Armada is seeking exceptional AI Engineers to build and deploy intelligent systems at the edge of the physical world.

You will develop production AI for offshore energy platforms, remote mines, defense installations, transportation networks, industrial facilities, autonomous systems, and distributed camera and sensor networks. These systems must operate reliably under real-world constraints, including intermittent connectivity, strict latency requirements, limited compute, noisy data, changing conditions, and rigorous security demands.

Depending on your expertise, your work may span multimodal and generative AI, real-time computer vision, large language and vision-language models, robotics, reinforcement learning, statistical machine learning, time-series analysis, anomaly detection, or distributed AI inference.

This role is intended for engineers with at least three years of relevant industry experience who have moved beyond experimentation, RAG implementation, and model prototyping. You will own substantial portions of the AI lifecycle, from problem definition and data preparation through model development, optimization, evaluation, deployment, observability, and continuous improvement in production.

The strongest candidates combine rigorous ML and AI fundamentals with strong software-engineering judgment. They are equally comfortable interpreting research, implementing and evaluating new methods, diagnosing performance bottlenecks, and deploying resilient containerized AI services on GPUs across cloud, data-center, and disconnected edge environments.

What You'll Do (Key Responsibilities)

• Translate operational problems into AI requirements, datasets, evaluation criteria, and production architectures.
• Design, train, fine-tune, and deploy models for vision, language, multimodal AI, time-series analysis, autonomy, and optimization.
• Apply state-of-the-art research using fine-tuning, distillation, quantization, and hybrid AI approaches.
• Build training and evaluation datasets from video, images, text, telemetry, sensor data, and synthetic data.
• Evaluate models for accuracy, latency, throughput, robustness, safety, groundedness, and resource efficiency.
• Optimize inference through quantization, pruning, distillation, batching, caching, and hardware-aware acceleration.
• Build reliable AI services using Python, testing, versioning, observability, and automated deployment.
• Deploy containerized AI workloads across Kubernetes, cloud, on-premises, and disconnected edge environments.
• Build pipelines for model monitoring, data validation, drift detection, retraining, and controlled updates.
• Partner with customers, product teams, engineers, and domain experts to move prototypes into production.
• Diagnose issues across data pipelines, models, GPUs, orchestration, networking, and applications.
• Contribute reusable models, datasets, evaluation tools, platform components, documentation, patents, and publications.

Required Qualifications

• Master’s or PhD in applied mathematics, computer science, computational science, engineering, or a related technical field.
• 3+ years of industry experience in AI research, machine learning, and production software development.
• Strong programming skills in Python and proficiency in Java, C++, or another production language.
• Hands-on experience with statistical machine learning, deep learning, and natural language processing.
• Strong understanding of supervised, unsupervised, transfer, and representation learning.
• Experience with modern neural architectures, including transformers, convolutional networks, detection models, generative models, and autoencoders.
• Proficiency with deep-learning frameworks such as PyTorch, TensorFlow, or JAX.
• Familiarity with containers, numerical libraries, modular software design, version control, and automated testing.
• Experience applying machine learning to real-world problems and deploying models beyond research prototypes.
• Evidence of technical depth through peer-reviewed publications, open-source contributions, or significant production systems; publications at conferences such as NeurIPS, ICML, ICLR, AAAI, CVPR, or ICCV are highly valued.

Preferred Experience and Skills

• Experience deploying production AI on GPUs, edge servers, embedded accelerators, robotics platforms, or distributed infrastructure.
• Experience with TensorRT, Triton Inference Server, ONNX Runtime, vLLM, CUDA, or similar inference technologies.
• Experience optimizing AI workloads for latency, throughput, memory, power, and real-time performance.
• Experience with Kubernetes, microservices, distributed systems, CI/CD, and production MLOps.
• Experience building multimodal systems using vision, language, audio, telemetry, geospatial, or sensor data.
• Experience developing autonomous, robotic, industrial, defense, energy, transportation, or safety-critical systems.
• Familiarity with quantization, pruning, distillation, LoRA, and other parameter-efficient optimization techniques.
• Experience with synthetic data, active learning, weak supervision, human-in-the-loop workflows, or simulation.
• Ability to make sound decisions under ambiguity and own systems from concept through production.