Top Machine Learning Jobs in Tijuana

Configure and maintain containerized ML environments, execute and monitor distributed training workloads on accelerator clusters, and automate operations with Python and Bash. Troubleshoot failures through log collection and infrastructure analysis, document findings, track execution status, communicate test progress, and collaborate with engineering teams to improve reliability and operational efficiency.

Own the architecture, deployment, reliability, and cost optimization of production AI/ML infrastructure on GCP. Build model serving systems, reproducible ML pipelines, GitOps workflows, and multi-tenant Kubernetes platforms. Manage LLM and agentic workloads, Terraform infrastructure, observability, SLOs, drift monitoring, accelerator capacity, and inference costs. Define operational standards, improve automation, and guide platform evolution for scalable enterprise-grade ML systems.

Industry Expert who reviews advanced AI and systems engineering projects, leads technical defenses and mock architecture review boards, hosts occasional design sessions, and sets technical standards for instructors. The role requires senior-level production engineering experience, strong architectural judgment, the ability to critique systems live, and expertise in AI/ML engineering, AI systems, or forward-deployed engineering. This is a fully remote commitment of 4–10 hours monthly with negotiable hourly compensation.

Designs and optimizes Retrieval-Augmented Generation systems, including document ingestion, chunking, embeddings, retrieval, reranking, vector search, and source-grounded responses. Develops evaluation frameworks to measure relevance, citation accuracy, factual grounding, latency, cost, and hallucination reduction. Collaborates cross-functionally to deliver scalable, trustworthy, and responsible Generative AI solutions for legal and professional information products. Preferred work includes AI agents, LLM workflows, hybrid search, observability, and regulated-domain applications.

This role involves leading strategic planning, product innovation, and cross-functional collaboration to enhance pharmacy analytics and drive growth initiatives for CVS Health.

Create and review realistic, locale-specific robotics and natural sciences tasks in Spanish (LATAM), apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce gold-standard solutions and expert feedback.

Design, build, and maintain production-grade ML systems for large-scale legal NLP datasets. Develop and orchestrate ML/AI agents, prototype solutions, collaborate with cross-functional teams, and work with legal data annotators to improve data quality and drive product-focused outcomes.

Operate and monitor AI models and agent systems in production. Track performance, drift, latency, errors, availability, and business impact; manage incidents, rollbacks, deployments, versioning, registries, and lifecycle transitions. Maintain governance, audit trails, risk controls, fairness, bias, explainability, and transparency monitoring. Integrate cloud, container, monitoring, logging, alerting, and CI/CD platforms while collaborating with product, engineering, data science, and risk teams.

Develop and validate change-detection and anomaly models on multi-temporal Sentinel imagery for pipeline corridors. Fine-tune geospatial foundation models with LoRA/PEFT, implement SAR displacement methods and dune-migration tracking, engineer data ingestion from Copernicus/STAC sources, design labelling and validation strategies, and communicate results to technical and business stakeholders.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.

Develop and calibrate change-detection and anomaly models on multi-temporal Sentinel-1/2 imagery to detect sand encroachment and pipeline displacement. Fine-tune geospatial foundation models, implement SAR displacement techniques, build dune-migration tracking, engineer Copernicus data ingestion, design labeling and validation strategy, and communicate results to stakeholders.

Lead a team of ML engineers, manage client engagements, and provide strategic advisory on ML architecture while driving pre-sales. Ensure team growth and quality of deliverables.

Design and implement scalable Generative AI architectures, build Python services, integrate LLMs, oversee deployment operations, and evaluate emerging technologies.

Build benchmarks and eval infrastructure for real-world AI evaluation. Partner with researchers and customers to design benchmarks, process multimodal data, build data pipelines, sandboxed execution environments, storage, and orchestration. Ship iterative eval infrastructure and own engineering for customer engagements.

Design, train, and refine machine learning algorithms on high-dimensional data to produce practical predictive models. Apply random forests, deep learning, generative modeling, and neural-network techniques to improve NLP and machine perception for real-world applications.