AI Engineer - Reinforcement Learning

Who we are

At Logical Intelligence, we're revolutionizing software development with AI-powered formal verification. We've developed groundbreaking agents that provide mathematical guarantees of code correctness, ensuring that software behaves exactly as intended while proactively identifying bugs and security vulnerabilities. Our novel foundation model enables scalable, precise reasoning for formally verifiable code across Rust, Golang, and smart contract VMs. We’ve won ​​a well-known formal verification benchmark called PutnamBench, which consists of 672 hard math problems from the William Lowell Putnam Exam, the oldest collegiate mathematics competition in North America. Backed by a world-class team – including ICPC champions, a Fields Medalist and an ACM Turing Award winner – we're building the future where all code is provably correct.

About the role

Join our team as an AI Engineer and help us push the boundaries of what's possible in logical reasoning! We’re looking for a motivated individual to design, implement, and refine efficient Large Language Models (LLMs) pipelines for scaled distributed training. You'll be at the forefront of designing and refining algorithms that go beyond the capabilities of traditional LLMs. You'll work closely with a talented team of AI experts, EBM specialists, formal verification engineers, and software developers to create groundbreaking solutions.

What you'll do

Implement new reasoning algorithms and models

Evaluate reasoning approaches, including latent space reasoning

Pre-train, fine-tune, and modify the State-of-the-Art LLMs

Optimizing and scaling LLM pipelines

Adjust frameworks and interfaces to accelerate machine learning development

Derive practical solutions and integrate them with the results of other teams to provide the best overall resolution

Qualifications

Deep understanding of transformers' internals, and ability to make radical changes to the architecture and handle higher-order derivatives

Expertise in programming languages and tools critical for high-performance computing in Python/C++ and machine learning including Deep Learning frameworks like PyTorch /TensorFlow/JAX

Expertise in optimizing machine learning systems, including general techniques and LLM-specific optimizations

Understanding state-of-the-art approaches in LLM reasoning

Ability to understand complex learning approaches, such as energy-based models

Experience with basic distributed optimization techniques

Familiarity with torch.compile or similar performance optimization tools

Understanding of LLM architectures and LLM fine tuning internals

3+ years of production experience in ML Infra, DataOps, distributed training. Proficiency with Kubernetes clusters and distributed compute assets

Strong communication and teamwork skills

Readiness to explore and promote cutting edge technologies in ML Infrastructure domain and beyond

Bonus Points:

Demonstrated publications in any of the major conferences

Experience in EBM or latent reasoning

Demonstrated publications in any of the major conferences

Mathematical Reasoning – discrete math and logic

logicalintelligence.com