Top Machine Learning Jobs in Los Angeles, CA

Lead technical direction and deliver large-scale ML platform infrastructure including feature store, model serving, experimentation, and training pipelines. Drive architecture, mentor engineers, coordinate cross-team initiatives, resolve system-wide issues (latency, reliability, GPU utilization), and define safe ML deployment paths from registration through rollout, monitoring, and rollback.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Lead development and operationalization of a clinical reasoning framework for trustworthy clinical AI. Partner with product, engineering, editorial, and design to define decision pathways, evaluation benchmarks, validation protocols, and ongoing expert review. Translate clinical feedback into AI system improvements, represent the team externally, and optionally maintain limited clinical practice.

Lead portfolio-level Data & AI strategy and technical governance for enterprise clients. Advise C-suite, drive architecture and modern data stacks, win high-value engagements, and scale engineering and data science teams.

Join Trexquant as a Global Alpha Researcher to develop trading signals, implement research, and apply machine learning for alpha discovery.

As a Forward Deployed Machine Learning Engineer, you'll build and deploy machine learning models, enhance system performance, and collaborate with Data Science and Engineering teams for effective solutions.

Lead end-to-end deployments of AI for hotel revenue teams: integrate PMS/CRS/RMS, ship ML/optimization models and agentic workflows, validate recommendations with revenue managers, define metrics, drive adoption, and hire/mentor the forward deployed engineering team.

Design, build, and ship production AI agents integrated end-to-end into a TypeScript/Node.js/React stack. Apply ML judgment to product decisions, improve agent reliability and evaluations, and move quickly in a startup environment owning ambiguous problems.

Oversee global analytics and algorithm design initiatives for a nonprofit: identify high-impact projects, coordinate with student directors, collect feedback, design and run training for developers and technical advisors, and form partnerships to support implementation. Volunteer commitment ~5-6 hours/week.

Contribute to development, deployment, and monitoring of production ML models; perform data cleaning, analysis, and visualization; collaborate with cross-functional teams to deliver AI-driven business solutions and communicate insights to stakeholders.

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Embed with clinical and operations teams to identify high-impact problems and build production AI systems. Design and deploy LLM/RAG-driven tools, data pipelines, APIs, models, and user-facing applications using messy clinical and claims data. Iterate quickly based on real-world feedback and operational needs.

Lead GenAI strategy and research to develop scalable LLM and agentic AI applications (e.g., conversational assistants). Analyze data to personalize investment outcomes, collaborate with engineers, productionize models, ensure best practices in data management and evaluation, publish research, and mentor applied scientists.

Design, build, and ship production-grade AI and data solutions including LLM-powered applications, ML models, and APIs. Consult on architecture, own features end-to-end, support production, run design-thinking workshops, and collaborate across teams to deliver cloud-based, scalable data and AI capabilities.

Design, build, and ship production-grade AI and data solutions (LLM apps, ML models, APIs). Consult on architecture, own end-to-end features, support deployments, run design workshops, and collaborate cross-functionally to deliver cloud-based data and AI systems.

Design, build, and ship production-grade AI and data solutions (LLM apps, ML models, APIs). Consult on architecture, own features end-to-end, apply cloud and data engineering, lead design thinking workshops, and collaborate across teams to deliver measurable business impact.

Design, train, and productionize underwriting ML models for real-time transaction decisions. Build feature pipelines and training datasets, run experiments, integrate models into batch/real-time systems, and monitor data and model health while collaborating with cross-functional teams.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

The role involves developing and improving TTS/Voice Generation models, training, fine-tuning, evaluating speech models, and collaborating across research, engineering, and product teams in a fast-paced startup environment.

The AI Engineer will design and build production systems, focusing on LLM integration, system reliability, evaluation frameworks, and high-quality product features while maintaining strong engineering practices.

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.

The Senior AI Data Engineer will design and build scalable data pipelines, ensure data quality, and support AI product development through a unified analytical data framework.

The role involves building predictive models for blockchain performance and user behavior, implementing solutions, and owning production prediction pipelines.

Drive new business and expand accounts by selling AI-driven services and software to engineering and manufacturing customers. Manage full sales cycle from prospecting to close, align solutions to measurable ROI, collaborate with technical and marketing teams, and meet revenue targets while attending industry events and maintaining CRM pipeline accuracy.