Top Machine Learning Jobs in Austin, TX

Develop and optimize real-time computer vision algorithms and ML models for an autonomous anti-drone turret. Integrate CV systems with embedded hardware and sensors, conduct testing and validation across environments, and help harden prototypes into military-grade systems and variants for different weapons and ranges.

Lead development and optimization of real-time computer vision and ML models for autonomous turret drone detection, tracking, and classification. Integrate algorithms with embedded hardware and sensors, conduct robust testing across environments, and harden prototypes to military-grade systems.

Lead end-to-end delivery of AI and ML services within Managed Services. Manage project planning, budgeting, execution, and quality. Mentor and develop team members, cultivate client relationships, implement model monitoring and operationalization practices, and collaborate with architects and engineers to ensure compliant, reusable AI solutions.

Design and build AI-native, high-performance distributed backend platforms for IT/OT/cybersecurity. Integrate LLMs and ML into production workflows, develop data pipelines and real-time processing, mentor engineers, and drive architectural direction and AI-assisted engineering practices.

Technical lead for end-to-end ML and CV pipelines to reconstruct, validate, and maintain map primitives from multi-modal vehicle sensor data. Architect scalable offline and production map reconstruction systems, define technical strategy, drive cross-functional integration, mentor senior engineers, and ensure robustness, safety, and operational scalability for national-scale mapping.

Lead AI strategy, partner engagements, and operational execution across CrowdStrike products. Align R&D and product delivery to company AI strategy, prioritize initiatives, set organizational goals, drive thought leadership, and create order in complex cybersecurity AI environments while delivering customer value.

Intern will own and deliver a scoped engineering project on Deepgram's voice platform (ASR/TTS/LLM/API/SDK), ship reviewed code to staging/production, use AI in development workflows, improve a component measurably, and present results with documentation.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Build and maintain Cloudflare's new vector database offering: design, implement, and operate distributed, low-latency cloud systems. Own features across the stack, ensure reliability and performance, write design documents, and apply machine learning and data retrieval expertise using TypeScript, Go, Rust, and related technologies.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

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

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Manage and mentor a global team of data engineers and data scientists, align technical delivery with business goals, allocate resources, improve delivery processes, drive automation and technical evangelism, and collaborate with sales and account teams to support growth.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Provide legal guidance on Rubrik's AI use and products, ensuring compliance with laws and contracts, drafting and negotiating AI terms, coordinating generative AI review processes, educating employees, and improving AI review controls and workflows across cross-functional teams.

Lead design and build of a scalable, multi-tenant AI/ML platform powering traditional ML, LLMs, and agentic systems. Own end-to-end architecture, production-grade backends, orchestration, LLMOps, vector DB integration, and cross-functional delivery while enforcing engineering standards, security, and observability.

Design, develop, and deploy end-to-end ML models and systems for pricing and revenue optimization. Build data pipelines, feature engineering, model training, evaluation, and production monitoring. Partner with product, engineering, and analytics to define success metrics and run experiments. Apply causal inference and optimization techniques to improve business outcomes and enable scalable model-serving and reuse.

Design, implement, and deploy end-to-end ML models and data pipelines for ranking, recommendations, search, and personalization. Build scalable data quality checks, monitoring, and evaluation (A/B tests, offline/online) to ensure reliable, fair, production-grade AI. Collaborate with product, analytics, and engineering to translate business needs into ML solutions and drive iterative improvements.