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Entry Level Machine Learning Jobs in Sugar Land, TX

Sogeti

Entry Level Technologist

Houston, TX · On-site

$24.25 - $33.50/hr

As an Entry-Level Technology Consultant at Sogeti, you will join one of our core practices based on ... Explore emerging tech-from artificial intelligence/machine learning to cloud-native engineering ...

Sogeti

Entry Level Technologist

Houston, TX · On-site

$24.25 - $33.50/hr

As an Entry-Level Technology Consultant at Sogeti, you will join one of our core practices based on ... Explore emerging tech-from artificial intelligence/machine learning to cloud-native engineering ...

Capgemini

Entry Level Technologist

Houston, TX · On-site

As an Entry-Level Technology Consultant at Sogeti, you will join one of our core practices based on ... Explore emerging tech-from artificial intelligence/machine learning to cloud-native engineering ...

Energy Transfer Equity Lp

Operator Trainee

Houston, TX · On-site

The Operator Trainee is an entry level position that creates value by observing and learning ... loud machinery, and require appropriate personal protective equipment. * Occasional overnight ...

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Entry Level Machine Learning information

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How much do entry level machine learning jobs pay per hour?

As of Aug 17, 2026, the average hourly pay for entry level machine learning in Sugar Land, TX is $15.66, according to ZipRecruiter salary data. Most workers in this role earn between $13.99 and $17.02 per hour, depending on experience, location, and employer.

What types of projects can an entry level machine learning professional expect to work on in their first year?

As an entry-level machine learning professional, you’ll typically start by supporting more senior data scientists and engineers with tasks such as data cleaning, exploratory data analysis, and building baseline models. You may work on pilot projects like developing recommendation systems, automating simple classification tasks, or contributing to model evaluation and performance tuning. Collaboration with cross-functional teams—including software engineers, product managers, and domain experts—is common, providing valuable exposure to real-world business problems and laying a foundation for more complex responsibilities as you gain experience.

How to get into entry level machine learning with no experience?

Entry level machine learning roles typically require foundational knowledge in programming, statistics, and data analysis. Gaining skills through online courses, practicing with projects, and learning tools like Python, TensorFlow, or scikit-learn can help build a portfolio; internships or entry-level positions can provide practical experience.

What are the key skills and qualifications needed to thrive as an entry level machine learning engineer, and why are they important?

To thrive as an Entry Level Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially in Python), typically supported by a degree in computer science or a related field. Familiarity with machine learning frameworks like TensorFlow or PyTorch, version control systems like Git, and data analysis libraries is commonly required. Strong problem-solving abilities, curiosity, and effective communication skills help differentiate candidates in collaborative and fast-evolving environments. These skills and qualifications are essential for building, testing, and improving machine learning models that drive innovation and business value.

What is the difference between Entry Level Machine Learning vs Data Analyst?

AspectEntry Level Machine LearningData Analyst
Required CredentialsBachelor's in CS, Math, or related; some knowledge of programming and statisticsBachelor's in Statistics, Math, or related; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentTech companies, startups, research labs; focus on developing models and algorithmsBusiness, finance, marketing; focus on interpreting data and generating reports
Employer & Industry UsageTech, e-commerce, healthcare; roles involve building predictive modelsRetail, finance, consulting; roles involve analyzing data trends and insights

Entry Level Machine Learning roles focus on developing algorithms and models using programming and statistical skills, often in tech-driven environments. Data Analysts interpret and visualize data to support business decisions, typically using tools like Excel and SQL. While both roles require analytical skills, Machine Learning positions emphasize coding and model development, whereas Data Analysts focus on data interpretation and reporting.

What are entry level machine learning jobs?

Entry-level machine learning jobs focus on creating and using software for the development of artificial intelligence (AI). In this role, you may help program computer software, engineer mechanical solutions, help develop learning objectives, and use analytics to determine whether or not the technology created is meeting development goals. Many entry-level machine learning jobs focus on particular parts of the industry. For example, some companies focus on surveillance and intelligence, while others are creating technology for self-driving vehicles. Employers often use this position as a type of extended learning period to help you develop your skills before you start taking responsibility for major projects.

