Top Machine Learning Jobs in Los Angeles, CA

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Leads the Safety AI organization and architecture for on-device and cloud-based computer vision and machine learning systems. Oversees sensor fusion, model lifecycle management, platform architecture, experimentation, deployment, monitoring, and continuous improvement across more than two million dashcams. Partners with product, firmware, full-stack, and platform teams to deliver reliable, low-latency safety features. Builds and develops teams of applied scientists and engineers while driving measurable improvements in driver safety and customer outcomes.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Leads a multidisciplinary data science and data engineering team supporting Finance. Sets strategy, prioritizes initiatives, guides analytical models and data architecture, advances AI and machine learning applications, improves data quality and reliability, establishes scalable operating processes, and communicates technical recommendations to Finance and executive stakeholders. The role also manages talent, oversees forecasting and decision-support outcomes, and drives measurable business impact.

Leads GitLab’s Product, Sales, and Finance Data Science teams, managing managers and a 12-person organization. Sets data science standards, prioritizes investments, partners with executives, drives product adoption and revenue growth, and oversees experimentation, causal inference, predictive modeling, and AI initiatives. Develops talent, builds succession, and collaborates with Analytics Engineering and Data Engineering on a shared data and AI roadmap.

Owns the full lifecycle of enterprise AI agent products, from discovering business use cases through deployment and optimization. Partners with engineering, product, program, and business teams to build scalable multi-agent workflows, LLM and RAG solutions, evaluation frameworks, analytics capabilities, and production integrations. Translates ambiguous problems into product requirements, measures adoption and quality, communicates technical concepts, and enables business teams to operate and customize deployed AI solutions.

Develops statistical and machine-learning models for pricing, revenue optimization, demand forecasting, segmentation, and experimentation. Prepares large datasets using Python or R and SQL, validates and documents models, and partners with data engineering teams on scalable pipelines and production deployment. Translates analytical findings into pricing recommendations, dashboards, and executive insights while supporting governance, monitoring, and analyst enablement.

Build and deploy production-grade AI and machine learning solutions for marketing and sales, including LLM agents, data-quality pipelines, semantic search, RAG, analytics, and integrations with Salesforce, Marketo, and Adobe platforms. Define evaluation and governance practices, monitor AI services, implement privacy controls, and collaborate with platform teams. Train and mentor technical and business stakeholders on responsible AI adoption while supporting DevOps, incident response, and operational runbooks.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Leads Snap’s Content Machine Learning Engineering organization, setting multi-year strategy for recommendation, ranking, content understanding, reliability, and experimentation. Oversees distributed teams, develops organizational roadmaps, partners with executives and cross-functional leaders, influences scalable architecture decisions, and builds high-performing engineering teams. The role requires strong expertise in machine learning, deep learning, AI, ranking systems, and large-scale engineering leadership.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Leads CDW’s AI advisory consulting portfolio, including strategy, governance, operating models, adoption, prioritization, and roadmaps. Develops consulting methodologies, delivery frameworks, sales enablement, and thought leadership. Advises executives and customers, leads complex presales pursuits, shapes proposals and business cases, oversees delivery quality and risk, evaluates technical feasibility, and mentors consulting and presales teams.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Designs, scales, and leads machine learning systems that detect harmful content and bad actors, support content moderation, and promote platform integrity. Provides technical direction across the ML organization, partners with cross-functional leadership, improves ML infrastructure and operational excellence, and delivers performant, scalable models using modern deep learning techniques.

Build evaluation frameworks, datasets, sandbox environments, and metrics for production AI agents. Design experiments to compare models, tools, harnesses, and execution environments; investigate failures; and improve agent reliability, performance, and cost. Contribute to agent infrastructure, reusable knowledge systems, internal tooling, and integrations while collaborating with research, engineering, infrastructure, security, and business teams. This applied research role combines production-quality software engineering with open-ended experimentation.

Leads the design and implementation of AI, knowledge graph, semantic layer, ontology, and generative AI solutions. Translates business problems into scalable architectures, develops data pipelines and graph-based machine learning solutions, evaluates emerging LLM and agentic technologies, and collaborates with technical and business stakeholders. The role also includes client engagement, business-case development, sales support, team leadership, and advising clients on data and AI innovation.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

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 scale production machine learning solutions for lottery fulfillment. Develop and optimize computer vision and multimodal models, fine-tune and evaluate models, create data pipelines and evaluation frameworks, and deploy models on cloud infrastructure. Integrate models into real-time systems involving image processing, barcode decoding, and OCR. Collaborate cross-functionally, improve engineering standards, mentor teammates, and support reliable nationwide expansion.

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.

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.