Theorem Verified
Software Development, IT Consulting, Product Engineering, Digital Transformation
ML Research Engineer
San Francisco, California, United StatesOnsiteFull Time$150,000–$250,000 /yrPosted 2 months agoHidden Gem · YC Startup
Role summary
We are seeking an ML Research Engineer to build products that enhance software correctness, understandability, and security. The role involves advancing AI capabilities in program analysis and formal reasoning, and developing interfaces for navigating complex code to identify bugs and generate documentation. The ideal candidate is driven by ambitious projects, enjoys solving challenging problems, and is eager to contribute to a collaborative team environment.
We’re building products to make the future of software **correct, understandable, and secure**. Right now, AI has raised the floor on software engineering. We’re building for the world where it raises the ceiling.
### **What we’re building**
Our approach is to write software correctness feedback that scales with complexity and volume of code, so software is provably safe and reliable.
What this looks like in practice:
1. Post-training that make models as good at program analysis and formal reasoning as they are at writing Python.
2. Interfaces for navigating complex implementations of simple specifications to help users find bugs, generate design documentation, and implement non-functional requirements using compiler-level reasoning methods.
### **You may be a good fit if you**
* Pick projects that are a few orders of magnitude bigger, faster, or harder than SOTA
* Have the gumption to try out something before everyone starts talking about it
* Love to be helpful to the people around you, whether it is pair-debugging or teaching someone something new or just picking up the slack on the mundane tasks that are blocking progress
* Dig deep to get satisfying explanations for how and why things work
* Are practiced in solving last-mile problems
**Compensation**: $150,000-250,000 with generous equity
### **What we’re building**
Our approach is to write software correctness feedback that scales with complexity and volume of code, so software is provably safe and reliable.
What this looks like in practice:
1. Post-training that make models as good at program analysis and formal reasoning as they are at writing Python.
2. Interfaces for navigating complex implementations of simple specifications to help users find bugs, generate design documentation, and implement non-functional requirements using compiler-level reasoning methods.
### **You may be a good fit if you**
* Pick projects that are a few orders of magnitude bigger, faster, or harder than SOTA
* Have the gumption to try out something before everyone starts talking about it
* Love to be helpful to the people around you, whether it is pair-debugging or teaching someone something new or just picking up the slack on the mundane tasks that are blocking progress
* Dig deep to get satisfying explanations for how and why things work
* Are practiced in solving last-mile problems
**Compensation**: $150,000-250,000 with generous equity
