Machine Learning Engineer

About the Company

Interval helps enterprises turn messy, underused data into governed, high-confidence intelligence—without handing control to a black box. We bring compute to your data with a private data lakehouse, verifiable audit trails, and U-AI, our contextual AI framework for secure AI workflows.

Responsibilities

• Develop and deploy models that work with distributed, privacy-preserving enterprise data
• Work closely with the AI team on Val, our internal contextual intelligence framework, including NLP, embedding systems, and semantic search
• Collaborate across product and engineering to build robust ML pipelines for data classification, anomaly detection, semantic inference, and explainability
• Research and prototype novel applications of machine learning in private and federated contexts
• Integrate ML systems into a secure infrastructure governed by on-chain access control and data provenance
• Contribute to internal tools and libraries that help automate model training, evaluation, versioning, and monitoring

Requirements

• 3–6 years of experience in machine learning, data science, or applied AI roles
• Strong programming skills in Python
• Experience in ML frameworks like PyTorch, TensorFlow, or Hugging Face
• Demonstrated experience working with real-world datasets (e.g., financial, medical, telemetry)
• Comfort with data privacy techniques such as differential privacy, federated learning, or homomorphic encryption
• Interest or experience in working with LLMs, embeddings, or knowledge graph-based approaches

Preferred Qualifications

• Experience building ML systems in production environments (MLOps, CI/CD, data versioning)
• Familiarity with data governance, compliance, or regulatory environments (e.g., HIPAA, GDPR)
• Background in knowledge representation, multi-modal learning, or semantic reasoning
• Familiarity with smart contracts or blockchain (Solidity, EVM)
• Prior experience in high-signal industries like real estate, energy, finance, or logistics