Top Machine Learning Jobs in Los Angeles, CA

Lead research at the intersection of AI, cybersecurity, and computer systems. Define research problems, develop hypotheses, build prototypes, design rigorous experiments, evaluate AI and security methods, and translate findings into practical systems. Research areas may include AI agents, autonomous penetration testing, attack-path reasoning, cyber deception, AI safety, and security evaluation. Mentor junior researchers, collaborate with engineering and product teams, and communicate findings through reports, presentations, prototypes, or publications.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Supports the full AI development lifecycle by helping execute pilots and proofs of concept, documenting standards and playbooks, evaluating AI use-case pipeline health, researching tools and platforms, developing acceleration dashboards, supporting value assessments, and preparing leadership updates. The intern will also present a final project and gain experience with responsible AI, governance, and enterprise AI delivery.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Develop and optimize computer vision and machine learning perception algorithms for autonomous detection, classification, and pose estimation of space targets. Responsibilities include model training, evaluation, deployment to resource-constrained systems, simulation integration, sensor modeling, tooling maintenance, and collaboration across autonomy and mission software teams.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Leads the company’s AI-first enterprise and digital technology strategy, including AI governance, enterprise modernization, cloud, data platforms, software development, digital commerce, cybersecurity, and automation. Advises the CEO and board, oversees global technology teams and budgets, drives digital transformation and M&A integration, and builds scalable technology capabilities that improve innovation, productivity, consumer experiences, and profitable growth.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

Design, deploy, and maintain scalable machine learning solutions in production. Build end-to-end ML pipelines, predictive models, forecasting systems, and feature engineering workflows. Analyze large datasets, generate business insights, collaborate with engineering teams, monitor model performance, and deliver production-ready solutions using Python, Snowflake, MLOps, Scikit-learn, Pandas, NumPy, and SQL.

Designs and supports enterprise data platforms, pipelines, models, Power BI reporting, and AI-enabled applications. Develops ETL/ELT integrations, semantic models, dashboards, data governance controls, and scalable analytics solutions. Partners with stakeholders to identify AI use cases, build proofs of concept, and deploy solutions using Python, Azure OpenAI, LLMs, RAG, and AI agents. The role also optimizes performance, establishes responsible AI practices, and supports cloud and hybrid data environments.

Leads machine learning and software engineering teams building models, agentic systems, data pipelines, and ML infrastructure for advertising integrity and recommendations. Owns technical strategy, roadmap, model quality, reliability, privacy, and responsible AI. Partners cross-functionally to deliver measurable outcomes, scales LLM-powered ad review, contributes technically, and hires and mentors high-performing engineers.

Lead growth data science initiatives by partnering with Marketing, Sales, and Customer Experience leaders. Develop analyses, experimentation frameworks, and machine learning models for acquisition, engagement, retention, monetization, targeting, segmentation, uplift modeling, and lifetime value prediction. Productionize reliable models with engineering teams, communicate insights to senior stakeholders, establish experimentation best practices, and mentor junior data scientists. The role requires strong business judgment, advanced technical expertise, and experience operating in ambiguous, zero-to-one environments.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.

Build and own ClickUp’s AI evaluation systems, agent harness, self-improvement loops, model benchmarking, routing, and fallback strategies. Architect scalable backend and AI platform services for LLM deployment, orchestration, monitoring, logging, and automated evaluation. Improve reliability, privacy, security, and compliance while integrating AI features into the core product and collaborating cross-functionally.

Leads advanced analytics, AI, automation, and scientific computing initiatives supporting pharmaceutical clinical development. Translates scientific and business requirements into scalable analytical products, software specifications, decision-support tools, and production automation. Partners across biometrics, clinical development, IT, and business teams; evaluates emerging AI technologies; improves regulated workflows; establishes value metrics; drives adoption and change management; and mentors technical talent.

Conduct research at the intersection of AI, cybersecurity, and computer systems. Formulate research questions, design experiments, build prototypes, evaluate AI and security methods, analyze real-world security data, and communicate findings through reports, presentations, and potential publications. The role combines research with practical engineering and offers mentorship while developing independence.

Build and evolve machine learning tooling and infrastructure for AI-powered clinical solutions, including automated prompt tuning, feedback loops, model benchmarking, experimentation, and evaluation. Partner with data science, clinical, software engineering, and product teams to develop scalable solutions, solve ambiguous technical problems, establish engineering standards, and provide technical mentorship without direct management responsibility.

Leads the enterprise Data Science and AI vision, strategy, roadmap, and global organization. Oversees production-grade machine learning, Generative AI, Agentic AI, predictive modeling, AI governance, deployment, monitoring, cost optimization, and responsible AI. Partners with executive, product, engineering, commercial, legal, operations, and customer success stakeholders to deliver scalable AI products, automation, improved customer outcomes, and measurable business value.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.