About Aalo Atomics

Aalo Atomics is pioneering a new era in clean energy with factory-fabricated microreactors designed to deliver affordable, scalable, and reliable nuclear power. Our mission is to make nuclear energy globally accessible, starting with the Aalo-1, a 10 MWe reactor leveraging cutting-edge safety, modularity, and efficiency. Based in Austin, TX, we’re rapidly growing as we work to deploy the world’s first fleet of advanced microreactors. Join us and help revolutionize energy for a sustainable future.

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

Required

• 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.