Senior Machine Learning Engineer

About This Role

• CAE Vision:
  Our vision is to be the worldwide partner of choice in defense and security, and civil aviation by revolutionizing our customers’ training and critical operations with digitally immersive solutions to elevate safety, efficiency and readiness.
• CAE Defense & Security Mission: CAE’s Defense and Security business unit focuses on helping prepare military customers to develop and maintain the highest levels of mission readiness.
• CAE Values:
  Empowerment, Innovation, Excellence, Integrity and OneCAE make us who we are and we strive to make a difference in the world while helping each other succeed.

• Comprehensive and competitive benefits package and flexibility that promotes work-life balance
• A work environment where all employees are valued, respected and safe
• Freedom to succeed by enabling team members to deliver, take initiatives and make decisions
• Recognition, professional development, advancement and having fun!

We are seeking a highly skilled and experienced Machine Learning Engineer to join our growing AI & Data Science team in R&D. This role is ideal for someone passionate about solving complex problems using data-driven approaches and deploying scalable machine learning solutions in production environments. Additionally, this role will focus on designing scalable NLP systems powered by state‑of‑the‑art transformer models, optimizing inference performance, and integrating LLMs into real‑world products.

You’ll collaborate with cross‑functional teams to deliver intelligent, language‑driven solutions that enhance user experience and business outcomes. This position is onsite with locations in Tampa FL, Arlington TX, or Orlando FL.

• Design, develop, and deploy machine learning models for real‑world applications.
• Build scalable data pipelines and model training workflows using modern tools and frameworks.
• Conduct rigorous model evaluation, validation, and performance tuning.
• Monitor and maintain deployed models, ensuring reliability and accuracy over time.
• Design, fine‑tune, and deploy LLMs (LLaMA, Mistral, etc.) for various NLP tasks such as summarization, question answering, semantic search, and chatbots.
• Develop scalable and efficient model serving infrastructure using tools like ONNX, Tensor
  
  RT, Deep Speed, or vLLM.
• Implement retrieval‑augmented generation (RAG) pipelines using vector databases (e.g., FAISS, Weaviate, Pinecone, Milvus).
• Optimize LLM inference for latency, throughput, and cost across cloud and edge environments.
• Collaborate with cross‑functional teams to understand business requirements and translate them into ML solutions.
• Stay current with the latest research and trends in machine learning, AI & LLMs.
• Mentor junior engineers and contribute to team knowledge sharing.
• Document processes, models, and decisions for transparency and reproducibility.

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field. PhD is a plus.
• 5+ years of software development experience, with at least 2 years focused on NLP or LLMs.
• Proficiency in ML frameworks (PyTorch, Tensor Flow, Scikit-learn, CUDA).
• Good understanding of distributed systems, understanding of microservice architecture and REST APIs.
• Strong understanding of MLOps tools and practices (MLflow, Airflow, DVC).
• Hands‑on experience with Hugging Face Transformers, Lang Chain, and OpenAI APIs.
• Technology proficiency with cloud platforms (AWS, GCP, Azure), Linux, and container orchestration (Docker, Kubernetes).
• Proven track record of deploying ML models in production environments.
• Experience in working with SQL/No
  
  SQL database systems such as MySQL, Mongo
  
  DB or Elasticsearch.
• Due to U.S. Government contract requirements, only U.S. citizens are eligible for this role.

• Experience with deep learning, NLP, computer vision, or reinforcement learning.
• Experience with feature engineering and model interpretability techniques.
• Knowledge of prompt engineering and prompt optimization strategies.
• Experience with multi‑modal models (e.g., combining text with image or audio inputs).
• Familiarity with…