Top Machine Learning Engineer Jobs in Austin, TX

Build production machine learning benchmarks and evaluation systems for foundation models. Own backend infrastructure including data pipelines, execution environments, storage, and orchestration, as well as sandboxed environments for agentic evaluations. Partner with researchers, enterprise customers, and company leadership to scope and deliver technical solutions. Identify scalable evaluation patterns, communicate clearly, and operate effectively in an ambiguous, high-ownership startup environment.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy-farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and integration with cloud infrastructure. The role also drives AI adoption, establishes engineering practices, collaborates cross-functionally, and may build React and TypeScript user experiences.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and adoption enablement. The role also involves collaborating on React and TypeScript user experiences, translating ambiguous requirements into scalable systems, and communicating technical tradeoffs across product, engineering, and dairy science teams.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

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.

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 technical direction for high-impact machine learning initiatives, from research and experimentation through production deployment. Build and fine-tune LLMs, document understanding systems, model-serving infrastructure, evaluation frameworks, and data feedback loops. Establish standards for model quality, calibration, reproducibility, and responsible deployment while mentoring ML engineers and influencing organizational roadmaps. The role requires hands-on software and ML engineering expertise, production-scale model development, and strong statistical judgment.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Designs, builds, and operates reliable, high-performance inference platforms for large machine learning models in production. Responsibilities include request routing, batching, caching, autoscaling, GPU utilization, observability, distributed systems, performance engineering, capacity planning, and incident response. The role requires production experience with high-throughput services, LLM inference frameworks, Kubernetes, cloud platforms, and GPU architecture. Preferred experience includes globally distributed AI serving, model optimization, FinOps, open-source contributions, and external-facing AI APIs.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

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.

Own the end-to-end machine learning lifecycle, including data preparation, dbt pipelines, feature engineering, model development, deployment, monitoring, and evaluation. Apply ML to risk assessment, cost prediction, image analysis, and business operations. Contribute to architecture and strategy while partnering with Engineering, Product, Operations, and Business teams. Build scalable solutions using Python, AWS, Kafka, Django or FastAPI, PostgreSQL, and GitHub Actions.

Build and operate production machine learning training systems on Amazon SageMaker using AWS Trainium. The role focuses on optimizing PyTorch code for Trainium hardware, understanding NeuronCore compilation, diagnosing device-level issues, tuning distributed training for cost and throughput, and delivering production pipelines. The engineer will collaborate directly with client and internal teams in a forward-deployed engineering environment.

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.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

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.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Build production ML infrastructure for humanoid robotics, including multimodal data curation, annotation, versioning, large-scale pipelines, simulation and evaluation harnesses, model registries, deployment promotion, experiment tracking, and developer tooling. Partner with autonomy, data platform, and teleoperation teams on lifecycle contracts and technical direction while mentoring engineers through code and design reviews.

Build and deploy AI applications, agents, intelligent workflows, document-processing solutions, predictive analytics, and automation. The role spans AI use-case assessment, solution design, prototyping, evaluation, testing, production deployment, monitoring, governance, and responsible-AI controls. The engineer will integrate AI with enterprise systems, APIs, workflows, and data platforms while partnering with engineering, cybersecurity, governance, and mission teams in a federal environment.

Design, build, evaluate, and deploy AI agents and machine learning automation for engineering, product, delivery, and customer-facing workflows. Integrate AI into existing software, collaborate with technical and healthcare stakeholders, optimize systems for accuracy, latency, cost, and safety, and document development processes. The role emphasizes LLMs, agentic frameworks, production ML systems, human-in-the-loop safeguards, and healthcare-aware solutions.

Develops AI-assisted CAD/EDA automation, tools, and workflows for RTL design and verification. Responsibilities include troubleshooting EDA tools, building simulation and regression automation, improving CI/CD, analyzing infrastructure performance, supporting emulation and static sign-off, and collaborating with design, verification, infrastructure, cloud, and EDA vendor teams.

Founding Engineer responsible for building FilmoreAI’s production AI and data platform for construction equipment dealers. The role covers agent infrastructure, model routing, LLM extraction and reasoning, canonical data modeling, entity resolution, Python data pipelines, Azure cloud orchestration, evaluation, observability, and safe human-approved write-back. The engineer will work independently in an ambiguous startup environment and use agentic development tools to ship reliable systems with strong cost, latency, accuracy, and operational guardrails.

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.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.