Top Entry Level Machine Learning Jobs in San Diego, CA

Develop, debug, integrate, and test software and complex software/hardware systems for intelligence and defense customers. Work with Java, AI/ML services, software design, and agile multidisciplinary teams. Support requirements and regression testing, product demonstrations, software installation, and system testing, with possible business travel. Participate in a structured first-year engineering development and mentorship program.

Develop and test C++ and Python software, embedded firmware, autonomy features, simulation tools, and testing infrastructure for UAV systems. Work with sensors, perception, planning, mission systems, and real or simulated data. Investigate failures using logs and telemetry, support hardware- and software-in-the-loop integration, collaborate with multidisciplinary engineering teams, document workflows, and participate in code reviews and design discussions.

Manage AI and machine learning products for materials science, including customer demonstrations, roadmap planning, product expansion, ML model development, and leadership of the materials science ML team. The role requires deep expertise in materials science and ML, software-industry experience, customer engagement, public presentations, and global travel.

Develops data-driven solutions for sustainment and defense clients by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Builds predictive models, algorithms, and quantitative analyses using R or Python, machine learning, artificial intelligence, and natural language processing. The role applies data science to reliability, logistics, maintenance, and combat-system datasets and requires Secret clearance.

Develop and support software, automation tools, dashboards, and alarms for cloud infrastructure, cost attribution, and cloud efficiency. Collaborate with cross-functional teams and senior engineers to design reliable systems, APIs, databases, tests, and AI agents. Contribute to architecture and automation strategies while maintaining strong security, communication, and delivery practices.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Own product outcomes for an AI-native cloud infrastructure platform. Conduct customer discovery, frame problems, prioritize opportunities, prototype solutions, analyze product and business data, and collaborate with Design, Engineering, Sales, Customer Success, and Marketing. The role requires enterprise B2B product judgment, technical fluency across cloud infrastructure and FinOps, strong commercial awareness, AI-assisted workflows, outcome measurement, and end-to-end ownership from strategy through iteration.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Develop and deploy mineral exploration instruments, sensor systems, data acquisition methods, and proprietary software. Integrate geospatial, geophysical, geochemical, geological, and geographic data; build statistically rigorous and physics-based models; identify ore-forming anomalies; and generate 2D and 3D exploration predictions. Collaborate with data scientists, engineers, geoscientists, vendors, and external partners to design field programs, deploy sensors, reduce uncertainty, and guide exploration decisions.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Participate in a two- to three-rotation engineering program focused on turbomachinery technology and new product introduction. Responsibilities may include gas turbine aerodynamics, performance analysis, heat transfer, combustion, additive manufacturing technology, or engineering data science. Engineers will design and analyze turbine components, develop simulations and models, assess test data, optimize performance and durability, and collaborate with multidisciplinary teams. The role requires onsite work in San Diego five days per week and relocation.

Implement customer projects using Python, machine learning, and deep-learning tools for engineering, physics, CAD, CAE, and manufacturing workflows. Develop proofs of concept, analyze project data, train customer teams, and translate customer feedback into product improvements. The role requires strong engineering and machine-learning knowledge, simulation experience, customer-facing communication, and travel to US customer locations.

Leads a recruiter pod while personally managing quota-carrying GTM hiring across sales teams and regions. Coaches recruiters, owns hiring quality and funnel performance, partners with sales leadership, and builds an AI-forward recruiting function. The role also develops Boston’s GTM talent community through events, manages reporting and forecasting, and serves as a strategic talent partner for territories, competitors, and workforce planning.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

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.

Develop technical employment-marketing content that showcases Socure’s engineering challenges, architecture, APIs, machine learning, identity, fraud, and infrastructure work. Interview engineers and technical leaders, write blogs, deep dives, whitepapers, and recruiting copy, and distribute content through developer communities, publications, social channels, and talent platforms. Partner with engineering, recruiting, employer brand, marketing, design, and subject matter experts while maintaining editorial quality and using AI tools thoughtfully.

Freelance writers summarize recent machine learning research papers for The Batch, an AI newsletter reaching over two million professionals and students. Writers must understand machine learning technology, explain complex research clearly in an established format, and communicate effectively in English. The role is intended for graduate students and postdocs in machine learning, with negotiable freelance compensation.

Own Avon AI’s US go-to-market motion, including enterprise prospecting, discovery, demos, pilots, negotiations, forecasting, and closing deals with regulated organizations. Build demand through outbound campaigns, events, webinars, partners, and resellers. Develop the sales process, messaging, qualification framework, and playbook while helping recruit the future US sales and marketing team. Collaborate closely with founders and product teams, translating customer and market feedback into product direction.

Design and deploy production machine learning models and conversational AI agents that transform advertiser briefs into optimized media plans. Build evaluation systems to improve model and agent quality, collaborate across science and engineering teams, investigate ambiguous problems, and iterate using performance data and seller feedback. Communicate effectively with technical and non-technical stakeholders while contributing broadly across machine learning domains.

DoD SkillBridge Fellows gain practical experience through assigned projects, collaboration with experienced professionals, and exposure to operational processes, technologies, and mission objectives. Opportunities span telecommunications, acquisition and procurement, technology, accounting, finance, and other areas. Participants are compensated by their military employer during the fellowship and may be considered for full-time employment afterward based on performance and organizational needs.