Senior ML Engineer Jobs in Dallas, TX
Senior ML Engineer jobs in Dallas are concentrated in Uptown, the Platinum Corridor, and Las Colinas, driven by demand in financial services, healthcare technology, and enterprise software. Employers posting right now include GEICO, DEPLOY, and AppFolio. See the openings below and apply to the ones that match your experience.
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DEPLOY has been retained by a Dallas, Texas based firm that provides unique SaaS products to automotive dealerships across the United States.
DEPLOY is a Mid Level Machine Learning Engineer for an in office role in Dallas.
DEPLOY's client will hire smart and ambitious doers and set them loose in an exciting and complex technology business where they will build, sell, and deploy call tracking, CRM integration and Artificial Intelligence solutions in a dynamic business environment.
Our solutions attack one of the biggest business problems in existence today: The Phone.
As a member of the Machine Learning (ML) Team you will:
Design & Deploy ML Models:
Develop, fine-tune, and deploy NLP and LLM-driven models using frameworks like PyTorch, TensorFlow, or Hugging Face, ensuring they are robust, scalable, and production-ready.
Build APIs & Pipelines:
Construct APIs and automated pipelines that integrate real-time or batch data (e.g., call transcripts) to power conversational AI features in our products.
MLOps & Model Monitoring:
Implement MLOps best practices—model versioning, automated CI/CD pipelines (Azure), containerization (Docker), orchestration (Kubernetes)—to ensure reliable, repeatable deployments.
Employ infrastructure-as-code (Terraform, AWS CDK) to maintain scalable, cloud-based ML environments on AWS (SageMaker, EC2/Fargate).
Experiment Tracking & Performance:
Track experiments, artifacts, and metrics using MLFlow, Weights & Biases, or ML Studio.
Continuously monitor performance (Prometheus, CloudWatch), troubleshoot issues, and optimize models for latency, accuracy, and scalability.
Cross-Functional Collaboration:
Partner with data engineers, product managers, and senior ML engineers to align technical solutions with business goals.
Contribute to evolving data pipelines and guide improvements based on user feedback and performance metrics. Mentorship & Best Practices:
Participate in code reviews, pair programming, and technical discussions.
Serve as a mentor to junior team members, sharing best practices in ML engineering, MLOps, and model lifecycle management.
Our Ideal Candidates:
3+ years in ML engineering, with hands-on NLP/LLM expertise, ideally deploying transformer-based models (e.g., GPT, BERT) in production.
Strong Python skills and experience with deep learning frameworks (PyTorch/TensorFlow/HuggingFace), plus familiarity with cloud-based ML (AWS SageMaker, EC2), containerization (Docker), and orchestration (Kubernetes).
Working knowledge of CI/CD (Azure), infrastructure-as-code (Terraform/CDK), and experiment tracking (MLFlow, W&B, ML Studio).
A proactive, collaborative approach; eagerness to learn from senior engineers and improve both ML and MLOps skill sets.
Experience with AWS event-driven and streaming architectures (e.g., EventBridge, SQS) to manage large-scale, real-time data handling and ingestion pipelines.
Understanding of security, compliance, and reliability best practices in ML deployments.
Prior work with voice recognition, sentiment analysis, or conversational AI frameworks.
What's in It for You?
Competitive salary package (immediate PTO).
Full benefits package.
Fidelity 401k with company match.
Fun perks including a monthly gym reimbursement, a monthly wellness reimbursement, and a monthly reading allowance.
Weekly catered breakfast, Employee of the Month rewards, regular company events, and bi-weekly happy hours.
Opportunities for continued career growth within the organization.
Fun and collaborative work environment.
DEPLOY has been retained by a Dallas, Texas based firm that provides unique SaaS products to automotive dealerships across the United States.
DEPLOY is a Mid Level Machine Learning Engineer for an in office role in Dallas.
DEPLOY's client will hire smart and ambitious doers and set them loose in an exciting and complex technology business where they will build, sell, and deploy call tracking, CRM integration and Artificial Intelligence solutions in a dynamic business environment.
Our solutions attack one of the biggest business problems in existence today: The Phone.
As a member of the Machine Learning (ML) Team you will:
Design & Deploy ML Models:
Develop, fine-tune, and deploy NLP and LLM-driven models using frameworks like PyTorch, TensorFlow, or Hugging Face, ensuring they are robust, scalable, and production-ready.
Build APIs & Pipelines:
Construct APIs and automated pipelines that integrate real-time or batch data (e.g., call transcripts) to power conversational AI features in our products.
MLOps & Model Monitoring:
Implement MLOps best practices—model versioning, automated CI/CD pipelines (Azure), containerization (Docker), orchestration (Kubernetes)—to ensure reliable, repeatable deployments.
