Software Engineer, Arena

• 5+ years of software development experience at a venture-backed startup or top technology company, with a focus on distributed systems and platform engineering
• Proficient in building backend and distributed systems
• Demonstrated interest and familiarity with building multi-agent systems
• Deep understanding of scalable system design, performance optimization, and resilience engineering
• Extensive experience with cloud platforms (e.g., AWS, Azure, GCP)
• Skilled at analyzing complex problems, proposing innovative solutions, and communicating technical concepts to both technical and non-technical stakeholders
• Comfortable with rapid prototyping and iterating based on user feedback
• Self-directed and excited to take ownership of major initiatives
• Proven track record of leading software development projects and collaborating with cross-functional teams. Strong interpersonal and communication skills to support a collaborative and inclusive work culture

Desirable

• As a Software Engineer at Arena, you will work to help build and scale our core Atlas platform
• This will include designing multi-agent systems, architecting and building multimodal agentic workflows, analyzing internet-scale datasets, evaluating, training, and fine-tuning large models, and developing new E2E features built on Arena’s product stack
• You will work closely with our Product and Go-To-Market teams to ensure a rapid iteration loop that incorporates customer and user feedback
• You will be responsible for building scalable and performant software and experiences
• Design and implement the core technical architecture behind Atlas’s advanced AI capabilities for hardware testing and optimization
• Translate complex hardware-focused workflow requirements into robust, highly scalable solutions that deliver real value to engineers in industries such as semiconductors, automotive, and aerospace
• Develop and refine our large-scale data ingestion and indexing platform, ensuring constant-time latency for indexing and retrieving any hardware or test-related documentation, telemetry, or simulation results—regardless of data volume
• Bring Atlas to the lab-bench by building integrations with engineering systems of record, and laboratory + test-bench hardware systems to deliver agentic control of end-to-end hardware engineering workflows
• Architect and implement high-throughput, low-latency pipelines that fuse cutting-edge LLMs with physics-informed deep learning models
• Ensure Atlas operates efficiently at scale, leveraging real-time telemetry and historic multimodal data to optimize hardware performance
• Collaborate directly with hardware engineers and technical stakeholders to gain deep insight into their real-world testing and debugging challenges
• Translate this empathy into innovative features that empower engineers to iterate faster and achieve breakthroughs in hardware design