Top Machine Learning Jobs in Los Angeles, CA

Designs and builds large-scale clinical, claims, and document data platforms on Google Cloud. Leads architecture, production engineering, AI-assisted development and operations, including agent guardrails, observability, automation, auditability, and regulated-environment controls. Develops AI and machine learning systems involving retrieval, extraction, evaluation, and serving. Partners across engineering, security, privacy, compliance, and governance teams while communicating technical strategies to executives and customers.

Lead the Machine Learning Ranking team within ZipRecruiter’s Marketplace organization. Manage and develop senior data scientists and machine learning engineers, shape product roadmaps with Product and Decision Science, guide technical decisions, build and deploy ML pipelines, and monitor model speed and accuracy. The role requires strong software engineering leadership, ML expertise, coding ability, and experience applying embeddings, reproducible pipelines, and established ML frameworks to business problems.

Build and deploy production AI/ML systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, cost, lead-time, supplier-risk, anomaly-detection, and cash-flow modeling; real-time inference pipelines; MLOps; data infrastructure; governance; and cross-functional AI adoption. The role also sets the supply-chain AI roadmap, mentors junior scientists, establishes modeling standards, and partners with Planning, Procurement, Finance, Engineering, and Operations.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Develop advanced statistical and machine-learning solutions for fraud and other data-intensive business problems. Translate ambiguous needs into hypotheses, experiments, models, and prototypes; shape data strategies; evaluate emerging generative and agentic AI technologies; and partner with Product, Engineering, Analytics, and business teams to operationalize solutions. The role also includes mentoring junior team members and fostering experimentation and innovation.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Designs and optimizes scalable data pipelines, machine learning models, and AI-enabled enterprise applications. Builds agentic systems, chatbots, autonomous agents, and automation tools; integrates data and AI solutions with business platforms; and ensures data quality, security, compliance, and governance. The role requires Python, SQL, cloud data platforms, modern pipeline tools, MLOps, API integration, and collaborative delivery of reliable end-to-end solutions.

Own the design, development, integration, release, and reliability of tailored AI solutions for enterprise clients. Adapt a core AI platform, deliver client-specific changes to a shared codebase, manage production rollouts, diagnose complex issues, and serve as the primary technical contact for client engineers. The role requires strong Python, software architecture, system design, and experience deploying LLM or agent-based systems used by customers in live environments.

Labels video, image, and metadata content across social media platforms to create ground-truth data for machine learning systems. Applies contextual and cultural guidelines, handles ambiguous or sensitive content, identifies trends and edge cases, performs linguistic quality assurance, analyzes quality misses, and recommends process improvements. Requires Spanish and English fluency, strong attention to detail, familiarity with major social platforms, clear communication, and comfort using AI tools and Google Workspace.

Leads enterprise Data and AI strategy, use-case prioritization, governance, risk assessment, solution delivery, performance monitoring, and adoption. Partners with Finance, IT, Legal, Compliance, Privacy, Security, and business stakeholders to develop responsible AI and analytics solutions. Provides technical guidance on machine learning, generative AI, data requirements, prompt design, model selection, testing, and implementation. Creates training, communications, and reusable assets to improve AI literacy and adoption.

Leads end-to-end advanced analytics projects supporting regulatory operations and cross-market surveillance. Defines quantitative approaches, develops predictive pipelines, drives data and infrastructure availability, and delivers analytics capabilities across teams. Engages stakeholders, gathers use cases, provides research and insights, mentors analysts, and advances enterprise analytics strategy. Requires strong programming, machine learning, data mining, and analytical skills in a hybrid environment with occasional travel.

Build and operate secure, scalable production infrastructure for agentic AI platforms across AWS and Azure. Responsibilities include CI/CD automation, infrastructure as code, cloud security, governance, monitoring, observability, model lifecycle management, drift detection, incident response, networking, resilience, and cost optimization. The role establishes production-readiness standards, supports AI deployments, and partners with engineering, product, security, compliance, and business teams.

Research and prototype AI-powered tools for music generation, transformation, remixing, and audio understanding. Build and evaluate machine learning models using Python and modern frameworks, process audio datasets, assess model performance and musical quality, and collaborate with engineering, music technology, product, and creative teams. The intern will also document findings and contribute to responsible AI practices involving artist consent, music rights, and ethical data use.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Build and operate a scalable LLM inference platform integrated with the MAX stack. Develop distributed, multi-node model deployments, high-throughput batching, KV-cache management, autoscaling, observability, cold-start optimizations, and multi-cloud capabilities. Create Helm charts and Kubernetes operators while collaborating with kernel and generative AI teams to optimize performance. The role requires backend engineering, Kubernetes operations, machine learning experience, and distributed systems expertise.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing machine learning algorithms and systems, and delivering analytical products and dashboards. Works with clients and stakeholders to define needs, communicate development plans, brief senior leadership, and provide data-driven recommendations. Mentors team members, identifies opportunities for data science solutions, and manages competing priorities in complex environments.

Build and improve machine learning models that identify healthcare claims errors and fraud for audit. Responsibilities include curating labeled data, engineering features, selecting optimization metrics, measuring model and business impact, refining large-scale data pipelines, and developing modeling infrastructure and team tools. The role also involves collaborating with clients and healthcare experts to align insights with business needs.

Develops data science, machine learning, and generative AI solutions for customer engagements and pre-sales opportunities. Translates ambiguous business challenges into analytical use cases, prototypes, hypotheses, demonstrations, and actionable recommendations. Explores customer data, applies statistical and advanced analytical techniques, communicates findings to technical and business audiences, and creates reusable analytics assets. Partners across Microsoft teams to connect customer data and AI opportunities with broader transformation priorities while applying responsible AI, security, governance, and data quality principles.

Conduct machine learning and data science research for drug discovery, focusing on mass spectrometry, computational biology, and computational chemistry. Own projects from scientific question through results, develop data-driven solutions, validate AI-generated analyses, and communicate findings through visualizations and writing. Collaborate with the Data Science team and contribute to applications of AI in medicine development.

Build and deploy production machine-learning models for member identification, personalization, provider search, and healthcare AI tools. Lead projects from problem framing and prototyping through product handoff, design experimentation and causal analysis, define product metrics, and develop reliable feature pipelines and experiment infrastructure. Partner with product, engineering, care operations, marketing, and analytics engineering while communicating recommendations clearly and ensuring models are monitored, tested, and impactful.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, and end-to-end ML pipelines. Define AI/ML use cases, transition roadmaps, cognitive adoption metrics, and data-mining strategies. Participate in SAFe Agile delivery, write user stories, remove technical impediments, support DevSecOps integration, and produce transition and knowledge-transfer materials.

Leads the design, deployment, and operation of production AI/ML and LLM systems. Responsibilities include building data and ML pipelines, managing model registries and feature stores, developing CI/CD and experimentation frameworks, operating cloud training infrastructure, implementing observability and guardrails, and ensuring security, compliance, reliability, and performance. The role also guides architecture, incident response, governance, and cross-functional collaboration while mentoring engineers and transitioning models from research to production.

Develop and productionize large-scale machine learning models, ranking algorithms, data pipelines, and real-time or batch systems for Airbnb search and recommendation. Collaborate with engineering, product, operations, and data science teams to prioritize opportunities, drive technical decisions, and measure business impact. Build scalable ML infrastructure supporting feature engineering, model serving, experimentation, interpretability, optimization, and concept drift detection.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.