Top Machine Learning Jobs in San Diego, CA

Own the strategy, roadmap, packaging, and go-to-market evolution of Pluralsight’s AI, Cloud, and Security learning solutions. Design blended learning programs, define expert perspectives, identify content and lab components, monitor market needs, and use customer and learner data to improve outcomes. Partner with Product, Sales, Marketing, Curriculum, Engineering, and Services teams, support strategic enterprise deals, manage solution benchmarks, and drive adoption, renewals, and revenue growth.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Leads end-to-end AI transformation for 5–10 clients while managing a cross-functional AI pod and full P&L accountability. Responsibilities include developing AI roadmaps, advising C-suite stakeholders, delivering AI and automation projects, driving adoption, managing client relationships, expanding accounts, coaching technical teams, and overseeing revenue, utilization, margin, and profitability.

Designs and oversees scalable data platforms, warehouses, lakehouses, pipelines, ETL processes, dimensional models, and Spark workloads. Integrates AI/ML capabilities, data governance, lineage, testing, monitoring, and performance optimization. Collaborates with engineering, product, DevOps, and security teams to support tracking, analytics, and business insights. The role also mentors engineers and analysts and manages software delivery using AWS DevOps, CI/CD, and Terraform practices.

Leads client-facing data science and analytics initiatives, translating complex marketing and media data into actionable business narratives. Develops measurement strategies, including test/holdout incrementality, informs acquisition and retention decisions, scales analytics best practices, and manages analyst teams. Partners with Sales, Client Services, Operations, and other stakeholders while supporting predictive and adaptive AI/ML solutions. The role requires strong client communication, strategic leadership, analytics expertise, and proficiency with Python, SQL, Tableau, and related tools.

Lead the strategy, governance, delivery, and continuous improvement of an enterprise data platform. Partner with technical and business leaders to understand data needs, design scalable solutions, support BI, analytics, machine learning, and data products, and drive organizational transformation. The role also involves developing a high-performing data team, improving data accessibility, and fostering innovation and operational excellence.

The role involves deploying and supporting CrowdStrike's AI capabilities in cybersecurity, optimizing threat detection, and mentoring teams on AI solutions.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Build and maintain Palantir Foundry data pipelines, ontologies, applications, workflows, and decision-support capabilities for defense missions. Translate stakeholder problems into technical solutions, integrate structured and unstructured data, troubleshoot production systems, and collaborate directly with clients through deployment. Apply engineering best practices including testing, version control, and documentation while communicating with technical and non-technical audiences. Travel to client sites may be required.

Lead and build the Applications function to translate clinical/research lab workflows into validated, deployable robot capabilities. Own workflow validation, instrument onboarding, training data collection for ML, technical documentation, demos, customer technical leadership, and team hiring and development.

Lead Amazon performance marketing strategy and execution, manage Sponsored Ads and DSP, collaborate with agencies and cross-functional teams, optimize campaigns and budgets, analyze performance with AMC/analytics, and measure commercial impact to drive ROAS and customer acquisition.

Designs, develops, tests, documents, and integrates algorithms and software for intraoperative neuromonitoring medical devices. Responsibilities include machine learning model development using clinical data, software architecture, application development, root-cause analysis, cross-functional product collaboration, and full lifecycle ownership of algorithm projects. The role requires compliance with FDA regulations, ISO 13485, and related medical-device standards, with travel up to 10%.

Own adoption, retention, and expansion across a pooled portfolio of scaled customers through digital-first programs. Provide technical API integration guidance, troubleshoot issues, run demos, and create reusable enablement assets. Design automated customer journeys, webinars, self-service resources, and workflows that accelerate activation. Monitor usage and health data, identify expansion opportunities, report KPIs, and build scalable systems that reduce recurring manual work.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Lead corporate communications strategy and execution: own product and model launches, media and executive communications, narratives, social presence, and major company moments. Translate technical research and product advances into compelling stories for developers, enterprise buyers, partners, and analysts. Partner cross-functionally with CEO, CMO, product, brand, and demand gen to build credibility and influence in the voice AI ecosystem.

Own and execute multi-year strategic partnerships with a hyperscaler and an AI lab, aligning technical integrations, commercial motions, and joint go-to-market plans. Coordinate cross-functional teams, run operational cadences, track measurable outcomes (pipeline, ARR, integrations), and represent partnerships to executives while driving hands-on execution and follow-through.

Develop and optimize machine learning, computer vision, signal-processing, and image-analysis algorithms for workflow automation and biological technologies. Integrate algorithms with hardware and software systems, optimize deployment on edge devices and GPUs, validate performance, and document results. The role requires strong Python development, mathematical and algorithmic expertise, and collaboration across multidisciplinary engineering teams.

Lead CrowdStrike Finance & Accounting AI transformation: define operating model and governance, prioritize AI use cases, oversee POC-to-production delivery, drive enablement and change management, measure benefits realization, and report outcomes to executives.

Leads technical pre-sales and foundational implementation engagements for SailPoint SaaS solutions. Discovers customer requirements, maps them to product capabilities, delivers proof-of-value implementations, explains architecture and best practices, and collaborates with sales, product, services, and customer success teams. Develops AI and agentic technology solutions for demonstrations and customer problems, while serving as a technical and consultative advisor to customers, partners, and colleagues.

Lead technical customer engagements for strategic accounts, supporting sales, solution design, foundational implementations, and proof-of-value deployments. Discover requirements, map them to SailPoint solutions, educate customers on architecture and best practices, collaborate across sales, product, professional services, and customer success, and develop AI- and agentic-technology solutions for demonstrations and customer outcomes.

Forward Deployed Engineers support sales and customer engagements by discovering requirements, designing and implementing foundational SaaS solutions, delivering proof-of-value implementations, and educating customers on architecture and best practices. They collaborate with sales, product, professional services, and customer success teams; use AI and agentic technologies to solve customer problems; lead technical engagements; document requirements; and develop reusable demonstration solutions. The role may require up to 15% travel.

Forward Deployed Engineers support enterprise sales and customer engagements by discovering requirements, mapping them to SailPoint solutions, and delivering foundational implementations and proof-of-value projects. They provide technical leadership, explain architecture and best practices, collaborate across sales, product, services, and customer success teams, and develop AI- and agentic-technology solutions for demonstrations and customer problems. The role requires strong client communication, software implementation expertise, SaaS architecture knowledge, and familiarity with enterprise infrastructure and identity governance.

Leads a data science team focused on customer intelligence and personalization across digital products, apps, newsletters, and marketing channels. Develops lifetime value, segmentation, retention, recommendation, attribution, forecasting, and engagement models. Owns product funnel analytics and experimentation, including A/B testing and causal inference. Partners with Product, Marketing, Editorial, Data Operations, and Engineering to translate customer behavior into product improvements, measurable growth, and scalable data capabilities.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.