Top Machine Learning Jobs in San Diego, CA

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Leads executive discovery, consulting opportunity strategy, solution architecture, proposals, SOWs, commercialization, and delivery mobilization for enterprise data, analytics, AI, and GenAI engagements. Advises clients on modern data platforms, cloud modernization, governance, security, ML, and responsible AI. Requires strong presales, delivery leadership, workshop facilitation, architecture, estimation, and commercial skills across Snowflake, Databricks, Microsoft Fabric, AWS, Azure, and Google Cloud.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and reduce false positives. Review product detections, assess binary legitimacy, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers. Required capabilities include malware analysis, reverse engineering, Python scripting, binary fundamentals, threat assessment, research methods, operating systems, and AI-assisted workflow improvement.

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

Develop new enterprise and upper mid-market business, manage client accounts, and serve as a trusted consulting advisor. Prospect and qualify leads, collaborate with technical teams to create proposals and close SOW contracts, identify business improvement opportunities, build channel partnerships, maintain CRM records, and support localized marketing activities. The role is remote within specified target markets, includes up to 10% travel, and has no direct reports.

Own customer experience product strategy and roadmaps across web and mobile commerce. Lead experimentation, data analysis, usability testing, customer research, and post-purchase experience improvements. Partner with Engineering, Design, Marketing, Merchandising, AI, and Analytics teams to deliver features that improve engagement, conversion, trust, and customer lifetime value. Communicate product vision and priorities to executives while balancing short-term delivery with long-term strategy.

Lead the strategy, roadmap, and execution of an AI-driven B2B SaaS platform for government contracting. Translate RFP and customer needs into prioritized product enhancements, collaborate with AI research and cross-functional teams, apply agile methodologies using Jira, and drive successful product launches and adoption.

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.

Executes systems engineering across R&D platforms, hybrid cloud, SaaS, HPC, containers, integrations, data pipelines, observability, and AI architectures. Coordinates Enterprise IT, vendors, cybersecurity, compliance, and scientific stakeholders to deliver secure, scalable, validated, and supportable solutions. Develops technical documentation, deployment plans, runbooks, monitoring, lifecycle artifacts, risk logs, and operational handoffs. Implements RAG, vector database, AI gateway, agentic workflow, and multimodal integration patterns while supporting cloud migration, change management, governance, and technical issue resolution.

Build and operate Lakehouse data pipelines, product features, machine learning and generative AI components, testing, monitoring, instrumentation, and documentation. Collaborate with product managers, engineers, and business users to clarify requirements, deliver production solutions, investigate defects, and improve reliability and performance. Participate in code reviews, technical design discussions, and backlog-driven development while following engineering standards and contributing to scalable Data & AI products.

Leads application engineering teams delivering cloud-native, AI-enabled solutions on Microsoft Azure for Medicare and Medicaid claims-processing operations. Oversees architecture, software development, quality automation, integrations, support, governance, security, vendor relationships, and large-scale implementations. Develops Azure applications as needed, mentors staff, manages cross-functional initiatives, ensures HIPAA compliance, and partners with business and executive stakeholders to improve healthcare payer outcomes.

Drives 100% new-business sales for Conga’s SaaS solutions across assigned commercial accounts. Responsibilities include prospecting, territory and account planning, solution selling, managing opportunities through the sales cycle, closing enterprise-wide deals, exceeding revenue quotas, and building strategic partner relationships. Collaborates with sales engineering, marketing, customer success, professional services, and other internal teams to understand client needs and deliver tailored solutions.

Lead biostatistical support for Phase I–IV clinical trials, including study design, sample size calculations, exploratory and efficacy analyses, simulation studies, statistical analysis plans, regulatory submissions, and interpretation of results. Provide strategic guidance to cross-functional teams, oversee vendor-supported work, apply advanced statistical methods, and communicate technical findings clearly to technical and non-technical stakeholders.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Develops BI models, dashboards, reports, predictive analyses, and visualizations for Navy leadership assessment programs. Analyzes large datasets, identifies trends, supports executive decision-making, maintains data systems, prepares quarterly performance reports, and coordinates with stakeholders. The role also supervises an administrative assistant and may moderate milestone interviews.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Conduct outbound prospecting through cold calls, emails, and social outreach; qualify inbound leads; engage free users to identify paid-account opportunities; introduce ClickUp products; schedule sales appointments and demos for new-logo acquisition and expansion; and represent the brand while maintaining a healthy sales pipeline.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Design and scale automation and AI-driven solutions for security governance, risk, and compliance. Build pipelines to collect and validate evidence, integrate GRC platforms with security and IT systems, automate control monitoring and audit preparation, and deploy LLM-based workflows for testing, policy mapping, and risk reporting. Establish AI guardrails and quality controls while collaborating with security, engineering, data, and business teams to improve compliance and security posture.

Develop fraud detection models and predictive features for account opening, account takeover, and identity risk. Responsibilities include investigating large datasets, identifying fraud patterns, engineering features, training and evaluating machine learning models, writing tested Python and PySpark code, supporting production deployment, monitoring model performance and drift, and presenting analytical recommendations. The role also requires adherence to data privacy, explainability, model validation, documentation, and governance standards.