Top Remote Machine Learning Jobs in San Diego, CA

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.

Leads DISCO’s machine learning research strategy, directs advanced algorithm and methodology development, oversees large-scale research programs, and manages research teams. Collaborates with industry partners and cross-functional stakeholders to apply machine learning to real-world challenges. Develops long-term research plans, mentors researchers, optimizes research processes, and contributes to the broader research community through publications, presentations, and industry engagement.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Leads financial strategy, planning, forecasting, and operating cadence across Sales, Marketing, Customer Success, and GTM Operations. Owns GTM economics, sales compensation, commissions forecasting, executive reporting, driver-based modeling, resource allocation, and profitability analysis. Modernizes Finance through AI, automation, advanced analytics, improved controls, and scalable processes while developing the GTM Finance team and advising senior executives.

Leads automated scoring and natural language application research for educational assessment products. Responsibilities include setting team priorities, mentoring scientists, designing and analyzing studies, developing machine learning applications, collaborating with stakeholders, presenting technical findings, writing reports, publishing applied research, and supporting products involving automated scoring, crisis-content detection, and formative feedback.

Evaluate, deploy, and optimize machine learning models for on-device execution on Android phones. Benchmark models on mobile hardware, optimize latency, memory, and power consumption, and apply quantization, compression, and distillation. Integrate models with Android applications, system services, and AI agent runtimes. Build hybrid local/cloud model routing and determine which workflows should run locally versus through cloud inference.

Defines and leads architecture standards for production machine learning, generative AI, LLM, and agentic systems. Establishes lifecycle management, observability, evaluation, safety, security, operational readiness, support ownership, and governance practices. Partners with engineering, product, data, security, infrastructure, and IT support teams to deliver reliable AI capabilities. Provides hands-on technical leadership, architecture reviews, documentation, mentoring, prototyping, incident support, and guidance on RAG, vector search, model gateways, prompt orchestration, and agent workflows.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Leads technical integration for defense software capabilities, translating operational needs into requirements, user stories, interfaces, and development priorities. Coordinates integration across software, systems, data, infrastructure, cybersecurity, identity, and deployment environments. Evaluates architecture, resolves cross-system dependencies, supports Agile reviews and testing, and applies API-first, modular, and open architecture principles. Collaborates with operational, acquisition, product, engineering, and government stakeholders in support of Joint Fires Network and CDAO missions.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Own the technical sales cycle for Databricks consulting engagements, including discovery, architecture design, solution scoping, estimation, proposals, demos, proofs of concept, workshops, and statements of work. Design lakehouse, data engineering, analytics, governance, security, and AI architectures. Partner with sales, Databricks, alliance, and delivery teams while ensuring effective handoffs into implementation.

The Senior Display Media Account Manager develops and executes digital media strategies, focusing primarily on Amazon DSP, while fostering client relationships, providing analytics, and driving business growth for clients.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads high-visibility product programs focused on market adoption, analyst relations, customer advisory boards, executive briefings, and industry events. Translates technical product roadmaps into compelling market value propositions, synthesizes analyst and customer feedback, and provides actionable insights to product leadership. Partners cross-functionally with Product, Marketing, and executive stakeholders while supporting strategic initiatives and external-facing communications.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Build and deploy mechanistic, hybrid, and data-driven models for pharmaceutical CMC processes, including drug substance, drug product, packaging, labeling, technology transfer, and manufacturing. Integrate process and experimental data into FAIR data products, analytics, dashboards, and AI-enabled tools. Partner with technical teams and CDMOs to support process understanding, forecasting, risk assessment, regulatory filings, and technology-transfer decisions while owning the digital roadmap and mentoring colleagues.

Senior Product Manager responsible for FinCrime product strategy and delivery across fraud prevention, scams, payment risk, vendor integrations, and cryptocurrency-related products. Owns the full product lifecycle, global feature rollouts, stakeholder management, metrics, regulatory alignment, and customer experience. The role requires hands-on experience with fraud analytics, risk and financial modeling, machine learning, AI tools, APIs, payment systems, and financial-services compliance.

Build and manage technical relationships with LATAM enterprise customers, helping them adopt JetBrains products, overcome technical barriers, and achieve business outcomes. Serve as a trusted technical advisor, customer advocate, and liaison with Support and Product Engineering. Partner with Account Executives and Sales on account strategies and expansions, recommend product enhancements, present product ideas, and maintain expertise in software development technologies.

Leads the architecture, research, development, deployment, and lifecycle management of enterprise AI and machine learning solutions, including generative AI, LLMs, and agentic systems. Partners with data scientists, engineers, IT, business stakeholders, and life sciences experts to deliver reusable, compliant, production-grade solutions across pharmaceutical R&D and corporate functions. Guides AI product strategy, responsible AI, data governance, MLOps, stakeholder adoption, and technical enablement.

Designs, builds, and evaluates agentic AI systems for healthcare workflows such as documentation, coding, denial management, and revenue cycle automation. Develops long-horizon agent behavior, evaluation frameworks, benchmarks, feedback loops, monitoring, and production safeguards. Partners with research, ML engineering, clinical, product, and engineering teams to prototype and deploy reliable, explainable, auditable AI systems using LLMs, machine learning, and agentic technologies.

Deploys, integrates, and scales enterprise Generative AI solutions across Azure environments. The role develops LangGraph agentic workflows, fine-tunes and evaluates LLMs, reduces hallucinations, integrates retrieval systems and APIs, and collaborates with customer engineering teams. Responsibilities also include requirements documentation, process mapping, automation engineering, software architecture, and communicating business value.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Build and scale AI-native healthcare products through full-stack applications, backend services, data pipelines, and AI systems. Design, test, deploy, and operate production-ready software, run A/B tests, automate manual workflows, and collaborate with product, operations, and AI/ML teams. The role requires end-to-end ownership of scalable, secure, reliable systems and features, with a focus on performance, data integrity, clean code, and measurable business outcomes.

Conduct applied AI research focused on explainability, reinforcement learning, retrieval-augmented generation, and long-context retrieval. Design, train, and evaluate machine learning models using large-scale healthcare data and distributed infrastructure. Build research prototypes and experimentation frameworks, validate measurable impact, and collaborate with ML Engineering, MLOps, and Product teams to transition innovations into production.