Machine Learning Engineer

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Engineering

Machine Learning Engineer

As a Machine Learning Engineer at Octave-X, you will be responsible for the end-to-end lifecycle of our AI models — from research prototyping and architecture design through distributed training, evaluation, and production deployment. You will work at the intersection of applied ML engineering and production reliability, building the inference and training infrastructure behind Tenzin, our flagship formally verified AI model. This role is deeply technical and high-impact: you will directly shape how enterprise customers experience safe, verifiable AI. You will collaborate closely with our research, infrastructure, and product teams to translate cutting-edge ideas into systems that operate at scale in regulated and high-trust environments, including healthcare, finance, and public sector.

Chicago, IL or Remote (US) Full-time $120,000 + benefits

Role Snapshot

Team

Engineering

Location

Chicago, IL or Remote (US)

Compensation $120,000 + benefits

About The Role What You Will Build

Design, train, and deploy formally verified AI systems that power Tenzin and the broader Octave-X platform for enterprise workloads.

• Design, build, and optimize large-scale distributed training and inference pipelines for Tenzin and future Octave-X models.
• Develop and maintain comprehensive evaluation suites that measure safety, reliability, alignment, and model quality across deployment scenarios.
• Partner with product, research, and infrastructure teams to translate new model capabilities from prototype to production-grade services.
• Build and improve real-time observability systems for model behavior, performance regressions, and data drift detection.
• Architect and implement secure, testable, and maintainable ML infrastructure using modern frameworks and cloud-native tooling.
• Contribute to the design of formal verification layers that provide mathematical guarantees over model outputs.

Required Qualifications

• 3+ years of hands-on machine learning engineering experience shipping models to production environments.
• Strong Python proficiency with deep experience in PyTorch, JAX, or equivalent modern ML frameworks.
• Demonstrated experience with distributed training (multi-GPU/multi-node), model parallelism, and inference optimization.
• Practical knowledge of transformer architectures, attention mechanisms, and large language model evaluation workflows.
• Solid understanding of MLOps practices: experiment tracking, model versioning, CI/CD for ML, and monitoring.
• Strong communication skills with an ownership mindset — you take problems end-to-end and hold yourself accountable.

Nice To Have

• Experience with formal methods, verification systems, type theory, or proof assistants (Lean, Coq, Agda).
• Background working in regulated domains such as healthcare, financial services, or government/public sector.
• Hands-on experience with GPU performance profiling, kernel optimization, and compute cost reduction strategies.
• Familiarity with Homotopy Type Theory or dependent type systems.

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