Simulation Research Scientist

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Machina

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Aerospace • Hardware • B2B

Machina is a manufacturing engineering company that integrates next-generation technologies—casting, metal and sand additive manufacturing, and precision CNC machining—with computational design and process simulation to deliver sustainable, efficient serial and small-batch production. They specialize in aerospace- and automotive-grade aluminum castings, topology-optimized lightweighting, DfAM, post-processing (heat treatment, coatings), and end-to-end engineering services that turn AM prototypes into high-value production parts. Based in Istanbul, Machina focuses on industrial B2B collaborations to accelerate product development and reduce carbon footprint through optimized workflows.

1 - 10 employees

Founded 2021

🚀 Aerospace

🔧 Hardware

🤝 B2B

📋 Description

• Develop and maintain high-fidelity simulation environments for software build systems and remote execution processes • Implement performance models for build caching, task distribution, and system coordination in large-scale development environments • Create virtual testbeds for optimizing software compilation, testing workflows, and CI/CD pipeline automation • Integrate multi-agent simulation for real-time task coordination and resource allocation across distributed build systems • Work with Hardware-in-the-Loop (HIL) and Software-in-the-Loop (SIL) frameworks to validate build performance in real-world conditions • Optimize simulation workflows to validate build efficiency, cache hit rates, and task execution accuracy • Collaborate with the engineering, AI, and product teams to ensure consistency between simulation and real-world software builds

🎯 Requirements

• 3+ years of experience in software simulation, system modeling, or performance testing for distributed systems • Strong expertise in simulation frameworks like Gazebo, PyBullet, CARLA, or similar for creating virtual environments • Proficiency in Python, Go, or C++ for real-time simulation development and system integration • Experience with distributed systems, microservices architecture, and CI/CD pipelines in large-scale environments • Knowledge of multi-agent reinforcement learning (MARL) principles and AI-driven optimization techniques for software systems • Experience with performance monitoring, load balancing, and task scheduling in complex development infrastructures • Familiarity with build systems (e.g., Jenkins, CircleCI, etc.), containerization (e.g., Docker), and cloud infrastructure (AWS, GCP, Azure)

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development