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Junior Machine Learning Jobs in Dallas, TX (NOW HIRING)

Newrez

Senior Data Scientist

Coppell, TX · On-site

The Senior Data Scientist will lead the data science and AI roadmap, develop advanced machine learning solutions, and ensure compliance with regulatory standards while mentoring junior team members.

Deloitte

Senior AI Engineer - SFL Scientific

Dallas, TX

$103K - $142K/yr

Work with clients to design, develop, and deploy new architectures to support machine learning ... Mentor, motivate, and coach junior members on technical best practices and inspire professional ...

Waabi

Research Scientist, Simulation Agents

Dallas, TX · On-site +1

$158K - $269K/yr

... Mentor junior scientists and interns; foster a culture of scientific rigor and rapid experimentation. - Publish high-impact research at top-tier conferences in machine learning or robotics.

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All JobsMachine Learning JobsJunior Machine Learning JobsJunior Machine Learning Jobs in Dallas, TX

Junior Machine Learning information

See Dallas, TX salary details

$7

$26

$46

How much do junior machine learning jobs pay per hour?

As of Aug 7, 2026, the average hourly pay for junior machine learning in Dallas, TX is $26.67, according to ZipRecruiter salary data. Most workers in this role earn between $16.15 and $32.84 per hour, depending on experience, location, and employer.

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

AspectJunior Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some experience with ML toolsBachelor's or Master's in CS, Statistics, or related; strong programming and statistical skills
Work EnvironmentEntry-level projects, supervised tasks, team collaborationAdvanced analysis, model development, cross-functional teams
Industry UsageCommon in tech companies, startups, research labsWidespread across industries like finance, healthcare, tech

Junior Machine Learning roles focus on foundational ML tasks and learning on the job, while Data Scientists handle complex data analysis, model building, and strategic insights. The roles differ mainly in experience level and scope of responsibilities, but both require strong technical skills and familiarity with data tools.

What does a junior machine learning engineer do?

A Junior Machine Learning Engineer assists in the development and implementation of machine learning models and algorithms under the supervision of more experienced engineers. They typically help with data collection, cleaning, feature engineering, model training, and evaluation. Junior engineers may also write code, test prototypes, and contribute to improving model performance while learning best practices in the field. Their role often involves collaborating with data scientists and software engineers to integrate machine learning solutions into products or services.

What types of projects and tasks can a junior machine learning professional typically expect to work on in their first year?

As a Junior Machine Learning professional, you’ll often support senior data scientists and engineers by preparing data, implementing basic algorithms, and assisting with model evaluation. Your daily tasks may include data cleaning, feature engineering, running experiments, and writing code to automate data pipelines. You might also help document processes and present your findings to team members. While the work is often collaborative, you’ll have opportunities to take ownership of smaller projects and progressively contribute to larger initiatives as you gain experience.

What are the key skills and qualifications needed to thrive as a junior machine learning engineer?

To thrive as a Junior Machine Learning Engineer, you need a solid understanding of programming (especially Python), basic statistics, linear algebra, and familiarity with machine learning concepts, typically supported by a relevant degree or coursework. Proficiency in tools and frameworks like scikit-learn, TensorFlow, PyTorch, and version control systems such as Git is often expected. Strong problem-solving abilities, curiosity, and effective communication are crucial soft skills for collaborating with teams and explaining technical concepts. These skills and qualities are important because they enable you to contribute effectively to building, testing, and improving machine learning models in real-world applications.
What are the most commonly searched types of Machine Learning jobs in Dallas, TX? The most popular types of Machine Learning jobs in Dallas, TX are:
What are popular job titles related to Junior Machine Learning jobs in Dallas, TX? For Junior Machine Learning jobs in Dallas, TX, the most frequently searched job titles are:
What job categories do people searching Junior Machine Learning jobs in Dallas, TX look for? The top searched job categories for Junior Machine Learning jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Junior Machine Learning jobs? Cities near Dallas, TX with the most Junior Machine Learning job openings:
Junior Machine Learning jobs near you
Infographic showing various Junior Machine Learning job openings in Dallas, 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 $55,465 per year, or $26.7 per hour.

Corporate Treasury, Dallas, Vice President, Software Engineering

Goldman Sachs, Inc.

Dallas, TX

Full-time

Re-posted 9 days ago

Goldman Sachs rating

8.3

Company rating: 8.3 out of 10

Based on 27 frontline employees who took The Breakroom Quiz

47th of 170 rated banks

Job description

We are seeking an AI Engineer with 5+ years of experience to join the Liquidity Risk technology team. In this role, you will design, build, and deploy AIdriven solutions that enhance liquidity risk monitoring, stress testing, scenario generation, and decision support. You will work closely with liquidity risk managers, quantitative teams, and engineering partners to translate complex risk problems into scalable, productionready AI systems.

Key Responsibilities
  • Design, develop, and deploy machine learning and AI models to support liquidity risk metrics, stress scenarios, earlywarning indicators, and forecasting.
  • Build endtoend AI pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
  • Apply supervised, unsupervised, and timeseries modeling techniques to largescale financial and transactional datasets.
  • Partner with liquidity risk managers and quantitative teams to translate regulatory and business requirements into AIdriven solutions.
  • Optimize Agents' performance, scalability, and reliability in distributed and cloudbased environments.
  • Contribute to the firm's AI engineering standards, including testing, model documentation, and production controls.
  • Mentor junior engineers and contribute to code reviews, design discussions, and architecture decisions.
Required Qualifications
  • 5+ years of professional experience as an AI Engineer in a production environment.
  • Handson experience in integrating LLM models using agents and developing monitoring and observability tools for those agents.
  • Experience with AWS Bed Rock platform especially using AWS Agent core for deploying agents
  • Experience in developing agents using Google ADK or Lang Graph frameworks and deploying them on AWS
  • Exposure to distributed computing frameworks and workflow orchestration tools (e.g., Airflow).
  • Strong proficiency in Python and experience with ML/AI libraries such as PyTorch, or similar.
  • Solid understanding of machine learning fundamentals, including model selection, biasvariance tradeoffs, and evaluation techniques.
  • Experience working with large, structured datasets using SQL and distributed data platforms (cloud data warehouses).
What We Offer
  • Opportunity to work at the intersection of AI, engineering, and liquidity risk at a global scale.
  • Highimpact role influencing how the firm measures and manages liquidity under stress.
  • Collaborative environment with exposure to senior risk managers, quants, and technology leaders.
  • Ongoing learning, development, and career progression within the Liquidity and Engineering organizations.

What Goldman Sachs employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Goldman Sachs logo

About Goldman Sachs

Sourced by ZipRecruiter

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1869

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