AI Engineer in ML Data

Who We AreAt 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 RoleJoin 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 DoResearch new reasoning algorithms and modelsDevelop model benchmarking processes and toolsBuild effective and efficient ML data pipelinesAdjust frameworks and interfaces to accelerate machine learning developmentDevelop the infrastructure for data augmentation pipelines and synthetic data generationCollaborate with other teams to understand their pain points and priorities to define milestones of the corresponding roadmapsDerive practical solutions and integrate them with the results of other teams to provide the best overall resolutionQualificationsYou have an M.Sc. focusing on one or more of the following areas: Computer Science, Artificial Intelligence, Mathematics, or a closely related field3+ years of production experience in ML Infra, DataOps, distributed trainingExpertise in programming languages and tools critical for high-performance computing in Python/C++ and machine learning including Deep Learning frameworks like PyTorch /TensorFlow/JAXAbility to understand deep learning algorithms, e.g. in natural language processing, reasoningFamiliarity with Azure/AWS/GCP cloud products for MLOps and DataOps pipelines Proficiency with Kubernetes clusters and distributed compute assetsStrong communication and teamwork skillsReadiness to explore and promote cutting edge technologies in ML Infrastructure domain and beyondBonus PointsDemonstrated publications in any of the major conferences Multi-node and multi-GPU trainingMathematical Reasoning – discrete math and logicFormal Verification - leanlogicalintelligence.com