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Junior Machine Learning Engineer Jobs in Spring, TX

NobleAI

Lead Machine Learning Engineer

Houston, TX · On-site +1

$97K - $128K/yr

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI, you will be responsible for architecting, building, and deploying intelligent features to our VIP ...

NobleAI

Lead Machine Learning Engineer

Houston, TX · Remote

$104K - $138K/yr

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI, you will be responsible for architecting, building, and deploying intelligent features to our VIP ...

NobleAI

Lead Machine Learning Engineer

Houston, TX · On-site +1

$97K - $128K/yr

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI, you will be responsible for architecting, building, and deploying intelligent features to our VIP ...

Pivotal Solutions

Senior AI Engineer

Houston, TX · On-site

$99K - $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 ...

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Showing results 21-40

All JobsMachine Learning Engineer JobsJunior Machine Learning Engineer JobsJunior Machine Learning Engineer Jobs in Spring, TX

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 Aug 6, 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 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.

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.

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
Infographic showing various Junior Machine Learning Engineer job openings in Spring, TX as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $63,894 per year, or $30.7 per hour.

Lead Machine Learning Engineer

NobleAI

Houston, TX • On-site, Remote

$97K - $128K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 15 days ago

Job description

At NobleAI, we believe that energy, material science and chemistry are key to building a sustainable world and that artificial intelligence is essential to unlock this potential. NobleAI leverages innovative Science-Based AI technology to revolutionize energy workflows, materials development, and chemical designs. We enable companies to accelerate innovation and reduce costs in developing sustainable technologies and products. 

We're a team of excellence-driven individuals who value thoughtfulness and respect while focusing on delivering products that empower engineers and researchers to create better solutions faster.

At NobleAI, we are developing the next generation of intelligent chemical informatics platform. Our goal is to create a seamless, intuitive experience that empowers users to achieve unprecedented productivity in data processing, visualization, and model building. We are seeking a forward-thinking team member who thrives on innovation, collaboration, and rapid iteration, and has the ability to solve challenging problems in science and technology. 

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI, you will be responsible for architecting, building, and deploying intelligent features to our VIP platform. This role is ideal for individuals passionate about the cutting edge of LLMs and eager to build AI systems that can reason, plan, and act. 

Join us in building a more sustainable world through the power of AI and scientific innovation.

Requirements

  • Design domain specific AI systems and chatbots capable of complex dialogue management and workflow execution via tools, API calls and multi step tasks based on user goals, multi-agent orchestration.
  • Collaborate with scientists to assess, fine-tune, and deploy LLMs on domain specific data to support accuracy measurement for use cases
  • Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks, guardrail and assessment mechanisms for end to end lifecycle for customized models.
  • Collaborate with product and software engineers to integrate the features into our platform.
  • Establish prompt engineering and data management best practices for transparency and governance.
  • Establish best practices for monitoring and evaluation of data and models across the model lifecycle (development, testing, and production)
  • Keep a pulse on the latest advancements in NLP, LLMs, and agentic AI research, and act as a subject matter expert on architecture decisions on platform and use cases.


What We're Looking For

  • MSc (preferred) or BSc in Computer Science, Artificial Intelligence, or a related quantitative field.
  • 5+ years of hands-on experience building and deploying AI/ML systems, with a strong focus on Natural Language Processing (NLP).
  • Proven experience designing and shipping chatbots, virtual assistants, or agentic systems using modern LLM-based architectures.
  • Strong programming proficiency in Python (5+ years) and deep experience with core ML/NLP libraries such as PyTorch, TensorFlow
  • Hands-on experience with LLM agent frameworks for building complex, tool-using applications, multi-agent orchestration, or establishing MCP services for platform capabilities.
  • Demonstrated experience with Retrieval-Augmented Generation (RAG), including the use of vector databases like Pinecone, Weaviate, or ChromaDB.
  • Familiarity with techniques for fine-tuning LLMs (e.g., LoRA/QLoRA) and experience working with open-source models (e.g., Llama, Mistral) or major model APIs (e.g., OpenAI, Anthropic).
  • 5+ years of experience with cloud platforms (Azure preferred) and familiarity with deploying AI models as scalable microservices using Docker and Kubernetes (KFP, KServe).
  • Solid software engineering fundamentals, including version control (Git), automated testing, and CI/CD principles.
  • Excellent communication skills with the ability to articulate complex technical ideas to both technical and non-technical stakeholders.

Benefits

We offer great pay & benefits.

  • Top-tier health benefits coverage, including medical, dental, vision, disability and life insurance
  • Flexible paid time off & generous holidays
  • Remote-first with co-working access at Industrious offices
  • 401(k) with employer match 
  • Equity package
  • Salary Range $190,000 - $205,000 (Depending on experience & Geographic location) 
  • Performance-based bonus plan

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