Top Remote Machine Learning Jobs in Los Angeles, CA

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.

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.

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.

Develops data visualization, analytics, machine learning, and automation solutions for coherent optical product manufacturing. Mines wafer-to-module datasets to identify yield drivers, failure signatures, anomalies, correlations, and process drift. Builds and evaluates ML algorithms for yield prediction and root-cause analysis, performs statistical analysis, and automates engineering workflows using GenAI. Collaborates with R&D, manufacturing, and AI/ML teams to optimize product performance and communicate findings to technical audiences.

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.

Develop and demonstrate AI and data solutions for aerospace engineering projects. Responsibilities include designing architectures, building proof-of-concepts and machine learning systems, writing code, creating demos, supporting cloud and data engineering initiatives, and delivering production-ready solutions. The intern will collaborate cross-functionally, participate in design thinking, gain mentorship, and work on real-world projects using technologies such as Python, cloud platforms, machine learning, LLMs, and computer vision.

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.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

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.

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.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Own and advance Wonderlic’s AI-powered Jobs Engine, using production machine learning and NLP to analyze labor-market data and infer job characteristics at scale. Partner with product, engineering, and I-O psychology teams to integrate responsible AI into assessment and workforce solutions. Apply fairness, explainability, legal defensibility, and rigorous evaluation practices to employment-related AI systems, including generative AI outputs and assessment interpretation.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Designs, trains, evaluates, and operationalizes intelligent document-processing solutions using OCR, machine learning, LLMs, Python, and cloud data pipelines. Builds extraction, validation, reconciliation, exception-handling, and human-review workflows for sensitive information. Develops secure, auditable, configuration-driven systems with API integrations, monitoring, testing, CI/CD, and infrastructure as code. Partners with business experts to define document fields, quality thresholds, validation rules, and acceptance criteria.

Develop and evaluate AI/ML solutions for R&D projects supporting DoD systems. Responsibilities include model development, independent verification and validation, effectiveness reporting, vendor and research-partner collaboration, code review, and integration with external development teams. The role emphasizes mathematical and data science foundations, model validation, secure deployment, and constructive technical assessment across machine learning, LLMs, reinforcement learning, diffusion models, explainable AI, and adversarial AI.

Build and maintain data models, ETL pipelines, and production machine learning systems supporting recommendations, personalization, pricing, search, and marketplace optimization. Own features end to end, conduct statistically rigorous experimentation, monitor deployments, write technical designs, and collaborate with engineering, data science, and product teams to deliver measurable business impact.

Leads AI/ML research and development, owning enterprise evaluation methodology, predictive and decision models, experimentation, causal inference, model economics, and production deployment. Builds evaluation suites and models hands-on, monitors performance and cost, prototypes agentic techniques, and guides AI-native engineering practices. The role also develops a small team, raises organizational ML fluency, supports data-as-a-service benchmarks, and communicates model uncertainty and business value to executives.

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.

Owns the infrastructure and production lifecycle for product and marketing machine learning models. Responsibilities include real-time model deployment, feature pipelines and stores, monitoring and alerting, CI/CD, retraining infrastructure, marketing model operationalization, data pipelines, ad-platform feedback loops, and dependency management. The role bridges data science and engineering while mentoring teammates and making pragmatic architecture decisions.

Lead the Machine Learning Ranking team, managing data scientists and ML engineers while driving career development, technical quality, and product outcomes. Partner with Product Management and Decision Science to define roadmaps, metrics, and marketplace experiments. Oversee machine-learning pipeline development, model deployment, and performance monitoring. Provide technical guidance on ML and AI applications, evaluate engineering work, and promote scalable, reproducible practices across the team.