Top Machine Learning Jobs in Los Angeles, CA

Build and own production Vision AI systems end to end, including model selection, inference pipelines, evaluation, monitoring, and customer-facing features. Shape architecture, collaborate with product, design, and engineering teams, and use AI coding tools to accelerate development. Establish high standards for quality, testing, reliability, security, and privacy while mentoring engineers and taking ambiguous ideas from prototype to production.

Lead AI and autonomous vehicle behavior safety strategy for Level 3–4 automated driving systems. Own behavior safety claims, evidence criteria, validation methodologies, performance indicators, and launch-readiness assurance. Evaluate ML-driven AV performance using simulation, closed-course, and public-road data. Define safety metrics and strategies for imitation and reinforcement learning systems, interpret applicable standards, and influence cross-functional engineering teams and executives.

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.

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 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.

Advises clients on Responsible and Ethical AI policy, standards, regulations, governance, operating models, and controls. Leads consulting projects, supports sales and proposals, monitors AI regulatory developments, advises senior stakeholders, and represents organizational perspectives with clients and regulators. The role also serves as a Responsible AI subject matter expert within data science and transformation teams.

Develop and deploy AI/ML and Generative AI capabilities within enterprise systems. Build scalable APIs, microservices, middleware, data pipelines, intelligent monitoring, anomaly detection, and predictive workflows using Python, Node.js, MuleSoft, and cloud platforms. Collaborate with business, data science, ERP, and IT teams to design, test, deploy, troubleshoot, and support production software solutions. Participate in SDLC processes, technical design, documentation, project coordination, and support rotations.

Develop, train, test, deploy, and maintain machine learning models for mission-critical defense solutions. Apply ML algorithms and technologies, integrate LLMs and agentic workflows, guide ML strategy, and collaborate with clients and engineering teams. The role involves solving complex problems involving deep learning, computer vision, generative AI, cloud platforms, and production-grade model deployment.

Develop predictive models and metrics supporting player evaluation, roster construction, player development, and in-game strategy. Responsibilities include exploratory analysis, model validation and deployment, large-scale data processing, research integration, vendor data evaluation, and translating analytical results into basketball recommendations. The role collaborates with software development and data engineering teams to build applications and maintain production solutions.

Develops advanced machine learning and deep learning models for computer vision, synthetic aperture radar, and geospatial exploitation. Responsibilities include designing neural network architectures, training detection and classification models, preprocessing SAR data, creating physics-based simulations, evaluating model performance, and delivering production-quality code. Higher levels independently deploy and monitor models, make architecture decisions, mentor engineers, and provide technical leadership. The position requires eligibility for a U.S. government security clearance and U.S. Person status under export regulations.

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.

Architect and build Toast’s Vision AI platform, including video ingestion, processing, storage, inference pipelines, evaluation, and monitoring. Develop production ML services and customer-facing computer vision features, make architecture decisions, write significant code, and guide technical direction. Partner across teams, mentor engineers, establish reliability, security, and privacy standards, and promote AI-assisted development. The role requires deep distributed-systems and cloud expertise, production ML experience, video familiarity, and the ability to take ambiguous ideas from prototype to customer-ready products.

Designs, builds, and operates shared machine learning infrastructure, including feature stores, model hosting, inference, training, evaluation, and rules engines. Leads large-scale distributed systems, reliability improvements, incident response, and safe deployments. Partners with applied ML teams to develop reusable platform capabilities, supports internal users, mentors engineers, and contributes to technical roadmaps and design reviews.

Apply established machine learning techniques—including clustering, classification, and anomaly detection—to ICS/OT cybersecurity data. Integrate model outputs into batch and near-real-time pipelines, evaluate third-party models, maintain reliable data contracts and observability, and troubleshoot production issues such as data drift and output quality. Collaborate with AI engineers, data engineers, and product teams while clearly communicating model behavior, uncertainty, and downstream use.

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.