Asks for

The ad says“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”
The ad says“3+ years of production experience in ML Infra, DataOps, distributed training. Proficiency with Kubernetes clusters and distributed compute assets”
The ad says“3+ years of production experience in ML Infra, DataOps, distributed training. Proficiency with Kubernetes clusters and distributed compute assets”
The ad says“Experience with basic distributed optimization techniques”
The ad says“3+ years of production experience in ML Infra, DataOps, distributed training. Proficiency with Kubernetes clusters and distributed compute assets”
The ad says“Ability to understand complex learning approaches, such as energy-based models”
The ad says“Deep understanding of transformers' internals, and ability to make radical changes to the architecture and handle higher-order derivatives”
The ad says“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”
The ad says“3+ years of production experience in ML Infra, DataOps, distributed training. Proficiency with Kubernetes clusters and distributed compute assets”
The ad says“Understanding of LLM architectures and LLM fine tuning internals”
The ad says“Expertise in optimizing machine learning systems, including general techniques and LLM-specific optimizations”
The ad says“Understanding state-of-the-art approaches in LLM reasoning”
The ad says“3+ years of production experience in ML Infra, DataOps, distributed training. Proficiency with Kubernetes clusters and distributed compute assets”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“Familiarity with torch.compile or similar performance optimization tools”
The ad says“Deep understanding of transformers' internals, and ability to make radical changes to the architecture and handle higher-order derivatives”

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