Machine Learning Jobs in Richardson, TX

Design and implement full-stack Java solutions; define business processes and architecture; work with cloud platforms (AWS/Azure/VMware), infrastructure orchestration, scripting, configuration management, and big-data technologies; apply machine learning libraries; operate in a consulting environment with regular travel.

Lead AI/ML-focused information security for enterprise agentic capabilities. Advise projects and CSOC teams on threat mitigation, compliance, automation, monitoring, and incident response. Partner with engineering and stakeholders to secure cloud, generative AI, and customer servicing platforms while driving data-driven decisions and continuous improvements.

The Senior Product Owner leads product development for Mainframe solutions at Broadcom, collaborating with cross-functional teams to deliver features based on customer needs and product roadmaps.

Design, develop, and deploy production-grade full-stack AI applications and agentic AI solutions. Build AI agents, multi-agent workflows, RAG pipelines, LLM applications, conversational interfaces, and scalable microservices using Java or Python and AWS services. Implement prompt engineering, evaluation, observability, guardrails, CI/CD, containerization, APIs, and infrastructure as code. Troubleshoot and optimize production systems in enterprise environments.

Lead workload-driven AI/ML systems architecture across hardware and software. Own workload characterization, analytical performance modeling, KPI definition, and hardware performance analysis to guide SoC, memory, and HW/SW co-design decisions. Evaluate compute, memory, latency, and software bottlenecks; collaborate with CPU, compiler, runtime, and ML framework teams; present recommendations to leadership; and mentor junior engineers. The role requires expertise in processor and memory architecture, edge AI acceleration, performance modeling, and cross-functional technical leadership.

Lead RTL development and microarchitecture for an AI-enabled RISC-V CPU core. Responsibilities include designing vector and matrix-processing engines, optimizing power, performance, area, and timing, developing detailed specifications, supporting functional verification, debugging simulations, and partnering on physical design validation. The role requires expertise in high-performance processor architecture, datapath design, vector and matrix processing, and hardware design languages.

Summer data science internship working on ML projects (anomaly detection, numerical optimization, PII/PHI NLP). Interns will develop models, write high-quality Python, use Linux and development tools, work with git, Docker, CI/CD and Kubernetes, participate in code reviews and agile rituals, and present results to technical and non-technical audiences.

Develop and productionize AI features from PoC to scalable deployments. Write production-ready Python code, implement testing and debugging, refine user stories, participate in ethical and security reviews, and collaborate with stakeholders to align AI solutions with business needs.

Design, develop, test, and support scalable AI/ML-powered enterprise applications and microservices. Implement LLMs, Generative AI, and RAG solutions; build RESTful APIs, automated tests, CI/CD pipelines; troubleshoot production issues and collaborate in Agile teams.

The ML Engineer will develop and deploy machine learning models, collaborate with teams, analyze datasets, and improve model performance.

Design and implement high-performance APIs and microservices using Python and AWS, focusing on generative AI and scalable applications.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Design, develop, and deploy scalable machine learning models and data pipelines for production. Collaborate with data scientists, engineers, and stakeholders, optimize model performance and scalability, automate deployments, monitor production systems, and evaluate emerging ML and cloud technologies to drive business outcomes.

Deploy, maintain, and improve production ML workflows and platforms for media measurement and customer analytics on AWS. Build SageMaker jobs and endpoints, orchestrate pipelines with Step Functions, Lambda, and Airflow, manage model artifacts and CI/CD, troubleshoot production issues, and collaborate on platform enhancements and operational best practices.