Senior AI/ML Engineer
$180,000–$350,000 year
On-siteLos Angeles, California, United States
Job Summary
Design, build, and operate production-grade machine learning systems for a real-time AI intelligence platform ingesting satellite feeds, autonomous sensors, and open-source intelligence. Own the full lifecycle of specialized prediction models including temporal event forecasting, supply chain logistics analysis, and composite risk scoring using Bayesian inference and probabilistic calibration. Engineer large-scale data fusion pipelines processing heterogeneous streams into knowledge graphs managed with Neo4j, Qdrant, and Apache Iceberg. Deploy ensemble architectures and anomaly detection systems via NVIDIA Triton Inference Server, implementing automated versioning, A/B evaluation, and human-in-the-loop feedback mechanisms. Partner with backend engineers to integrate scalable AI infrastructure into operational environments, ensuring high reliability and calibrated decision-making for real-world mission-critical applications.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, Engineering, or a related technical discipline.
- 5+ years of experience building and operating production machine learning systems.
- Demonstrated experience shipping ML systems into real production environments—not just research or notebook-based experimentation.
- Strong Python software engineering skills with production-quality coding standards.
- Hands-on experience with Bayesian inference.
- Hands-on experience with Survival analysis.
- Hands-on experience with Probabilistic calibration (Platt Scaling, Isotonic Regression, or similar).
- Production experience using Neo4j.
- Production experience using Qdrant.
- Production experience using Apache Iceberg (or equivalent analytical storage).
- Experience deploying models using NVIDIA Triton Inference Server or equivalent model-serving technologies.
- Experience with PostgreSQL, pgvector, and Google Cloud Platform.
- Experience building streaming data pipelines, anomaly detection systems, and real-time inference services.
Desired Qualifications
- Experience with Model Context Protocol (MCP) or similar orchestration frameworks.
- Experience with Adversarial machine learning.
- Experience with Data poisoning detection.
- Experience with Secure or regulated deployment environments.
- Experience in Defense, intelligence, aerospace, or other mission-critical industries.
- Experience with CesiumJS or geospatial visualization technologies.
- Experience with TypeScript.
- Experience with Distributed ML infrastructure.
- Experience with Air-gapped or sovereign deployments.
- Experience with Enterprise AI infrastructure.
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