Top Machine Learning Jobs in Los Angeles, CA

Leads experimentation, causal inference, advanced statistical analysis, machine learning, and scalable data-product development. Partners with product, engineering, and research teams to generate insights, identify opportunities, influence executive decisions, and shape product strategy. Owns cross-functional projects, analytics roadmaps, reporting, stakeholder adoption, and methodological standards while translating complex data into actionable business outcomes.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Leads New York Life’s AI Center of Excellence across frontier AI provider relationships, AI software lifecycle standards, platform architecture, governance integration, talent acquisition, upskilling, and production delivery. Builds and manages West Coast AI engineering teams, evaluates emerging AI technologies, establishes scalable governance and engineering practices, and owns measurable business outcomes from enterprise AI solutions. Provides senior leadership coverage for West Coast partners and coordinates with Home Office leadership.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Leads data engineers, data scientists, analytics/ML engineers, and BI analysts while owning enterprise data platform architecture and modernization. Establishes DevSecOps practices, governance, compliance, automated testing, CI/CD, infrastructure as code, and operational documentation. Translates business needs into scalable real-time and batch solutions, oversees technical roadmaps involving Synapse and Purview, manages suppliers, and develops team members. The role is onsite in El Segundo, California, and requires U.S. citizenship with the ability to obtain a Secret clearance.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Build and optimize Snap’s machine learning infrastructure, including scalable training, evaluation, inference, feature serving, data management, vector search, and model deployment systems. Improve reliability, performance, and cost efficiency for large-scale ML workloads while collaborating with ML engineers. The role requires strong programming, distributed systems, big data, cloud infrastructure, and production machine learning experience.

Design and productionize causal machine learning models, including uplift modeling and treatment-effect estimation. Analyze A/B tests and quasi-experiments, develop experimentation strategies, evaluate modeling tradeoffs, and build scalable infrastructure. Collaborate with product and engineering teams, conduct code reviews, maintain engineering standards, communicate technical insights, and mentor others.

Develops and maintains prompts, responses, guidelines, and datasets for generative AI and large language model tools. Leads labeling initiatives with internal teams and third-party firms, trains stakeholders on LLM and dataset best practices, and supports quality assurance and testing. Requires native-level English, bilingual ability in a specified language, specialized domain knowledge, and a college degree or equivalent experience.

Leads and develops a team of autonomy engineers building multi-agent resource allocation, planning, coordination, and edge AI systems for spacecraft. Provides technical oversight on optimization, reinforcement learning, validation, architecture, and constrained real-time deployment. Owns team roadmaps, delivery, cross-functional dependencies, process improvement, and stakeholder communication while managing hiring, performance, career development, and team culture. The role bridges autonomy expertise with spacecraft constraints including compute, latency, power, and communication limitations.

Own product vision and AI strategy for internal catalog management and content-understanding tools. Lead roadmap, incubate ML/LLM capabilities, collaborate with researchers and engineers, translate AI advances into scalable internal products, and measure impact through hypothesis-driven evaluation.

Leads the end-to-end design, deployment, and performance of real-time ML prediction and bid-optimization systems for programmatic advertising. Provides hands-on technical leadership to applied scientists and ML engineers, formulates optimization problems, manages production ML challenges such as latency, delayed feedback, drift, and retraining, and aligns technical roadmaps with business priorities.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Lead advanced analytics initiatives by translating ambiguous business problems into analytical solutions, building and deploying predictive and machine learning models, and delivering actionable insights. Partner with stakeholders across sales, finance, supply chain, eCommerce, cloud, and vendor management. Independently scope workstreams, shape technical roadmaps, lead cross-functional collaboration, and communicate complex findings through executive-ready presentations.