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Executive Full Stack Machine Learning Engineer Jobs in Texas

Lead Machine Learning Engineer

Plano, TX · On-site +1

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Machine Learning Engineer We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work ...

This job will validate and develop machine learning models and algorithms to solve complex problems. You will work closely with senior engineers, data scientists, and product teams to enhance ...

Machine Learning Engineer Location - New York / Dallas, TX (Need Only Local Candidates) Exp need - 8+ years Local to NY/NJ or Dallas only! * 1.5+ years of software development in one or more ...

Senior Machine Learning Engineer

Houston, TX · On-site

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing ...

Senior Machine Learning Engineer

Houston, TX

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing ...

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

Senior Machine Learning Engineer

Houston, TX

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing ...

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source ...

AI/ Machine Learning Engineer

Austin, TX

$96K - $132K/yr

Mid-Level AI/Machine Learning Engineer Join one of the top 10 global powerhouses transforming the future of Market. As an AI / Machine Learning Engineer, you'll play a key role in designing ...

Comscore, Total Visits, March 2025) Day to Day As a Machine Learning Engineer III, you will be a team lead. You will own one of the team's major workstreams, help drive technical direction for the ...

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

We are looking for an experienced and innovative individual contributor to fill the position of AI Machine Learning Engineer within our AI Center of Excellence group based in Houston, TX. The ...

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Executive Full Stack Machine Learning Engineer information

What is the difference between Executive Full Stack Machine Learning Engineer vs Data Scientist?

AspectExecutive Full Stack Machine Learning EngineerData Scientist
CredentialsBachelor's/Master's in CS, Engineering, or related; often requires experience in ML and full stack developmentBachelor's/Master's in Data Science, Statistics, or related; strong analytical and statistical skills
Work EnvironmentDevelops end-to-end ML solutions, integrates backend and frontend, collaborates with engineering teamsAnalyzes data, builds models, visualizes insights, often in research or analytics teams
Industry UsageUsed in tech companies, startups, and enterprises deploying ML productsCommon in research institutions, analytics firms, and data-driven organizations

The Executive Full Stack Machine Learning Engineer focuses on building and deploying complete ML solutions, combining software engineering and data science skills. In contrast, Data Scientists primarily analyze data and develop models without necessarily handling full stack development. Both roles require strong technical credentials but differ in scope and daily tasks.

What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in Texas? The most popular types of Full Stack Machine Learning Engineer jobs in Texas are:
What cities in Texas are hiring for Executive Full Stack Machine Learning Engineer jobs? Cities in Texas with the most Executive Full Stack Machine Learning Engineer job openings:

Other

Posted 20 days ago


Job description

AI Models Team Member

Splunk, a Cisco company, is building a safer, more resilient digital world with an end-to-end, full-stack platform designed for hybrid, multi-cloud environments. Join the AI Models team at Splunk, where we advance the state of AI for high-volume, real-time, multi-modal machine-generated data — including logs, time series, traces, and events. We combine deep AI research expertise with the scale and operational excellence of Splunk and Cisco's global engineering capabilities. Our work spans networking, security, observability, and customer experience — designing and deploying foundation models that enhance reliability, strengthen security, prevent downtime, and deliver predictive insights across Splunk Observability, Security, and Platform at enterprise scale. You'll be part of a culture that values technical excellence, impact-driven innovation, and cross-functional collaboration — all within a flexible, growth-oriented environment.

Your Impact
  • Set and Drive Vision: Define and champion the strategic vision for AI and foundation models across Splunk and Cisco platforms, shaping the research and technology roadmap to anticipate and address industry-defining challenges.
  • Architect and Lead Breakthroughs: Lead the end-to-end lifecycle of research, design, and deployment for large-scale foundation models targeting machine-generated data, with deep focus on logs and complementary modalities (time series, traces, events).
  • Influence at Scale: Partner with executive leadership, engineering, product, and data science teams to ensure AI solutions align with broader organizational objectives, product strategies, and customer needs.
  • Mentorship and Thought Leadership: Cultivate organizational excellence by mentoring senior technical talent, fostering research communities, and driving best practices in AI across global teams.
  • Foster Innovation: Embed cutting-edge research and technological advances into products, driving sustained competitive advantage and transformation at enterprise scale.
Minimum Qualifications:
  • PhD in Computer Science, or related quantitative field, plus 7+ years of industry research experience.
  • Proven track record in at least one of the following areas: large language modeling for both structure and unstructured data, deep learning-based time series modeling, advanced anomaly detection, and multi-modality modeling.
  • Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
  • Experience translating research ideas into production systems.
Preferred Qualifications:
  • Deep NLP & Domain-Adapted LLMs: Background in building and adapting large-scale language models (e.g., T5, BERT, LLaMA, GPTs) for specialized domains including structured/unstructured logs, text, and event sequences.
  • Log Analytics Expertise – In-depth knowledge of structured/unstructured system logs, event sequence analysis, anomaly detection, and root cause identification.
  • Advanced Anomaly Detection – Experience creating robust, scalable approaches (statistical, deep learning, or hybrid) for high-volume, real-time logs data.
  • Multi-Modal AI Modeling – Strong track record fusing logs, time series, traces, tabular data, and graphs for foundation models tackling complex operational insights.
  • Large-Scale Training & Optimization – Experience optimizing model architectures, distributed training pipelines, and inference efficiency to minimize cost and latency while preserving accuracy.
  • MLOps & Continuous Learning – Fluency in automated retraining, drift detection, incremental updates, and production monitoring of ML models.
  • Strong Research Track Record – Publications in top AI/ML conferences or journals (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, ACL, KDD) demonstrating contributions to state-of-the-art methods and real-world applications.