Top Machine Learning Jobs in Los Angeles, CA

Leads client-facing applied AI engineering engagements in healthcare, from solution design through deployment. Translates clinical and health services needs into AI requirements, architectures, and roadmaps; designs RAG, memory, prompt, and agent orchestration components; guides data, model integration, and cloud implementation; evaluates model performance; advises on AI platforms and governance; and develops LLMOps and AgentOps practices. Coaches teams, resolves delivery challenges, and communicates technical decisions to clients and leaders.

Supports client cloud modernization engagements by designing and building Google Cloud data and generative AI solutions. Develops Python workflows for prompt engineering, retrieval-augmented generation, agent orchestration, and LLM evaluation. Configures Vertex AI, LLM APIs, vector databases, guardrails, and LLMOps processes while applying machine learning and NLP concepts. Collaborates on data platform and analytics modernization, troubleshooting, research, solution design, documentation, and iterative improvement with senior staff.

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.

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

Lead teams building Samsara Assistant, Agent Studio, and agent extensibility. Own product growth, activation, answer quality, trust, repeat usage, and proactive agent capabilities. Set technical direction for an AI platform used by customers and internal product teams, partner across Safety and Maintenance, and lead approximately 10 machine learning, applied science, and full-stack engineers through two tech leads. Hire, develop, and manage a high-performing engineering organization.

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.

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.

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 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 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.

Paid summer internship in NBCUniversal’s Media Tech program, focused on data, AI, and machine learning initiatives. Interns may support content research and measurement, data products, AI and data engineering, SAP data foundations, forecasting analysis, and enterprise AI solutions. The role offers hands-on technical experience, mentorship, networking, and career development in Universal City, California, with a 40-hour weekly commitment.

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.

Leads Riot’s AI Foundations engineering organization, setting technical strategy, roadmap, architecture, and investment priorities. Oversees engineering managers and multiple teams, develops organizational capabilities, drives hiring and performance management, and ensures reliable, secure, scalable AI/ML and software systems. Partners with product, game, platform, data, research, and executive leaders while advancing responsible AI, operational excellence, technical quality, and reusable AI platform capabilities across Riot.

Lead the Hardware Intelligence research agenda, developing AI agents that reason over hardware data, operate engineering tools, connect simulations with physical tests, and coordinate across systems. Build RL environments, verifiers, and post-training methods; deploy models on-device, on-premises, and in air-gapped environments; establish benchmarks; and hire and mentor researchers. The role requires setting technical direction, conducting statistically rigorous experiments, and delivering research that improves shipped products.

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, 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.