Employ infrastructure-as-code (Terraform, AWS CDK) to maintain scalable, cloud-based ML environments on AWS (SageMaker, EC2/Fargate).
Experiment Tracking & Performance:
Track experiments, artifacts, and metrics using MLFlow, Weights & Biases, or ML Studio.
Continuously monitor performance (Prometheus, CloudWatch), troubleshoot issues, and optimize models for latency, accuracy, and scalability.
Cross-Functional Collaboration:
Partner with data engineers, product managers, and senior ML engineers to align technical solutions with business goals.
Contribute to evolving data pipelines and guide improvements based on user feedback and performance metrics. Mentorship & Best Practices:
Participate in code reviews, pair programming, and technical discussions.
Serve as a mentor to junior team members, sharing best practices in ML engineering, MLOps, and model lifecycle management.
Our Ideal Candidates:
3+ years in ML engineering, with hands-on NLP/LLM expertise, ideally deploying transformer-based models (e.g., GPT, BERT) in production.
Strong Python skills and experience with deep learning frameworks (PyTorch/TensorFlow/HuggingFace), plus familiarity with cloud-based ML (AWS SageMaker, EC2), containerization (Docker), and orchestration (Kubernetes).
Working knowledge of CI/CD (Azure), infrastructure-as-code (Terraform/CDK), and experiment tracking (MLFlow, W&B, ML Studio).
A proactive, collaborative approach; eagerness to learn from senior engineers and improve both ML and MLOps skill sets.
Experience with AWS event-driven and streaming architectures (e.g., EventBridge, SQS) to manage large-scale, real-time data handling and ingestion pipelines.
Understanding of security, compliance, and reliability best practices in ML deployments.
Prior work with voice recognition, sentiment analysis, or conversational AI frameworks.
What's in It for You?
Competitive salary package (immediate PTO).
Full benefits package.
Fidelity 401k with company match.
Fun perks including a monthly gym reimbursement, a monthly wellness reimbursement, and a monthly reading allowance.
Weekly catered breakfast, Employee of the Month rewards, regular company events, and bi-weekly happy hours.
Opportunities for continued career growth within the organization.
Fun and collaborative work environment.
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Find Senior ML Engineer JobsSenior ML Engineer Job Market in Dallas
Who's Hiring
- GEICO14
- DEPLOY14
- AppFolio14
- JustPark14
- Bain & Company14
Top Industries Hiring
- Technology & Software
Senior ML Engineer Jobs in Dallas: Frequently Asked Questions
How do I get a senior ml engineer job in Dallas?
Focus your search on the financial services firms clustered in Uptown and downtown Dallas, the health tech companies along the Platinum Corridor, and the enterprise software and defense contractors based in Las Colinas and Plano. Candidates who stand out here typically bring production-level MLOps experience and familiarity with large-scale data pipelines. Networking through Dallas-area tech meetups and targeting firms known for active ML investment gives you a concrete edge over remote applicants.
Which companies hire senior ml engineers in Dallas?
Dallas senior ml engineer roles are posted by GEICO, DEPLOY, and AppFolio and others right now, based on current listings on Migrate Mate as of September 2026. The Dallas market is anchored by a mix of large financial institutions, regional health systems, and mid-size technology firms that maintain dedicated ML teams locally rather than relying on remote hires.
Are there remote senior ml engineer jobs in Dallas?
Yes, though availability depends on the role: senior ml engineer positions that involve model deployment, data infrastructure, or research tend to be more remote-friendly, while roles tied to on-site data environments or regulated industries are more often hybrid or in-office. About 100% of senior ml engineer openings tied to Dallas are remote or hybrid as of September 2026, with the highest remote share found in enterprise software and consulting roles headquartered in the Platinum Corridor.
How can I get a senior ml engineer job in Dallas with little or no experience?
The most realistic entry path in Dallas is through a junior data scientist or ML engineer role at one of the mid-size fintech or health tech firms in the Uptown or Frisco corridors, where smaller teams give early-career candidates broader exposure to production systems. Dallas companies in those sectors also hire heavily from the University of Texas at Dallas and Southern Methodist University, so graduate programs and alumni networks are practical starting points. Building a portfolio of end-to-end ML projects and targeting companies with active internship pipelines accelerates the transition.
Which industries hire the most senior ml engineers in Dallas?
The sectors hiring the most senior ml engineers in Dallas are Technology & Software, based on current listings on Migrate Mate as of September 2026. Dallas's concentration of large financial institutions, regional hospital networks, and established technology companies creates sustained local demand that extends beyond the typical coastal tech hubs.
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