AI Researcher - Frontiers & Reasoning

Logical Intelligence; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Logical Intelligence in San Francisco is advancing AI-driven formal verification and scalable reasoning for provable code correctness. We are seeking an AI Researcher to push logical reasoning and energy-based modeling forward, collaborating with a world-class team of AI experts and engineers.

In this role you will design novel algorithms beyond traditional LLMs, pre-train and fine-tune models, and integrate cross-team insights to deliver groundbreaking solutions that advance verifiable software

Qualifications

• M.Sc. or Ph.D. preferred in CS/AI/Math or related field.
• 5+ years in ML/DL/Reasoning/Energy-based Modeling.
• Proficiency in Python and C++ for high-performance computing.
• Experience with PyTorch, TensorFlow or JAX.

Responsibilities

• Research new reasoning algorithms and models.
• Pre-train and fine-tune state-of-the-art LLMs.
• Combine reasoning algorithms with LLMs.
• Build efficient ML pipelines.
• Collaborate with other teams to define milestones.

Skills

Strong communication

Teamwork

Explicit and implicit reasoning

Research publications

Education

M.Sc./Ph.D. in CS/AI/Math

Tools

Python

C++

PyTorch

TensorFlow

JAX

Job description

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 Researcher and help us push the boundaries of what's possible in logical reasoning! We’re looking for a motivated individual to build, maintain, and improve innovative approaches including (but not limited to!) energy-based modeling (EBM). You'll be at the forefront of designing and refining algorithms that go beyond the capabilities of traditional Large Language Models (LLMs), tackling complex reasoning challenges. 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

• Research new reasoning algorithms and models
• Pre-train and fine-tune the State-of-the-Art LLMs
• Combine Reasoning algorithm and LLMs
• Build effective and efficient ML pipelines
• Collaborate with other teams to understand their pain points and priorities to define milestones of the corresponding roadmaps
• Derive practical solutions and integrate them with the results of other teams to provide the best overall resolution.

Qualifications

• M.Sc. or Ph.D. (preferable) focusing on one or more of the following areas: Computer Science, Artificial Intelligence, Mathematics, or a closely related field
• 5+ years of subject matter expertise and research in one or more of the following areas: Machine Learning, Deep Learning, Reasoning, Energy-based Modeling (preferable)
• 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
• Strong communication and teamwork skills
• Experience with explicit and implicit reasoningDemonstrated research publications in any of the major conferences (CVPR, ICLR, ICML, NeurIPS, ICCV, AAAI, ACL, etc.

Bonus Points

• Provable record of Energy-based usage for different problems
• Hands-on with algorithms used to train Energy-based models
• Multi-node and multi-GPU training
• Mathematical Reasoning – discrete math and logic