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Junior Machine Learning Engineer Jobs in Spring, 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 ...

Pivotal Solutions

Senior AI Engineer

Houston, TX · On-site

$99.80K - $137K/yr

Design, develop, and deploy advanced AI and machine learning models to solve complex business ... Mentor junior engineers and provide technical guidance on AI best practices, model development, and ...

Cisco

Principal Machine Learning Engineer

Houston, TX · On-site

$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 ...

Pivotal Solutions Inc

Senior AI Engineer

Houston, TX

$117K - $154.20K/yr

Job Posting Responsibilities Design, develop, and deploy advanced AI and machine learning models to ... Mentor junior engineers and provide technical guidance on AI best practices, model development, and ...

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Junior Machine Learning Engineer information

See Spring, TX salary details

$29.8K

$63.9K

$97.4K

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

As of Jun 2, 2026, the average yearly pay for junior machine learning engineer in Spring, TX is $63,894.00, according to ZipRecruiter salary data. Most workers in this role earn between $43,200.00 and $71,200.00 per year, depending on experience, location, and employer.

What Does a Junior Machine Learning Engineer Do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

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

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

What kinds of projects and responsibilities can a Junior Machine Learning Engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

What is the difference between Junior Machine Learning Engineer vs Data Scientist?

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

More about Junior Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Spring, TX? The most popular types of Machine Learning Engineer jobs in Spring, TX are:
What are popular job titles related to Junior Machine Learning Engineer jobs in Spring, TX? For Junior Machine Learning Engineer jobs in Spring, TX, the most frequently searched job titles are:
What job categories do people searching Junior Machine Learning Engineer jobs in Spring, TX look for? The top searched job categories for Junior Machine Learning Engineer jobs in Spring, TX are:
What cities near Spring, TX are hiring for Junior Machine Learning Engineer jobs? Cities near Spring, TX with the most Junior Machine Learning Engineer job openings:
Junior Machine Learning Engineer jobs near you

Principal Machine Learning Engineer

Webex Events (formerly Socio)

Houston, TX

Other

Posted 4 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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