AI Engineer, ML Data

🔥 1 hour ago

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

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Logical Intelligence

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

🤝 B2B

🏢 Enterprise

Artificial Intelligence • B2B • Enterprise

Logical Intelligence is an AI research and engineering company developing energy‑based models (EBMs) and formal reasoning/proving systems to produce verified code for safety‑critical systems. Their flagship technologies include the Aleph prover — a state‑of‑the‑art formal reasoning system — and Kona 1. 0 (an EBM offering). Leadership shown on their site includes Eve Bodnia (Founder & CEO), Yann LeCun (Founding Chair, Technical Research Board), and senior technical officers. The company emphasizes automatic formal verification for code generation, formal theorem proving benchmarks, and applying EBM reasoning with LLMs as interfaces to scale reliable, agentic AI for critical applications.

📋 Description

• 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

🎯 Requirements

• 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