Top Senior Machine Learning Jobs in Austin, TX

Own and evolve Braze’s ML platform for globally scaled training pipelines, model serving, deployment, orchestration, and observability. Lead complex infrastructure initiatives, improve reliability and cost efficiency, manage incident response, and establish production quality standards. Partner across teams, shape technical strategy, mentor senior engineers and data scientists, and connect platform decisions to customer and business outcomes.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

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 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.

Develop and deploy machine learning systems for humanoid robot manipulation, covering data collection, annotation, synthetic data generation, model training, simulation, hardware deployment, and fine-tuning. Diagnose vision, calibration, sensor fusion, model, and data bottlenecks to improve reliability. Build real-time robotic software using modern computer vision and deep learning, while mentoring engineers and driving architectural excellence across the robotics organization.

Design, optimize, and deploy machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, production deployment, performance profiling, and systems engineering. The role also addresses on-device privacy and security while collaborating cross-functionally to deliver reliable edge AI. Preferred experience includes NPU/DSP toolchains, federated learning, safety-critical deployments, open-source contributions, and on-device LLM optimization.

Lead development of scalable, reliable continuous integration infrastructure supporting autonomous-vehicle development, machine learning training, simulation, and remote builds. Specialize in Remote Build Execution and a FUSE-based file system, enabling source-code editing and developer workflows. Design and implement productivity improvements, evaluate technologies, influence technical roadmaps, establish engineering best practices, manage technical debt, and mentor engineers while balancing business and customer priorities.

Designs, develops, deploys, and operates production machine learning and AI systems for identity security. The role spans classical ML, generative AI, foundation models, RAG, agentic workflows, semantic search, behavioral modeling, and graph ML. Responsibilities include experimentation, model evaluation, monitoring, MLOps, AI governance, architecture, cross-functional delivery, and continuous improvement of scalable customer-facing capabilities.

Leads and develops a perception and machine learning engineering team building computer vision, sensor fusion, tracking, detection, and scene-understanding capabilities for autonomous systems. Owns roadmap planning, technical decisions, delivery, model development and deployment workflows, field readiness, hiring, performance management, and cross-functional execution with autonomy, embedded, data, hardware, product, programs, and field operations teams.

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

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 the development and production deployment of machine learning, generative AI, and multi-agent solutions. Translate business problems into scalable AI products, build models and data pipelines, architect cloud and lakehouse systems, and implement MLOps, LLMOps, evaluation, governance, security, and monitoring. Partner with cross-functional stakeholders, communicate technical outcomes, troubleshoot production issues, mentor teammates, and shape the strategic roadmap for enterprise AI capabilities across Azure, Databricks, AWS, and GCP.

Build scalable evaluation, introspection, visualization, and metrics tools for autonomous vehicle machine learning models. Develop workflows processing billions of examples and connect offline evaluation with data, training, simulation, and launch decisions. Collaborate with modeling, data, infrastructure, and validation teams to improve model performance, safety, reliability, and development speed. Responsibilities include software design, implementation, maintenance, code reviews, and production tooling across backend, frontend, and data infrastructure.

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

Designs, builds, and operationalizes enterprise AI, generative AI, machine learning, and agentic AI solutions. Develops integrations, orchestration services, reusable tooling, deployment pipelines, monitoring, and governance controls. Partners with architecture, platform, data, and product teams to deliver scalable, secure, compliant AI capabilities across the organization.

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.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Lead the design, training, evaluation, and deployment of large-scale machine learning models, including LLMs, diffusion models, and GNNs. Own distributed training pipelines, dataset interfaces, optimization strategies, model evaluation, serving, and lifecycle management. Collaborate with data, infrastructure, and scientific teams while providing technical leadership through architecture reviews, mentorship, and cross-functional decision-making.

Leads AI/ML engineering teams across platform and delivery pods, managing employees and contractors while enforcing code, agent, review, and production-readiness standards. Partners on reusable templates, pipelines, and evaluation processes, ensures adoption across teams, manages contractor performance and conversion, and removes delivery blockers. The role requires hands-on credibility reviewing agent architectures, prompts, and evaluation suites, with experience operating as a player-coach in fast-moving organizations.

Design, train, evaluate, and deploy reinforcement learning systems for complex decision-making problems. Responsibilities include developing RL algorithms and reward functions, working with simulation environments, collecting experience at scale, training neural network policies on GPU clusters, and moving research into stable production systems. The role also involves communicating technical work and may include RLHF, multi-agent or hierarchical RL, robotics, autonomous driving, and open-source or research contributions.

Lead the development, fine-tuning, deployment, and optimization of machine learning solutions involving LLMs, vision models, and NLP for healthcare applications. Build production-grade models and pipelines, support scalable GPU and TPU training, validate performance and compliance, collaborate with healthcare and product teams, and mentor junior engineers. The role also requires documenting technical approaches and applying MLOps and LLMOps practices in regulated environments.