Top Machine Learning Jobs in Austin, TX

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build tooling for profiling and debugging, integrate kernels into the ML runtime, collaborate with compilers and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.

Develop and optimize real-time computer vision and ML systems for detecting, tracking, and classifying drones on an autonomous gun turret. Implement resource-efficient models, integrate vision systems with hardware, and validate performance across diverse, challenging environments to harden prototypes to military-grade standards.

Design, build, and operate CV/ML infrastructure: manage a 130+ GPU bare-metal Kubernetes cluster with AWS bursting, own CV/ML CI/CD, maintain model/data/versioning and experiment tracking, automate training/testing, implement model optimization for edge (Jetson/TensorRT/quantization), and provide ML observability and security for production deployment.

Own end-to-end deployment of ML models to resource-constrained edge hardware: optimize and convert models (quantization, pruning, distillation, operator fusion), profile inference on accelerators, write and maintain C++ inference host code, build test harnesses and tooling, and set best practices while mentoring engineers to meet real-time, memory, power, and latency constraints.

Develop and optimize real-time computer vision algorithms and ML models for drone detection, tracking, and classification; integrate CV systems with embedded hardware and sensors; test and harden systems for military-grade, safety-critical deployment.

Manage and grow a CV/ML team while contributing hands-on to real-time computer vision algorithms for drone detection, tracking, and classification. Define and execute the CV/ML roadmap with senior leadership, own delivery commitments, coordinate hardware integration, and enforce rigorous testing and validation for field and safety-critical deployment.

Lead technical direction and architecture for real-time CV/ML perception on an autonomous anti-drone turret. Solve hardest technical problems, own roadmap, mentor senior engineers, set validation and production standards, and align CV/ML strategy with product and delivery.

As a Senior AI/ML Engineer, you'll enhance developer productivity by leading CI solutions, mentoring engineers, and implementing scalable systems.

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

Drive hardware-aware ML algorithm development and optimization for ARM compute platforms. Analyze ML workloads (PPA focus), prototype algorithms, and optimize state-of-the-art networks for CPU/GPU/NPU targets.

Lead technical direction and evolution of Bumble's recommendations platform: design and operate large-scale distributed systems for retrieval, ranking, serving, experimentation, and feedback loops; mentor engineers, define architecture and observability standards, drive platform initiatives, and contribute hands-on to prototypes and production code with strong ML and Generative AI focus.

The Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

Design and implement scalable ML infrastructure solutions, mentor junior engineers, own technical projects, and collaborate across teams to solve complex AI/ML problems.

The Staff AI/ML Engineer will design and implement scalable ML infrastructure solutions, lead projects, mentor peers, and enhance ML productivity at GM.

Develop reusable ML tooling and libraries, collaborate with data scientists to productionize models, build services and APIs (Docker/Linux), follow engineering best practices, and support project lifecycle from intake to maintenance.

Lead end-to-end development, validation, and deployment of large-scale predictive models. Drive rigorous data-driven research, translate business goals into analytical frameworks, and communicate findings to executives. Mentor data scientists and partner with engineering and stakeholders to productionize ML solutions and define long-term analytics strategy.

Lead architecture and specification of high-performance AI accelerators. Build functional and analytical models, collaborate with hardware and software teams, and guide design, verification, emulation, firmware, and kernel teams through end-to-end silicon development to optimize performance, power, cost, and time-to-market for cloud-scale AI workloads.

The role involves designing, building, and deploying compute and networking solutions for AI and HPC, supporting customers with integration and performance debugging.

The role involves developing novel methodologies for conversion modeling in advertising, using machine learning and statistical techniques to enhance performance and optimization.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Design, develop, and deploy data-driven solutions across product lifecycle. Partner with design, manufacturing, test, and quality teams. Mine and analyze company data, build custom models and algorithms, monitor model performance, and propose continuous improvement strategies. Integrate with vendor software and support product modifications and maintenance.

Develop and deploy machine learning and AI solutions for high-volume semiconductor wafer manufacturing. Integrate quality, engineering, production, and support teams to detect and prevent incidents, model large multi-source datasets, collaborate with SMEs, and contribute to global factory standardization and analytics improvements.

Lead productionization and optimization of ML models for a global, serverless inference platform. Build benchmarking/evaluation tooling, improve inference performance (quantization, compilation, batching), integrate models with heterogeneous GPUs and accelerators, enhance deployment workflows and observability, and mentor engineers while driving ML production best practices.

Own strategy and roadmap for Crunchbase's customer-facing AI data layer. Identify and validate model-derived data products, lead cross-functional development, set quality standards, drive adoption and monetization, and monitor performance and product economics.

Lead applied research and end-to-end development of production ML systems for a high-volume e-commerce marketplace. Prototype and deploy models, optimize auction and decisioning logic, improve performance, robustness, and interpretability, and collaborate with product and engineering. Provide technical leadership for a small specialist team, set engineering standards, manage delivery, and mitigate project risks.