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Machine Learning Engineer Associate Jobs in Crosby, TX

Cisco

Principal Machine Learning Engineer

Houston, TX · On-site

The Principal Machine Learning Engineer will define the vision for AI across platforms, lead the lifecycle of large-scale foundation models, and collaborate with various teams to ensure alignment ...

Talentry, LLC

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Machine Learning Guru (Autonomous Navigation)**

Houston, TX · Remote

$200K - $300K/yr

Senior/Principal Machine Learning Engineer 200-300k Remote position possible Description * Develop solutions for autonomous driving, from experimentation to full commercialization. * Explore new ...

Cisco

Principal Machine Learning Engineer

Houston, TX

$291.50K - $369.10K/yr

Partner with executive leadership, engineering, product, and data science teams to ensure AI ... Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) * Experience ...

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Machine Learning Engineer Associate information

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$42.1K

$83.8K

$133.8K

How much do machine learning engineer associate jobs pay per year?

As of Jun 1, 2026, the average yearly pay for machine learning engineer associate in Crosby, TX is $83,750.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,400.00 and $96,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Associate, and why are they important?

To thrive as a Machine Learning Engineer Associate, you need a solid understanding of programming (especially Python), mathematics, and foundational machine learning concepts, typically supported by a relevant degree or coursework. Familiarity with tools and frameworks like TensorFlow, PyTorch, scikit-learn, and experience with version control systems such as Git are essential. Strong problem-solving abilities, communication skills, and a collaborative mindset help you work effectively within technical teams. These competencies ensure you can develop, implement, and improve machine learning models that deliver actionable insights and drive business value.

What are some common challenges faced by Machine Learning Engineer Associates when deploying models to production?

Machine Learning Engineer Associates often encounter challenges such as ensuring model scalability, managing data pipeline reliability, and addressing issues with model drift after deployment. Collaborating closely with data engineers and software developers is essential to integrate models seamlessly into existing systems. Additionally, balancing model performance with resource constraints and maintaining clear documentation for reproducibility are important aspects of the role. Gaining familiarity with deployment tools and best practices can help overcome these hurdles.

What are Machine Learning Engineer Associates?

Machine Learning Engineer Associates are entry-level professionals who help design, build, and maintain machine learning models and systems. They typically work under the guidance of senior engineers, assisting in data preprocessing, model training, and testing. Their responsibilities may include implementing algorithms, evaluating model performance, and deploying solutions to production environments. This role requires a strong foundation in programming, statistics, and machine learning principles, often acquired through education or internships.
What are the most commonly searched types of Machine Learning Engineer jobs in Crosby, TX? The most popular types of Machine Learning Engineer jobs in Crosby, TX are:
What job categories do people searching Machine Learning Engineer Associate jobs in Crosby, TX look for? The top searched job categories for Machine Learning Engineer Associate jobs in Crosby, TX are:
What cities near Crosby, TX are hiring for Machine Learning Engineer Associate jobs? Cities near Crosby, TX with the most Machine Learning Engineer Associate job openings:
Machine Learning Engineer Associate jobs near you

Principal Machine Learning Engineer

Webex Events (formerly Socio)

Houston, TX

Other

Posted 3 days ago

Job description

Splunk AI Models Team

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.

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