Top Machine Learning Engineer Jobs in Los Angeles, CA

The Machine Learning Engineer will design, develop, and implement advanced recommendation systems, collaborating with cross-functional teams to enhance personalization for a global audience.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

The Machine Learning Engineer will develop and implement ML algorithms for improving communication system performance, collaborating on various use cases and data analysis.

The Machine Learning Engineer will develop advanced Deep Learning technologies for Computer Vision and SAR, focusing on object detection and model generalization. Responsibilities include researching algorithms, applying signal pre-processing, and optimizing model performance.

Architect, build, and operate ML inference and deployment infrastructure (model serving, orchestration, CI/CD, feature stores). Improve developer experience, optimize GPU/CPU scheduling and performance, and establish ML deployment best practices across Riot’s production ML ecosystem while shaping long-term platform direction.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Build and maintain an ML feature platform: backend systems for feature creation, exploration, serving, data storage and availability, and offline backfilling. Deliver reliable, scalable, well-tested solutions that enable MLEs, analysts, and decisioning teams; debug large codebases and collaborate across engineering to meet availability and latency goals.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

The Machine Learning Engineer will develop ML infrastructure, optimize models for anomaly detection, and manage technical projects to enhance space cybersecurity.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

Develop and productionize deep learning models for computer vision and NLP on medical imaging products. Design and run experiments, document results, collaborate on data labeling, write deployment/docker code, optimize inference speed, and conduct DL methodology research.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.

Drive technical direction and architecture for AI Context systems across HubSpot's CRM: develop, evaluate, productionize, and monitor ML/LLM models; build scalable data processing, retrieval, feature generation, and serving systems; lead cross-team initiatives, mentor engineers, and measure customer and business impact while addressing privacy, bias, security, reliability, cost, and data governance.

Design, build, and operate cloud-native data and ML infrastructure for real-time and batch workloads. Implement reproducible training, evaluation, and inference pipelines; integrate event streams; monitor data quality, model performance, latency, and cost; automate deployments with IaC and CI/CD; collaborate with product, data science, and security teams; produce docs, dashboards, and runbooks.

As a Senior Machine Learning Engineer, you'll design and develop workflows utilizing vision-language models, manage data pipelines, and collaborate with multiple teams to enhance model accuracy and efficiency.

As a Staff Software Engineer, you will lead the development of computer vision systems for sidewalk inspection, overseeing model lifecycle, system architecture, and mentoring while improving deployment practices.

In this role, you will lead AI personalization initiatives, develop AI/ML models for user experience, collaborate across teams, and ensure rigorous development practices.

As a Senior Principal Machine Learning Engineer, you'll lead technical direction, drive scalable model development, and mentor teams in a dynamic environment, optimizing Disney's ad technology.

Lead ML strategy and integrate ML into real-time game systems and toolchains. Design and deploy LLM techniques, build training and inference pipelines for on-device and real-time models, prototype ML-driven gameplay features, analyze telemetry for personalization, and mentor engineers.