Senior Software Engineer

About the role

We are seeking a Principal/Senior Software Engineer to lead the development of our high-performance backend systems, data pipelines, and compute orchestration layers. You will build the core platform infrastructure that connects physics-based simulation engines and AI agent workflows with global enterprise clients.

Key Responsibilities

• System Architecture: Design, build, and maintain high-concurrency backend microservices in Python that power complex physical simulations and active learning loops.
• Data Pipelines & Integration: Build scalable ingestion and processing pipelines (RAG, Delta Lake) to unify fragmented enterprise data and physical schemas into a high-performance semantic layer.
• Agentic Compute Orchestration: Partner with AI/ML research teams to integrate agentic workflows, sandboxed execution systems, and model-serving infrastructure into secure, enterprise-grade cloud environments.
• Operational Excellence: Harden core system capabilities around multi-tenancy, security, auditability, CI/CD automation, and low-latency API performance.

Qualifications

• Experience: 6+ years of production software engineering experience building scalable backend microservices, high-throughput distributed systems, and API architectures.
• Core Tech Stack: Advanced proficiency in Python, alongside modern web frameworks (FastAPI, gRPC) and relational/NoSQL databases (PostgreSQL, Redis).
• Infrastructure & Cloud: Strong experience with containerization, orchestration, and IaC tools including Docker, Kubernetes, and Terraform on AWS, Azure, or GCP.
• Data & Systems: Hands-on experience with asynchronous task processing (Celery, RabbitMQ), large-scale data pipelines, and high-performance system design.
• Education: B.S. or M.S. in Computer Science, Software Engineering, Applied Mathematics, or a related quantitative field.

Preferred / Bonus Qualifications

• Experience or background in CAD engines, CAE/CFD software, computational physics, or digital twin simulation platforms.
• Exposure to physics-informed machine learning models, active learning loops, or autonomous LLM multi-agent orchestration.
• Knowledge of C/C++ low-level optimization, CUDA, or parallel compute systems.

Benefits

• Competitive pay
• Hybrid work options
• Comprehensive health benefits