Machine Learning Engineer - Interval

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.

• 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

• Contribute to internal tools and libraries that help automate model training, evaluation, versioning, and monitoring

• 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

• Familiarity with the basics of smart contracts or blockchain (Solidity, EVM, etc.) is a plus—but not required

• 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

• Prior experience in high-signal industries like real estate, energy, finance, or logistics