Top Machine Learning Engineer Jobs in Los Angeles, CA

As a Machine Learning Engineer, you will build and deploy models for Snapchat, applying modern ML techniques to solve problems and lead the full ML lifecycle.

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.

Lead vision and roadmap for large-scale recommender systems across Spotlight, Discover, and Friend Stories. Technically lead engineering teams, design next‑generation recommender and retrieval architectures, incorporate SOTA research and foundation models, advance ML infrastructure for scalability and reliability, and partner cross-functionally to drive experimentation, operational excellence, and cost-effective production deployments.

Design and implement generative ML systems (image, video, audio, multimodal LLMs) and deliver on-device and server-side inference. Build GenAI pipelines and AR experiences, prototype with cross-functional teams, and optimize efficient models for real-time mobile and wearable applications.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

As a Machine Learning Engineer at True Anomaly, you'll design and develop ML capabilities, assist with model development, and support data pipelines while collaborating with senior engineers in a mission-driven environment.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Develop large-scale Foundation Models for Autonomous Driving & Robotics, collaborating with teams, optimizing model performance, and mentoring junior members.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

Design and implement ML solutions for wireless communications using SDR-captured RF data. Combine signal processing, statistics, and ML to build prototypes and scalable systems from edge devices to cloud, collaborating with UX and infrastructure teams.

Build, deploy, and optimize production AI/ML and LLM-driven decisioning systems for claim offer generation and negotiation. Own end-to-end model development, evaluation, experimentation, and operational reliability while balancing fairness, business objectives, and product requirements.

Define and lead modeling architecture for personalization, matchmaking, recommendations, and social discovery. Build large-scale real-time inference systems, develop models for skill, behavior, trust & safety, and multi-objective optimization. Drive Responsible AI, experimentation, observability, and cross-functional standards while mentoring senior engineers and influencing ML strategy.