant graduate research).
• Master's degree in Computer Science, Electrical and Computer Engineering, or a related technical field. PhD preferred.

Required Skills

• Strong Python skills and working proficiency in C++.
• Deep hands-on experience with PyTorch and/or TensorFlow, plus the standard ML stack (NumPy, Pandas, scikit-learn).
• Strong foundation in deep learning, machine learning, and statistical modeling.
• Experience building end-to-end ML pipelines, from data preprocessing through training, evaluation, and deployment.
• Track record of optimizing model inference for latency and throughput (e.g. TensorRT, ONNX Runtime, quantization, model compression).
• Experience with GPU-accelerated compute platforms such as NVIDIA Jetson or similar edge AI hardware.
• Experience working with large datasets; experience with thermal, infrared, hyperspectral, or other sensor data is a strong plus.
• Familiarity with Linux, Git, Docker, and cloud platforms (AWS or similar) for training and experiment tracking.
• Ability to read research papers and turn new methods into working code.
• Strong debugging and analytical skills across data, models, and hardware.
• Ability to operate effectively in fast-paced, ambiguous environments.
• High ownership mindset with strong execution and problem-solving ability.

Preferred Skills

• Experience with NLP, large language models, or generative AI.
• Experience with reinforcement learning or multimodal models.
• Experience with MLOps tooling (e.g. MLflow, Weights & Biases) and production model deployment.
• Experience with synthetic data generation.
• Publications in machine learning or AI venues.
• Experience with defense, ISR, or mission-critical systems.

Eligibility Requirement

Must be a U.S. Citizen or U.S. Permanent Resident (Green Card holder).