Job Specifications
About The Role
At Autopoiesis Sciences, we're building the foundation for scientific superintelligence: AI that can autonomously advance knowledge across every scientific discipline. We're first developing an AI co-scientist that applies the same systematic doubt and verification that drives human scientific breakthroughs, establishing the reasoning capabilities necessary for true scientific autonomy. This represents the critical first stage toward systems that can eventually operate laboratories autonomously, design their own research programs, and pursue discoveries beyond human imagination. Backed by leading venture capital firms and with thousands of scientists from top universities already requesting early access, we're currently preparing to release our AI co-scientist to the scientific community.
Technical Focus Areas
Distributed systems architecture for large-scale AI training and inference
Production infrastructure for autonomous agent systems
High-performance computing and optimization
Data pipeline engineering for scientific datasets
System reliability, monitoring, and observability
API design and service architecture
Database systems and knowledge graph infrastructure
Deployment systems and MLOps tooling
Autopoiesis Sciences is seeking exceptional Software Engineers to build the robust, scalable infrastructure that powers scientific superintelligence. As a Software Engineer, you will architect and implement the production systems that enable our AI co-scientist to operate at scale across thousands of researchers and scientific disciplines. You'll work at the intersection of cutting-edge AI research and world-class engineering, translating breakthrough algorithms into reliable, performant systems. This role offers the opportunity to solve unique technical challenges that don't exist anywhere else: building infrastructure for AI systems that conduct scientific research autonomously. We're looking for engineers who are obsessed with elegant system design, performance optimization, and building tools that researchers love to use.
Responsibilities
Design and build highly scalable distributed systems that support massive-scale training, inference, and deployment of autonomous scientific agents
Architect production infrastructure for AI systems that perform complex multi-step reasoning, literature analysis, and hypothesis generation across scientific domains
Build robust data pipelines and storage systems that handle diverse scientific datasets, research papers, experimental results, and knowledge graphs
Develop comprehensive monitoring, observability, and debugging tools for complex AI agent behavior in production environments
Optimize system performance across the stack, from low-level computational efficiency to high-level service architecture
Create developer tools and APIs that enable research scientists to rapidly iterate on new capabilities while maintaining production reliability
Collaborate with ML researchers to translate novel algorithms into production-ready implementations that scale
Drive technical decisions around infrastructure, tooling, and architecture that will shape how scientific AI systems operate for years to come
Qualifications
Strong systems programming and software engineering fundamentals with experience building production infrastructure at scale
Proven ability to design distributed systems, databases, and high-throughput data processing pipelines
Experience with ML infrastructure, training systems, or deployment of large-scale AI models
Solid understanding of ML concepts and familiarity with modern AI systems (LLMs, agents, multi-step reasoning)
Track record of writing clean, maintainable code and making pragmatic engineering tradeoffs
3+ years of software engineering experience in production environments
Bachelor's degree minimum in Computer Science, Engineering, Mathematics, Physics, or related technical field (advanced degree preferred but not required)
Compensation And Benefits
Competitive compensation, equity, and benefits
Collaborate in building a thoughtful, mission-driven team culture focused on scientific discovery
Frequent team events, dinners, off-sites, and gatherings
Compensation packages are highly variable based on experience, expertise, and contribution potential. If your expectations fall outside our stated range, we encourage you to apply regardless. The cash compensation range for this role is $120,000 USD to $180,000 USD, plus equity
Get To Know Us
We're a dedicated team of scientists, researchers, and engineers who believe deeply in the transformative potential of our work. You'll work closely with our leadership team including Chief Executive Officer Joseph Reth (Attended University for CS at 14, former DARPA), Chief Business Officer Dr. Eike Gerhardt (University of Tubingen PhD, University of Tubingen staff), and Chief Scientist Dr. Larry Callahan (University of Chicago PhD, former FDA, former NIH).
Application Process: Due to the high vo