Top Remote Machine Learning Jobs in San Diego, CA

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Build and lead detection-as-code, AI/ML, automation, and response capabilities for a global security operation. Develop production AI/ML pipelines for investigation, triage, enrichment, and detection generation; establish version-controlled detection standards, testing, and CI/CD deployment across SIEM, XDR, and EDR platforms. Create SOAR workflows, response playbooks, and containment integrations. Maintain MITRE ATT&CK coverage, partner with threat intelligence and hunting teams, validate defenses through purple teaming and breach-and-attack simulation, and lead technical projects as the senior individual contributor.

Leads customer R&D transformation engagements from pre-sales through delivery. Shapes scientific and technical challenges into AI architectures, product backlogs, MVPs, simulations, integrations, data pipelines, and production solutions. Partners with executives, researchers, engineers, product teams, and account teams to define outcomes, scope delivery, assess data and compute needs, and establish roadmaps. Develops reusable architectures and delivery playbooks while influencing Microsoft Discovery product direction and scaling AI-powered R&D solutions across industries.

Drives operational excellence through data analysis, dashboards, process improvements, and cross-functional project leadership. The role embeds with business units to manage day-to-day operations, investigate trends, answer strategic questions, implement workflows, coordinate stakeholders, track deliverables, and present insights to technical and non-technical audiences. It serves as a strategic partner to operations leadership in a fast-paced, ambiguous environment.

Lead complex R&D and scientific AI engagements from pre-sales through production delivery. Partner with executives, researchers, engineers, and product teams to define use cases, architectures, backlogs, MVPs, simulations, integrations, data pipelines, and success metrics. Assess technical feasibility, coordinate multidisciplinary workstreams, and develop reusable architectures and delivery playbooks. Apply AI, machine learning, cloud computing, scientific workflows, and HPC to transform research processes while influencing product roadmaps and scaling solutions across industries.

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.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

Lead development and maintenance of character creation, rigging, animation, and simulation tools for game production. Collaborate with artists, engineers, studios, and research teams to prototype workflows, integrate game engines, optimize environments, and apply AI/ML to improve development efficiency. Build and customize internal tools, automate project environments, troubleshoot production issues, document systems, and support varied client pipelines. Participate in internal demonstrations and conferences, with required travel.

Designs and deploys large-scale NVIDIA AI and HPC GPU infrastructure for cloud partners and strategic customers. Leads technical engagements across the customer lifecycle, supports infrastructure development and production, builds networking and compute proofs of concept, troubleshoots performance and scaling issues, and delivers technical presentations and workshops. Partners with sales and business teams to identify opportunities and provides customer feedback to influence product strategy.

Develop and support internal low-code applications and AI agents using Microsoft Power Platform and Copilot Studio. Assist with API and database integrations, automated testing, Agile delivery, intelligent automation, documentation, and application monitoring. Collaborate with cross-functional teams to create secure, compliant, reliable digital solutions that improve enterprise productivity and workflows. The role is remote with occasional travel to Vermeer campuses.

Lead and own cross-organizational technical programs for ML and robotics platform development, define roadmaps and milestones, remove blockers, align diverse engineering teams, and drive adoption of platform best practices to deliver scalable autonomy and infrastructure outcomes.

Define and execute the market strategy for UserTesting’s AI solutions. Own positioning, messaging, value propositions, go-to-market plans, sales enablement, customer and buyer research, competitive differentiation, thought leadership, and market feedback loops. Translate complex AI workflows and human-judgment applications into compelling solutions for technical enterprise buyers, while influencing product and company strategy through cross-functional leadership.

Manage strategic enterprise accounts across a national territory, expanding existing customers and acquiring new logos. Build consultative account strategies, serve as a trusted AI and customer experience advisor, develop customer and channel-partner relationships, forecast and exceed bookings targets, and coordinate cross-functional teams to maximize customer and business outcomes. Support initiatives across the customer lifecycle, including business development, messaging, commercial positioning, and operational effectiveness.

Leads a team of machine learning engineers and data scientists developing and deploying styling algorithms and personalized client experiences. Owns AI and ML strategy, product roadmap contributions, experimentation, production deployment, code quality, and data practices. Partners with product, design, merchandising, styling, and business teams to optimize personalization outcomes while coaching the team and fostering collaboration, innovation, and technical excellence.

Lead delivery of medium- and large-scale AWS programs, overseeing planning, assurance, reporting, risk mitigation, program health, and customer outcomes. Manage multi-year product and service roadmaps, identify business opportunities, resolve customer issues, and drive continuous process improvement. Lead, recruit, coach, and develop business analysts and engineers while collaborating with technical and business leaders across global enterprise clients.

Develop machine learning and AI models using clinical, claims, and social determinants of health data to improve pediatric patient outcomes. Partner with stakeholders and cross-functional teams to define requirements, optimize data collection, build data assets, conduct exploratory analysis, and deliver strategic and patient-care insights. Query Snowflake independently and present findings to nontechnical staff.

Develop, test, maintain, and improve software solutions using established development practices. Write clean code, assist with feature implementation, debug issues, and collaborate remotely with team members. Support AI technology integration, automation, and AI-powered development workflows. Research emerging AI tools, document technical processes, and continuously develop programming skills.

Lead cross-functional compiler software programs from concept to delivery, coordinating global teams, tracking risks and schedules, and improving development processes. Present status to executives, drive Agile and Waterfall projects, resolve technical/resource conflicts, and implement process and tool automation to ensure timely product releases.

Own lifecycle marketing performance across patient onboarding, activation, early care, and retention. Design and analyze multichannel experiments, build reporting and dashboards, write SQL, diagnose funnel and segment performance, and integrate AI and machine learning signals into lifecycle journeys. Collaborate with Product, Operations, Data Engineering, Marketing Operations, Analytics, and Creative to improve patient outcomes and measurable business performance.

Leads product strategy, roadmap development, prioritization, product metrics, and go-to-market execution for a marketing planning and analytics platform. Manages and coaches product managers while acting as an individual contributor on key initiatives. Partners with the CEO, engineering, sales, marketing, and leadership teams to deliver API-based, data-heavy statistical and machine learning products for technical B2B SaaS customers.

Leads enterprise cybersecurity architecture for AI, cloud, hybrid, and on-premises environments. Designs secure AI architectures, cloud foundational controls, zero-trust identity models, API and data protections, and secure LLM, RAG, agent, and MCP integrations. Establishes governance, reusable security patterns, model-usage procedures, compliance controls, and audit readiness across AWS and Azure. Advises executives and technical teams, mentors security professionals, and drives secure cloud migrations while addressing AI/ML risks such as adversarial attacks, model poisoning, data drift, bias, and data leakage.

Lead and develop a team of partnership analysts; translate business questions into analytical frameworks, experiments, forecasts, and ROI models. Build dashboards and reporting infrastructure, analyze cohorts, funnels, retention, and partner performance, and own measurement frameworks for growth initiatives. Partner with Business Development, Engineering, Product, and Marketing to shape partnership strategy and communicate data-driven insights to technical and non-technical leaders.

Owns the strategy, roadmap, development, and adoption of financial data products and APIs. Partners with data science and engineering to integrate predictive and machine-learning models into scalable, trustworthy capabilities. Defines quality metrics, requirements, documentation, and support models while ensuring privacy, security, regulatory compliance, and downstream adoption across consumer and business banking products.

Manage and grow NVIDIA partner relationships in Manufacturing and Automotive: develop business plans, drive ecosystem enablement, increase adoption of NVIDIA AI solutions, maintain executive relationships, and present results in QBRs.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.