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.

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.

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.

The Senior Data Scientist supports Customer Success through deep-dive analysis, predictive modeling, causal inference, experimentation, and KPI development. The role analyzes churn, retention, renewals, upsell opportunities, and revenue trends; diagnoses metric shifts; builds data pipelines and dashboards; and develops AI/LLM-powered solutions. The scientist collaborates with stakeholders, communicates insights, improves data quality, and measures the impact of customer success initiatives and product changes.

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.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

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.

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.

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.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

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.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Leads teams developing scalable knowledge graph, semantic layer, generative AI, and machine learning solutions for enterprise clients. Designs ontologies, data architectures, AI pipelines, and end-to-end semantic applications; evaluates emerging LLM, multimodal, and agentic technologies; and demonstrates business value to senior stakeholders. The role includes client relationship management, consulting, pre-sales support, team development, and technology leadership across data and AI transformation programs.

Defines enterprise AI and machine learning architectures spanning Generative AI, agentic AI, data, applications, cloud, infrastructure, and enterprise systems. Translates business requirements into architecture blueprints, technical strategies, and implementation roadmaps; develops reusable standards and design patterns; evaluates technology alternatives; and leads technical reviews. The role also establishes patterns for MLOps, model serving, monitoring, governance, security, and deployment across cloud, hybrid, on-premises, and restricted environments while mentoring engineering teams.

Design, build, evaluate, and deploy production AI and ML solutions, including generative AI, large language models, retrieval-augmented generation, and agentic workflows. Develop reliable software, APIs, integrations, evaluations, and observability capabilities while partnering with architecture, platform, data, cybersecurity, and business teams. Apply AI engineering practices across enterprise, cloud, hybrid, and regulated environments.

Develop, validate, optimize, and deploy EO/IR image-processing and computer vision algorithms for space-based sensor payloads. Build image-system modeling and simulation frameworks, synthesize imagery for machine learning and validation, detect and track low-SNR objects, and transition algorithms to real-time software and firmware implementations. Analyze datasets, troubleshoot deployed systems, document performance, support technical milestones, and present results to government and technical stakeholders.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Develop, optimize, validate, and deploy EO/IR image-processing algorithms for real-time space-based sensor payloads. Build sensor modeling and simulation frameworks, synthesize imagery for machine-learning training, and apply classical and deep-learning methods to detect and track low-SNR objects. Analyze datasets, troubleshoot deployed systems, transition algorithms into software and firmware implementations, document performance, present results, and support technical milestones and customer meetings.

Architects and scales enterprise-grade agentic AI and generative AI platforms across public, private, and sovereign clouds. Leads client workshops, defines AI strategies, selects technologies, develops reference architectures, and designs knowledge, security, governance, orchestration, and observability layers. Guides engineering teams from ideation through production, establishes reusable agent frameworks, and manages AgentOps, LLMOps, MLOps, and AI governance while optimizing system performance, cost, reliability, and trust.

Develops, trains, tests, deploys, and maintains production computer vision and machine learning models for mission-critical applications. Responsibilities include deep learning, transformer-based vision models, sensor fusion, target tracking, GPU optimization, embedded systems, and real-time missile defense software. The engineer collaborates with multidisciplinary space, military, intelligence, engineering, and AI teams to design and implement robust operational capabilities.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.