Top Machine Learning Jobs in Austin, TX

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

Develop and deploy machine learning and LLM capabilities for agentic search, query understanding, search planning, and retrieval. Build evaluation datasets, improve model behavior through experimentation and fine-tuning, and optimize quality, latency, cost, and reliability. Collaborate across search, product, infrastructure, and AI teams while owning projects through production and mentoring engineers.

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Leads a team developing AI capabilities for CrowdStrike’s cross-platform endpoint sensor. Owns the AI-on-endpoint roadmap, including machine learning model operationalization, telemetry data pipelines, on-device inference, architecture, feature delivery, and cross-functional collaboration. Manages hiring, mentoring, engineering quality, testing, performance, and releases across Windows, macOS, and Linux sensor platforms.

Develops and operationalizes data science solutions that detect emerging vehicle, software, warranty, and product-quality issues. Builds analytical pipelines, anomaly-detection algorithms, dashboards, alerts, and monitoring workflows using Python, SQL, and cloud technologies. Partners with engineering, quality, warranty, and software teams to validate data, deploy production solutions, improve scalability and cost efficiency, and communicate actionable insights. The role also supports vehicle launches, documents analytical practices, mentors peers, and drives adoption of reusable analytics capabilities.

Design, prototype, and evaluate machine learning models for travel products. Build maintainable training and inference code, conduct statistical analysis and feature engineering, participate in ML system and API design, and collaborate with data scientists, engineers, mentors, and product partners. Interns will contribute to research and product experiments involving scalable services, A/B testing, fairness, and AI-enabled solutions.

Build and deploy production-grade AI and machine learning solutions for marketing and sales, including LLM agents, data-quality pipelines, semantic search, RAG, analytics, and integrations with Salesforce, Marketo, and Adobe platforms. Define evaluation and governance practices, monitor AI services, implement privacy controls, and collaborate with platform teams. Train and mentor technical and business stakeholders on responsible AI adoption while supporting DevOps, incident response, and operational runbooks.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.

Design, build, and evaluate multi-step agentic AI systems and orchestration frameworks integrating LLMs with tools, APIs, and data. Define evaluation and safety frameworks, mitigate agent-specific risks, collaborate with product and engineering, and mentor junior ML staff to deliver production-grade solutions.

Leads CRM marketing analytics for Seller and Owner verticals by developing segmentation, targeting, personalization, customer journey, and campaign measurement strategies. Analyzes consumer behavior to improve engagement, retention, lead generation, cross-selling, and revenue. Builds statistical and machine learning models, integrates CRM data, establishes KPIs, communicates insights to stakeholders, and partners with Marketing, Data Engineering, and IT teams.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Design and build large-scale mapping algorithms and geospatial data systems for automated map reconstruction and maintenance. Develop lane, road-network, geometry, topology, and map-conflation systems; process sensor and third-party data through distributed pipelines; apply machine learning and computer vision for feature extraction and change detection; and create automated validation and quality systems. Partner with perception, localization, simulation, and platform teams while mentoring engineers and driving technical problems into production.

Leads computer vision, machine learning, and tracking teams while translating AI strategy into roadmaps, standards, and deliverables. Manages managers, mentors technical staff, coordinates cross-disciplinary engineering efforts, owns schedule execution, and drives integration risk resolution. Establishes model evaluation and reproducibility practices, reviews code, develops prototypes, and supports architecture decisions for real-time perception systems.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Research and develop scalable machine learning and data science solutions for Vrbo pricing and monetization. The role applies causal inference, operations research, optimization, demand estimation, and experiment design to production systems. Responsibilities include defining performance metrics, analyzing complex datasets, collaborating with scientists, engineers, product managers, and analytics teams, communicating technical plans, and validating models through A/B testing.

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.