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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** NVIDIA Ltd. - **Location:** Los Angeles, CA, United States - **Experience:** Expert - **Salary:** $180,000.0 - $350,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Apache HTTP Server, Big Data, Cloud Computing, Data Deduplication, Data Files, Data Fusion, Data Infrastructure, Distributed Systems, Graph Database, Python (Programming Language), PostgreSQL, Machine Learning, Neo4j, Open Source Intelligence, Pattern Recognition, Systems Development Life Cycle, Reliability Engineering, Software Engineering, Data Streaming, Systems Integration, TypeScript, Management of Software Versions, AI Infrastructure, Data Processing, Scripting, Google Cloud, Data Ingestion, Delivery Pipeline, Reliability of Systems, Scalability Testing, Backend, Information Technology, Data Management, Machine Learning Operations, Software Coding - **Published:** July 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-ai-ml-engineer-los-angeles-ca--757bf9d6-5ce6-4055-85f7-faeeb1550624 ## About the Role * 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., Aerospace and Defense, Analysis Skills, Apache, Architectural Design, Artificial Intelligence (AI), Bayesian Networks, Best Practices, Calibration, Cloud Computing, Coding Standards, Computer Science, Continuous Improvement, Conversation Engine, Data Fusion, Data Management, Data Processing, Data Sets, Defense Intelligence, Distributed Computing, Engineering, Forecasting, Logistics, MCP - Microsoft Certified Professional, Machine Learning, Mathematics, Neo4j, OSINT (Open Source Intelligence), Performance Metrics, Performance Modeling, PostgreSQL, Predictive Modeling, Problem Solving Skills, Production Control, Production Machining, Production Systems, Prototyping, Python Programming/Scripting Language, Quality Metrics, Reliability Engineering, Risk, Scalability Testing, Scalable System Development, Software Engineering, Statistics, Structured Data, Supply Chain, System Integration (SI), Systems Reliability, Team Building, Technical Leadership, Test Plan/Schedule ## Description 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. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)