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Entry Level Machine Learning jobs near you
Infographic showing various Entry Level Machine Learning job openings in Sugar Land, TX as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $32,569 per year, or $15.7 per hour.

Reinforcement Learning Engineer, Grasping

Persona AI

Houston, TX • On-site

Other

Medical, PTO

Re-posted 1 hour ago

Job description

Persona AI is developing and commercializing rugged, multi-purpose humanoid robots that perform real work. Persona's founding team has a decades-long history in humanoid robotics, bionics, and product development delivering robust hardware that has touched the stars, worked miles below the surface of the ocean, roamed Disney Parks, and has even been featured on a US postage stamp. Our mission is focused squarely on shipping beautiful, reliable products at massive scale, while building a customer-focused team to achieve these aims.
Role Overview:
We are looking for a Reinforcement Learning Engineer to join our Manipulation team, focused on dexterous grasping. Our goal is to ship capable, reliable grasping policies on real hardware with high-DOF robotic hands. We are looking for someone who can follow recent advances in reinforcement learning and related learning-based methods, judge what is practically useful, and adapt those ideas on our platform. If you are earlier in your career but exceptional, we want to hear from you; equally, a more experienced candidate who brings deep RL expertise will thrive here.
Your Role:
  • Train and iterate on reinforcement learning policies for complex grasping tasks including functional grasping, tool use, in-hand manipulation, and environment interaction.
  • Implement and refine sim-to-real transfer pipelines to bridge the gap between simulation and physical robotic hand performance.
  • Develop reward functions, curriculum strategies, and training environments in MuJoCo and Isaac Lab.
  • Run experiments on real robots alongside simulation, evaluating and debugging policy behavior on hardware.
  • Monitor, evaluate, and adapt state-of-the-art research in learning-based grasping to deploy on our humanoid platform.
  • Collaborate with the rest of the software team to deploy end-to-end grasping systems.
  • Benchmark and evaluate grasp policies across object diversity, clutter scenes, and real-world uncertainties.
  • Integrate tactile sensing and feedback into grasp policies for robust, force-aware manipulation.

We're Looking For:
  • BS, MS, or PhD in Robotics, Computer Science, Machine Learning, or a related field.
  • 2+ years of hands-on experience in reinforcement learning for robotic manipulation; exceptional entry level candidates from relevant research labs will be considered.
  • Demonstrated ability to read, understand, and implement ideas from recent robotics and machine learning research.
  • Hands-on experience training RL agents for robotic manipulation tasks, including reward shaping and policy evaluation.
  • Experience with sim-to-real transfer: domain randomization, physics tuning, or real-world policy validation on hardware.
  • Proficiency in Python and deep learning frameworks (PyTorch, JAX), along with RL libraries such as rsl_rl or skrl.
  • Experience preparing meshes and collision geometries for RL environments in simulators such as MuJoCo and/or Isaac Sim.

Bonus Qualifications:
  • Experience deploying RL-trained policies on physical robotic hands.
  • Experience with tactile sensors and integrating tactile feedback into learned grasp policies.
  • Experience with contact-rich manipulation and force/torque estimation.
  • Familiarity with other learning-based approaches such as behavior cloning, imitation learning, or diffusion-based policy methods.
  • Publications or project work at top-tier venues (CoRL, RSS, ICRA) on grasping or dexterous manipulation.
  • Experience in a humanoid robot startup environment.

Why Join Persona AI?
  • We offer competitive compensation, a performance-based bonus, 99% employer covered medical benefits, early-stage equity, competitive PTO, and a company-wide paid winter break between December 24th and January 2nd.
  • You'll shape technology that's redefining the possibilities of robotics and human interaction.
  • Work alongside passionate teammates who value creativity, collaboration, and continuous learning.
  • Enjoy full access to advanced tools, hardware labs, and the freedom to push the boundaries of what robots can do.
    Persona AI is an Equal Opportunity Employer.
    All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, age, disability, veteran status, or any other characteristic protected by applicable federal, state, or local law.

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