Knowledge Graph Engineer Jobs in Los Angeles, CA

Location: West Hollywood / Los Angeles, CA
Work Model: On-site (5 days per week)
Employment Type: Full-Time
Compensation: $180,000–$350,000+ USD (depending on experience and seniority)

Applicants must be legally authorized to work in the United States. Visa sponsorship is not available for this role.

About the Opportunity

Our client is an AI-native technology company building a next-generation AI intelligence platform that ingests data from satellite feeds, autonomous sensors, logistics networks, structured enterprise data, and open-source intelligence (OSINT). These diverse data sources are fused into a live knowledge graph that generates calibrated probabilistic assessments in real time.

This is not a chatbot, prompt-engineering, or RAG-wrapper opportunity. The engineering team is building production-grade machine learning infrastructure where prediction accuracy, reliability, and system robustness directly impact real-world decision making.

You'll join a small, senior engineering team building AI systems from the ground up, with significant ownership over architecture, production deployment, and the future evolution of the platform. The role offers the opportunity to solve complex machine learning problems in an environment where technical depth, first-principles thinking, and engineering excellence are highly valued.

The Role

We are looking for a Senior AI/ML Engineer to design, build, and operate the core machine learning systems powering the platform's intelligence engine.

This role is best suited for engineers who have successfully shipped production ML systems—not just research prototypes—and who enjoy building scalable AI infrastructure capable of processing large volumes of heterogeneous data in real time.

You will work across the full machine learning lifecycle, including model development, probabilistic inference, data fusion, deployment, monitoring, evaluation, and continuous improvement.

Key ResponsibilitiesProduction Machine Learning

• Design, build, deploy, and maintain production-grade machine learning systems.
• Own the lifecycle of multiple specialized prediction models supporting:
  • Temporal event prediction
  • Activity convergence modeling
  • Supply chain and logistics forecasting
  • Behavioral attribution
  • Trajectory prediction
  • Composite risk and threat scoring
  • Long-term anomaly detection
• Design ensemble architectures that combine multiple independent models into calibrated predictions.

Bayesian Inference & Probabilistic Modeling

• Build Bayesian inference pipelines supporting real-time prediction across multiple ingestion tiers.
• Implement probabilistic calibration techniques including Platt Scaling and related approaches.
• Produce confidence-scored predictions suitable for operational decision-making.
• Continuously evaluate and improve model reliability and calibration performance.

Data Fusion & Knowledge Graph Engineering

• Design large-scale ingestion pipelines processing:
  • Satellite imagery
  • Autonomous sensor data
  • Video and imagery streams
  • Logistics networks
  • Structured intelligence datasets
  • Open-source intelligence (OSINT)
• Maintain knowledge graph infrastructure using:
  • Neo4j
  • Qdrant
  • Apache Iceberg
• Implement entity resolution, deduplication, temporal versioning, and confidence-weighted data fusion across multiple sources.

Pattern Recognition & Adversarial Detection

• Build spatiotemporal event aggregation pipelines.
• Develop anomaly detection systems over streaming multi-source data.
• Implement clustering and sequence analysis techniques including DBSCAN and Dynamic Time Warping (DTW).
• Design systems capable of detecting adversarial signal manipulation, deception, and data poisoning.
• Develop testing frameworks that improve model robustness in contested data environments.

MLOps & Model Serving

• Deploy production models using NVIDIA Triton Inference Server or comparable infrastructure.
• Build automated model versioning, promotion, A/B evaluation, and deployment pipelines.
• Implement human-in-the-loop feedback mechanisms.
• Maintain reproducible training lineage and auditable model lifecycle records.
• Monitor production KPIs including:
  • Calibration accuracy
  • Prediction lead time
  • False alert rate
  • Operational reliability

Engineering Collaboration

• Partner with software engineers, platform engineers, and technical leadership to integrate machine learning systems into production environments.
• Contribute to architecture decisions spanning backend systems, AI infrastructure, and large-scale data processing.
• Help establish engineering best practices around reliability, scalability, testing, and deployment.

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
  • Survival analysis
  • Probabilistic calibration (Platt Scaling, Isotonic Regression, or similar)
• Production experience using:
  • Neo4j
  • Qdrant
  • 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.

Preferred Qualifications

Experience with one or more of the following is highly desirable:

• Model Context Protocol (MCP) or similar orchestration frameworks
• Adversarial machine learning
• Data poisoning detection
• Secure or regulated deployment environments
• Defense, intelligence, aerospace, or other mission-critical industries
• CesiumJS or geospatial visualization technologies
• TypeScript
• Distributed ML infrastructure
• Air-gapped or sovereign deployments
• Enterprise AI infrastructure

What We're Looking For

Successful candidates will demonstrate:

• A strong production engineering mindset with experience delivering complex ML systems end-to-end.
• High ownership and comfort working in fast-moving, ambiguous environments.
• Excellent systems thinking across machine learning, infrastructure, backend engineering, and distributed systems.
• Strong analytical rigor with an emphasis on reliability, calibration, and measurable model performance.
• Ability to move from first principles to production without relying on predefined playbooks.
• Passion for solving technically challenging problems where engineering quality matters.

Compensation & Benefits

• Base Salary: $180,000–$350,000+, depending on experience and seniority.
• Compensation is flexible for exceptional candidates with outstanding production ML experience.
• Competitive sign-on bonus.
• Comprehensive benefits package.
• Opportunity to join a well-funded, high-growth AI company at an early stage with significant technical ownership and long-term career growth.

Why Join?

• Build sophisticated AI infrastructure—not chatbot wrappers or prompt-engineering solutions.
• Work on challenging machine learning problems involving probabilistic reasoning, knowledge graphs, large-scale data fusion, and production inference.
• Join a highly technical, senior engineering team with significant ownership and autonomy.
• Contribute to AI systems designed for complex, real-world operational environments.
• Competitive compensation, meaningful technical impact, and the opportunity to help shape the future of an ambitious AI platform.

If you're passionate about building production machine learning systems, solving complex engineering challenges, and working on technology that goes far beyond traditional AI applications, we'd love to hear from you.