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Founding Machine Learning Engineer Jobs in Kemp, TX

Machine Learning Engineer, Specialist

Dallas, TX

$113.30K - $136K/yr

Performs the development and programming of machine learning integrated software algorithms to structure, analyze, and leverage data in a production environment. Leverages detailed understanding of ...

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 ...

New

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Certifications in artificial intelligence, machine learning, or cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Microsoft ...

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Founding Machine Learning Engineer information

See Kemp, TX salary details

$25.7K

$105.2K

$158.1K

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

As of May 29, 2026, the average yearly pay for founding machine learning engineer in Kemp, TX is $105,215.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,900.00 and $126,600.00 per year, depending on experience, location, and employer.

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

To thrive as a Founding Machine Learning Engineer, you need deep expertise in machine learning algorithms, software engineering, and data science, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, TensorFlow or PyTorch, cloud platforms, and experience deploying ML models in production are typically required. Strong problem-solving abilities, entrepreneurial mindset, and excellent communication skills set standout candidates apart. These skills and qualities are vital for driving innovation, building scalable solutions from scratch, and collaborating within a fast-paced startup environment.

What are some unique challenges and expectations for a Founding Machine Learning Engineer in an early-stage startup?

As a Founding Machine Learning Engineer, you'll face the unique challenge of building the company's machine learning infrastructure from the ground up, often with limited resources and rapidly evolving requirements. You'll be expected to wear many hats, from designing and deploying models to setting up data pipelines and collaborating closely with product and engineering teams. Your role will also involve making critical decisions about technology stacks and best practices that will shape the company's technical direction. Additionally, you'll have significant influence on the company's culture and have ample opportunities for growth as the team expands.

What is a Founding Machine Learning Engineer?

A Founding Machine Learning Engineer is one of the first technical team members at a startup who specializes in designing, building, and deploying machine learning systems. This role involves working closely with the founders to set the technical direction, build core AI products, and establish best practices for data and model development. In addition to hands-on coding and experimentation, a Founding Machine Learning Engineer often influences product decisions and helps shape the company's engineering culture. The role typically requires a blend of deep technical expertise, startup agility, and a willingness to tackle both high-level strategy and low-level engineering tasks.
What cities near Kemp, TX are hiring for Founding Machine Learning Engineer jobs? Cities near Kemp, TX with the most Founding Machine Learning Engineer job openings:
Machine Learning Engineer, Specialist

Machine Learning Engineer, Specialist

Vanguard

Dallas, TX

$113.30K - $136K/yr

Full-time

Posted 7 days ago


Job description

We're the AI Center of Excellence within the Chief Data and Analytics Office. We work on projects that enable AI practitioners build AI products quickly and confidently. We work on many open problems around AI safety and evaluations. One way that we do so is by finding vulnerabilities in Gen AI applications with an automated adversarial testing service. We're looking for people who are technically sharp and effective communicators to help design and deploy this service. Performs the development and programming of machine learning integrated software algorithms to structure, analyze, and leverage data in a production environment. Leverages detailed understanding of machine learning models and their deployment architecture.


Responsibilities:

  • Develops complex data pipelines and implements data engineering design principles for iterative data pipeline development to drive scale and efficiency. Proficient in model development environments and coding best practices to enable model deployment.

  • Integrates and optimizes existing data and model pipelines in a production environment. Identifies and diagnoses data inconsistencies and errors, documents assumptions, and forages to fill data gaps. Applies knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning concepts to create self-running artificial intelligence (AI) systems to automate predictive models. Proficient in SDLC processes and related tools and technologies.

  • Partners with data science teams to review model ready dataset document/feature documentation. Develops data model design and document and reviews for completeness with data science teams.

  • Partners with data science teams to understand data requirements, performs data discovery for model development. Performs detailed analysis of raw data sources for data quality, applies business context, and model development needs. Drives efficiency through the use of data discovery tools.

  • Engages with internal stakeholders to understand and probe business processes and develop hypotheses. Brings structure to requests and translates requirements into an analytic approach. Participates in and influences ongoing business planning and departmental prioritization activities.

  • Writes model monitoring scripts as needed. Diagnoses root causes based on model monitoring alerts and triages issues. Coordinates and plans response to model monitoring alerts and resolves issues.

  • Serves as a machine learning engineering subject matter expert on cross functional teams for large strategic initiatives and contributes to the growth of the Vanguard analytic community.

  • Participates in special projects and performs other duties as assigned.

  • Designs and implements statistical analysis frameworks and protocols to uncover business trends across functions like marketing, supply chain, and economics.

  • Applies advanced data mining techniques to build scalable models for deriving actionable insights from large-scale data sets.

  • Explores and integrates evolving methodologies in machine learning and big data science to enhance decision-making strategies.


Qualifications:

  • Minimum 8 years of experience in machine learning engineering or data science roles

  • Bachelor’s degree (B.E./B.Tech) in Computer Science, Data Engineering, AI/ML, or related fields, or a Master’s degree/Diploma in Computer Science or Data Science

  • Strong expertise in data pipeline development, statistical modeling, SDLC, AI/ML concepts, model monitoring, and stakeholder engagement across domains

Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.