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Remote / Flexible
Remote only
About Interval
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.
Our platform is built around three outcomes:
- Control: Keep ownership of your data and how models use it.
- Verify: Audit what happened, why it happened, and where results came from.
- Monetize: Create new revenue opportunities through private, permissioned data exchange.
The Opportunity
We’re looking for a Machine Learning Engineer who’s excited about solving hard technical problems at the intersection of AI, privacy, and distributed systems—and who wants to help reimagine how enterprise data is activated, governed, and monetized.
What You’ll Do
Develop and deploy models that work with distributed, privacy-preserving enterprise data (structured, unstructured, and time series)
Work closely with our 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, with a focus on enterprise data security
Integrate ML systems into a secure infrastructure governed by on-chain access control and data provenance
What We’re Looking For
3–6 years of experience in machine learning, data science, or applied AI roles
Strong programming skills in Python, with experience in ML frameworks like PyTorch,TensorFlow, Hugging Face, or similar
Demonstrated experience working with real-world datasets—especially enterprise or high-integrity data (e.g., financial, medical, telemetry, etc.)
Comfort with data privacy techniques such as differential privacy, federated learning, or homomorphic encryption (or strong interest in learning them)
Interest or experience in working with LLMs, embeddings, or knowledge graph-based approaches
Nice to Have
Experience building ML systems in production environments (MLOps, CI/CD for models, data versioning)
Familiarity with data governance, compliance, or regulatory environments (e.g., HIPAA, GDPR)
Background in knowledge representation, multi-modal learning, or semantic reasoning
Why Join Interval?
Shape the frontier of AI, blockchain, and enterprise data infrastructure.
Build tools with real-world impact—help global enterprises activate and monetize their most valuable data assets.
Thrive in a sharp, mission-driven team backed by top-tier technical leadership and investors.
Enjoy meaningful equity, flexible work, and the autonomy to innovate where data, AI, and privacy meet.