AI Engineer in Synthetic Data

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 and refine the data and ML pipelines for scaled distributed training and validation of ML models. 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

Develop model benchmarking processes and tools

Build effective and efficient ML data pipelines

Adjust frameworks and interfaces to accelerate machine learning development

Develop the infrastructure for data augmentation pipelines and synthetic data generation

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

You have an M.Sc. focusing on one or more of the following areas: Computer Science, Artificial Intelligence, Mathematics, or a closely related field

3+ years of production experience in ML Infra, DataOps, distributed training

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

Ability to understand deep learning algorithms, e.g. in natural language processing, reasoning

Familiarity with Azure/AWS/GCP cloud products for MLOps and DataOps pipelines

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

Multi-node and multi-GPU training

Mathematical Reasoning – discrete math and logic

Formal Verification - lean

logicalintelligence.com