Talent.com
AI Engineer

AI Engineer

Aalo AtomicsAustin, Texas, United States
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About the role

We are building ML systems that accelerate nuclear regulatory workstreams and augment reactor engineering and operations. You’ll design and deploy language- and simulation-driven models that automate documentation, support human-in-the-loop decision making, and inform optimization of plant and control strategies. The ideal candidate blends deep hands-on ML experience with rigor suitable for high‑assurance environments.

What you’ll do

Model & application development

  • Design and implement large language model-driven solutions for nuclear regulatory, engineering, and operational contexts, within an agentic framework .
  • Develop RAG and tool‑use flows over internal and public corpora (e.g., regulatory guidance, procedures, technical reports) to ground outputs and reduce hallucinations.
  • Build evaluation harnesses and guardrails for reliability, transparency, and traceability of model outputs.

Safety & compliance automation

  • Create pipelines that automate regulatory documentation generation, review, and compliance mapping.
  • Implement test plans and acceptance criteria aligned with high‑assurance software practices (versioning, reproducibility, traceability).
  • Package evidence (tests, evals, datasets, provenance) to demonstrate robustness and fitness for use in regulated workflows.
  • Design and implement security controls to ensure export control compliance.
  • Reactor systems & optimization

  • Apply reinforcement learning, simulation, and optimization (e.g., Bayesian methods, evolutionary algorithms) to improve design and operational parameters.
  • Collaborate with nuclear engineers to translate physics and safety models into ML‑driven decision‑support tools and human‑in‑the‑loop workflows.
  • Build ML systems that support autonomous and semi‑autonomous reactor control and monitoring in collaboration with controls & safety teams.
  • Platform & MLOps

  • Establish scalable infrastructure for training, testing, and deployment (containerized services, experiment tracking, CI for ML, dataset / version governance).
  • Design for secure, possibly air‑gapped and on‑prem environments; integrate with monitoring / observability for models in production.
  • Interact and coordinate with external technology platform and solution providers.
  • Cross‑functional collaboration

  • Work with engineering, regulatory affairs, product, and operations to align ML capabilities with nuclear and data‑center applications.
  • Qualifications

  • Strong programming in Python and modern ML frameworks (PyTorch, TensorFlow, etc.).
  • Demonstrated experience fine‑tuning and evaluating foundation models (prompting, adapters / LoRA, distillation, safety / robustness evals).
  • Proven ability to build end‑to‑end ML applications : data pipelines, retrieval / grounding, inference services, and monitoring.
  • Experience with Microsoft Azure.
  • Familiarity with safety‑critical software practices : version control, testing, reproducible ML workflows, change control.
  • Comfortable working fully on‑site in Austin and collaborating closely with domain experts across disciplines.
  • Preferred

  • Background in numerical methods, scientific computing, or physics‑based modeling.
  • Experience with optimization methods (Bayesian optimization, evolutionary algorithms, reinforcement learning) for complex engineering systems.
  • Exposure to high‑assurance or functional‑safety environments (e.g., IEC 61508 or analogous) and quality systems (e.g., NQA‑1‑style rigor) is a plus.
  • Practical MLOps experience with distributed training / inference (e.g., Kubernetes / Ray), experiment tracking, and data / version governance.
  • Familiarity with nuclear systems, energy infrastructure, or regulatory processes.
  • Physical

  • Primarily office / lab work on‑site in Austin; occasional hands‑on work with compute and test equipment; ability to use a computer for extended periods.
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