Top Machine Learning Jobs in Los Angeles, CA

Leads Gibson Dunn’s firmwide legal innovation and AI strategy, including AI deployment, vendor portfolio management, custom solutions, governance, risk, training, adoption, and executive reporting. Oversees generative AI tools, RAG pipelines, integrations, evaluation standards, responsible AI frameworks, and multidisciplinary teams. Advises firm leadership on competitiveness, pricing, talent, client expectations, and regulatory obligations while building the firm’s internal AI capabilities and market presence.

Leads Torchlight’s creative technology and VFX strategy, partnering with filmmakers, executives, engineers, artists, and production teams. Drives adoption of AI, machine learning, virtual production, visual effects, real-time rendering, simulation, and other emerging technologies. Oversees experimentation, prototyping, production deployment, workflow innovation, VFX partnerships, and enterprise creative transformation while maintaining artistic standards and production feasibility.

Design, develop, test, debug, document, and deploy advanced AI/ML applications. Collaborate with AI/ML and software engineers to deliver solutions in development and production environments, develop software and testing requirements, and support rapid prototyping and demonstrations. The role involves LLMs, NLP, computer vision, sensor fusion, explainable AI, and responsible AI for advanced aerospace and national security systems.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Leads AI and machine learning engineering for federal and commercial digital transformation initiatives. Assesses support and contact center environments, designs AI-enabled operating models, develops modernization roadmaps, builds prototypes, creates ROI models, and advises executive stakeholders. The role includes architecture, workshops, client consulting, proposal support, business development, and implementation of conversational AI, automation, virtual agents, LLM orchestration, agentic AI, and workflow optimization solutions.

Defines enterprise AI and machine learning architectures spanning Generative AI, agentic AI, data, applications, cloud, infrastructure, and enterprise systems. Translates business requirements into architecture blueprints, technical strategies, and implementation roadmaps; develops reusable standards and design patterns; evaluates technology alternatives; and leads technical reviews. The role also establishes patterns for MLOps, model serving, monitoring, governance, security, and deployment across cloud, hybrid, on-premises, and restricted environments while mentoring engineering teams.

Design, build, evaluate, and deploy production AI and ML solutions, including generative AI, large language models, retrieval-augmented generation, and agentic workflows. Develop reliable software, APIs, integrations, evaluations, and observability capabilities while partnering with architecture, platform, data, cybersecurity, and business teams. Apply AI engineering practices across enterprise, cloud, hybrid, and regulated environments.

Develop, validate, optimize, and deploy EO/IR image-processing and computer vision algorithms for space-based sensor payloads. Build image-system modeling and simulation frameworks, synthesize imagery for machine learning and validation, detect and track low-SNR objects, and transition algorithms to real-time software and firmware implementations. Analyze datasets, troubleshoot deployed systems, document performance, support technical milestones, and present results to government and technical stakeholders.

Develop, optimize, validate, and deploy EO/IR image-processing algorithms for real-time space-based sensor payloads. Build sensor modeling and simulation frameworks, synthesize imagery for machine-learning training, and apply classical and deep-learning methods to detect and track low-SNR objects. Analyze datasets, troubleshoot deployed systems, transition algorithms into software and firmware implementations, document performance, present results, and support technical milestones and customer meetings.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Develop fraud detection models and predictive features for account opening, account takeover, and identity risk. Responsibilities include investigating large datasets, identifying fraud patterns, engineering features, training and evaluating machine learning models, writing tested Python and PySpark code, supporting production deployment, monitoring model performance and drift, and presenting analytical recommendations. The role also requires adherence to data privacy, explainability, model validation, documentation, and governance standards.

Build and evaluate machine learning models and features for fraud detection across account opening, account takeover, and identity risk. Analyze complex datasets to identify fraud patterns, develop hypotheses, validate models, and measure technical and business performance. Write tested Python code, support production deployment and monitoring, communicate findings, and follow standards for privacy, explainability, validation, documentation, and governance.

Owns the backlog, sprint cadence, releases, and production health of enterprise AI products. Translates product strategy into requirements, user stories, work packages, and sprint goals while coordinating engineers, data scientists, business partners, and dependent teams. Leads Scrum ceremonies, PI planning, testing, release communications, and uptime monitoring. Applies AI tooling to improve delivery and uses technical fluency to prototype, review pull requests, and support responsible AI implementation.

Lead and grow a team of Conversation Designers to build and scale conversational AI experiences. Partner with Product, Engineering, Analytics, and ML teams to drive strategy, tooling, evaluation, and quality practices. Recruit and remove roadblocks, own strategic initiatives, and remain hands-on in designing, testing, and improving AI-driven conversational products.

Designs, develops, trains, tests, and deploys secure AI and machine learning solutions for enterprise and government environments. Responsibilities include preparing datasets, identifying automation opportunities, evaluating model performance and security, documenting risks and controls, and collaborating with data scientists, software developers, cybersecurity engineers, architects, and domain experts. The role applies AI and ML to enterprise security engineering, testing, and mission workflows.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Develops, trains, tests, deploys, and maintains production computer vision and machine learning models for mission-critical applications. Responsibilities include deep learning, transformer-based vision models, sensor fusion, target tracking, GPU optimization, embedded systems, and real-time missile defense software. The engineer collaborates with multidisciplinary space, military, intelligence, engineering, and AI teams to design and implement robust operational capabilities.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Apply machine learning, artificial intelligence, statistical analysis, and causal inference to large datasets and product analytics. Develop insights and recommendations for recommendation systems and user experiences. Evaluate experiments through planning, monitoring, and post-analysis, build data pipelines and tools, and communicate complex findings to stakeholders to support business decisions.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Conduct research on frontier AI systems and develop datasets, experiments, prototypes, tooling, evaluation frameworks, and reinforcement learning environments. Investigate synthetic and agentic data generation, post-training, model understanding, benchmarks, and evaluation. Collaborate with research, engineering, product, and operations teams to translate findings into scalable AI applications, while contributing to technical publications, open-source work, mentorship, and the broader AI research community.