Simulation Engineer
Confirmed on the employer's own hiring board on Oct 30, 2026. First seen by Alion on May 22, 2026. HammerHead AI scores B on the Alion truth index.
About Hammerhead
We're unleashing AI with intelligent orchestration while addressing one of the most pressing bottlenecks for AI access to Power. Our cutting-edge platform optimizes data center power infrastructure to maximize AI token generation within existing electrical limits, without requiring new power plants or grid expansions. Our team has optimized over 8 gigawatts of mission-critical power globally, and we're addressing a $64 billion-per-year market opportunity while dramatically reducing the environmental footprint of AI infrastructure.
At Hammerhead, you will:
Work at the intersection of AI, energy, and compute creating the next generation AI infrastructure
Collaborate with colleagues that are experts in modern RL and AI, IoT and IIoT software, and infrastructure technologies
Contribute to building a more efficient and sustainable future for AI compute.
Join a company at the cutting edge of modern data center design and operation
Receive competitive compensation, equity, and benefits in a high-growth, mission-driven environment.
Learn from an experienced team that has built and sold startups before
Learn more about Hammerhead
Role Description
As an AI/ML Simulation Engineer, you will build the virtual world where our intelligence is born. You will be responsible for creating and scaling high-fidelity digital twins of physical data center environments. These simulations are the critical training ground for our Orchestrated RL Control Agents (ORCA). Reporting to the CTO, you will model the complex interplay of power, thermal dynamics, and computation. Your work will enable our RL Engineers to safely and rapidly develop control agents that can be deployed with confidence into live, mission-critical facilities.
Key Responsibilities
Digital Twin Development: Architect and build high-fidelity, physics-based simulations of data center components, including cooling systems, power distribution units, and server racks.
Simulation Platform Integration: Integrate individual asset models into a comprehensive, scalable simulation platform that represents entire data center environments.
Model Validation: Develop methodologies to validate simulation accuracy against real-world operational data, ensuring our digital twins faithfully represent physical reality.
AI Training Environments: Create and maintain the infrastructure and APIs that allow Reinforcement Learning engineers to train and evaluate control agents at scale within the simulation.
Performance Optimization: Ensure the simulation platform is fast, scalable, and efficient to accelerate the AI/ML development lifecycle.
Qualifications
Digital Twin Experience: 3+ years of professional experience in developing digital twins or high-fidelity simulations for complex physical systems (e.g., in energy, aerospace, manufacturing, or robotics).
Strong Programming Skills: Proficiency in Python, Go and/or C++ and experience with simulation frameworks or libraries.
Physics-Based Modeling: Strong understanding of first-principles modeling, with experience capturing the dynamics of physical systems (thermal, electrical, or mechanical).
ML/AI Exposure: Familiarity with the lifecycle of machine learning models and experience creating environments for training and testing AI agents.
Educational Background: MS or PhD in a relevant engineering discipline, Computer Science, Math or Physics.
Problem Solver: A practical mindset, able to abstract complex physical interactions into computationally efficient models and troubleshoot discrepancies between simulation and reality.
What We Offer
Competitive salary, bonus, 401(k) plan and equity in a rapidly growing startup
Comprehensive health, dental, and vision coverage
Opportunity to apply the latest AI technologies working with an experienced team
Join our team to shape the foundation of tomorrow’s AI infrastructure
Visit our Careers page at (hammerheadco dot ai / careers) to apply