Top Senior Machine Learning Jobs in Los Angeles, CA

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.

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.

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 Machine Learning Ranking team within ZipRecruiter’s Marketplace organization. Manage and develop senior data scientists and machine learning engineers, shape product roadmaps with Product and Decision Science, guide technical decisions, build and deploy ML pipelines, and monitor model speed and accuracy. The role requires strong software engineering leadership, ML expertise, coding ability, and experience applying embeddings, reproducible pipelines, and established ML frameworks to business problems.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Develop and productionize large-scale machine learning models, ranking algorithms, data pipelines, and real-time or batch systems for Airbnb search and recommendation. Collaborate with engineering, product, operations, and data science teams to prioritize opportunities, drive technical decisions, and measure business impact. Build scalable ML infrastructure supporting feature engineering, model serving, experimentation, interpretability, optimization, and concept drift detection.

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.

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.

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 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.

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.

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.

Lead the architecture and development direction of NVIDIA ALCHEMI and related AI software for chemistry and materials science. Design developer tools, APIs, and workflows; translate advances in machine learning, generative modeling, interatomic potentials, and quantum chemistry into product capabilities; coordinate researchers, engineers, product teams, partners, and customers; and represent NVIDIA at scientific and industry events.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Lead production machine learning and AI engineering initiatives from data assessment through deployment, evaluation, monitoring, and continuous improvement. Build reliable LLM workflows, data and feature pipelines, model-serving systems, and MLOps infrastructure across forecasting, recommendations, search, marketing, artist intelligence, and ecosystem integrity. Partner with technical and business stakeholders, establish evaluation and observability practices, mentor engineers, and deliver scalable, cost-effective decision-support capabilities.

Lead the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

Leads modeling strategy for Riot’s anti-cheat products, designing adaptive classifiers and anomaly-detection systems that identify bots, cheaters, and behavioral threats. Builds production-ready ML pipelines, prototypes novel approaches, establishes evaluation standards, improves feature and architecture strategies, and translates technical tradeoffs for cross-functional audiences. The role also mentors junior staff, supports recruiting, and bridges machine-learning research with scalable production systems.

Lead the architecture and evolution of DraftKings’ machine learning platform, including real-time data pipelines, lakehouse governance, distributed data systems, and scalable model-serving infrastructure. Establish engineering standards, guide complex initiatives, improve reliability and cloud cost efficiency, and partner across engineering and data teams. The role also includes mentoring engineers, managing production operations, and developing AI-assisted automation tools.

Build, fine-tune, evaluate, and deploy computer vision and multimodal machine learning models in production. Develop scalable data pipelines, evaluation frameworks, real-time processing systems, and cloud infrastructure with monitoring and automated scaling. Improve image processing, barcode decoding, and OCR capabilities while partnering with engineering and fulfillment teams. Contribute through code reviews, documentation, testing, technical leadership, and mentorship.

Designs, builds, and deploys production machine learning systems for search, ranking, discovery, recommendations, and personalization. Responsibilities include semantic and hybrid retrieval, embeddings, data pipelines, search infrastructure, evaluation, monitoring, experimentation, performance optimization, and privacy-aware access controls. The role partners with product and engineering teams to integrate intelligent capabilities across healthcare, commerce, clinical, pharmacy, patient, and merchant applications, while mentoring engineers and owning initiatives through deployment and continuous improvement.

Lead the design and production operation of Navanta’s AI platform, including RAG, retrieval, model serving, MCP tool orchestration, text-to-SQL, evaluation, and guardrails. Build auditable, secure AI systems with PII protection, prompt-injection defenses, grounded responses, and governed metric selection. Partner with data, platform, and product teams while optimizing model quality, latency, cost, and reliability for regulated banking environments.

Build and operate production machine learning systems for EV valuation, auction pricing, forecasting, demand prediction, battery health, and document extraction. Responsibilities include feature pipelines, model training, evaluation, deployment, monitoring, drift detection, retraining, experimentation, and LLM evaluation. The role also strengthens ML engineering practices through reproducible training, model registries, documentation, code review, and collaboration within a small, high-autonomy team.