ML Systems Engineer – AI & Data Infrastructure
Are you passionate about building scalable machine learning infrastructure and enabling cutting-edge AI solutions? We are seeking an experienced ML Systems Engineer to join a forward-thinking technology team based in Dallas, TX. This is a unique opportunity to design, optimize, and deploy high-performance ML systems that power innovative applications across industries. If you have deep expertise in ML pipelines, distributed systems, and MLOps, this role could be your next big challenge.
Shape the Future of AI Systems from Dallas
A leading tech-driven organization in Dallas, TX is searching for a talented ML Systems Engineer to architect and maintain mission-critical ML infrastructure. The ideal candidate will have a strong background in cloud-based environments, container orchestration (Docker/Kubernetes), and experience with tools like TensorFlow, PyTorch, and MLFlow. Join a collaborative team focused on pushing the boundaries of AI, ensuring robust deployment of intelligent systems at scale.
Key Responsibilities of the ML Systems Engineer – Artificial Intelligence & Machine Learning
ML Infrastructure Development: Design, build, and maintain scalable, efficient, and secure machine learning infrastructure and data pipelines. Enable seamless model development, training, and deployment across cloud and on-prem environments.
Model Deployment & MLOps: Develop and manage robust MLOps workflows to support continuous integration and deployment (CI/CD) of ML models. Implement automation tools and frameworks to monitor model performance and ensure stability in production.
System Optimization & Scalability: Optimize the performance of ML systems by identifying bottlenecks and implementing solutions for computational efficiency and scalability. Leverage distributed computing, GPU acceleration, and containerization for high-volume workloads.
Cross-Functional Collaboration: Collaborate closely with data scientists, software engineers, and product teams to understand ML requirements and translate them into reliable systems. Facilitate smooth communication and alignment between teams to deliver end-to-end AI solutions.
Data Pipeline Engineering: Design and maintain data ingestion and preprocessing pipelines to support large-scale model training and analytics. Ensure data integrity, security, and accessibility through well-engineered ETL processes.
Cloud & DevOps Integration: Leverage cloud platforms such as AWS, Azure, or GCP to deploy scalable infrastructure. Utilize tools like Docker, Kubernetes, Terraform, and Jenkins to automate infrastructure provisioning and management.
Monitoring & Maintenance: Establish monitoring systems to track model drift, performance metrics, and system health. Implement alerting and logging mechanisms to ensure quick troubleshooting and uptime reliability.
Security & Compliance: Ensure ML systems adhere to data privacy, compliance, and security standards. Collaborate with security and governance teams to enforce policies and safeguard sensitive information.
Innovation & Research Support: Stay up to date with the latest in machine learning infrastructure and technologies. Evaluate and implement new tools and frameworks to support the evolving needs of research and production environments.
Documentation & Best Practices: Create detailed technical documentation and establish best practices for reproducibility, versioning, and maintainability. Support knowledge sharing within the engineering and data science community.
What the Client is Looking for in You
As an ML Systems Engineer, the client is seeking a highly skilled and forward-thinking technologist who excels in building and maintaining scalable machine learning infrastructure. You should be passionate about developing robust systems that empower data science teams and support the full machine learning lifecycle—from model development to production deployment. A deep technical foundation combined with a collaborative mindset will set you apart.
Deep Technical Expertise in ML Infrastructure
The ideal candidate will have hands-on experience architecting and scaling machine learning systems in production environments. You should be proficient in designing data pipelines, managing model versioning, and implementing MLOps practices using tools such as Docker, Kubernetes, MLFlow, and TensorFlow or PyTorch. Experience with distributed systems and cloud services (AWS, GCP, or Azure) is essential.
Problem Solver with a System-Level Mindset
The client values engineers who take a systems-thinking approach—solving problems not just at the code level but by improving architecture, automation, and scalability. You should be able to troubleshoot performance issues, manage dependencies, and optimize resource usage across diverse platforms.
Strong Programming and DevOps Skills
You are expected to be proficient in languages like Python and/or Go, with experience in scripting, automation, and configuration management. Familiarity with CI/CD pipelines, infrastructure as code (e.g., Terraform), and version control systems like Git is a strong advantage.
Collaborative and Cross-Functional Team Player
This role requires close collaboration with data scientists, backend engineers, and product stakeholders. The client is looking for a team player who communicates effectively, translates complex technical needs into practical solutions, and supports colleagues in deploying scalable ML applications.
Driven by Innovation and Continuous Learning
The client is looking for someone who is curious, innovative, and always eager to explore emerging technologies in the ML/AI and systems engineering space. Staying current with advancements in MLOps, model serving, and data engineering best practices is highly valued.
Detail-Oriented with a Focus on Reliability
As the systems you build will support mission-critical applications, the client seeks someone who writes clean, maintainable code and is dedicated to high standards of system performance, reliability, and uptime. You must take ownership of your work and follow through on delivery.
Security and Compliance Awareness
You should be familiar with best practices in data privacy, governance, and regulatory compliance. The ability to secure ML infrastructure and ensure ethical use of data is an important aspect of this role.
FAQs About the Role – ML Systems Engineer – Dallas, TX
1. What are the key responsibilities of the ML Systems Engineer in this role?
As an ML Systems Engineer, you will be responsible for designing, developing, and maintaining scalable machine learning infrastructure and deployment pipelines. You’ll work closely with data scientists and software engineers to support model training, automate deployments, manage ML workflows, and ensure reliable production performance of AI systems. You’ll also contribute to system optimization, data engineering, monitoring, and cloud integration.
2. What qualifications and experience are required for this position?
The ideal candidate should have a strong background in software engineering, machine learning systems, and cloud infrastructure. Proficiency in Python and experience with tools like TensorFlow, PyTorch, Docker, Kubernetes, and cloud platforms such as AWS, GCP, or Azure is essential. A bachelor’s or master’s degree in Computer Science, Engineering, or a related field is typically required, along with 3+ years of hands-on experience in ML systems or MLOps roles.
3. What technical skills are most important for success in this role?
Key technical skills include:
Building and optimizing ML pipelines
Containerization and orchestration using Docker/Kubernetes
MLOps tools like MLFlow, Kubeflow, or SageMaker
CI/CD practices and DevOps tooling
Strong coding skills in Python, Bash, or Go
Experience with data processing tools such as Apache Spark or Airflow
A strong understanding of system architecture and infrastructure management will also contribute to success.
4. What are the biggest challenges in this role?
You can expect challenges such as designing systems that scale with high volumes of data, ensuring model reproducibility and consistency in production, maintaining security and compliance, and troubleshooting issues across complex pipelines. Balancing reliability with rapid iteration and supporting cross-functional teams under tight deadlines are common aspects of the role.
5. What kind of impact will I have in this role?
As an ML Systems Engineer, your work will directly influence how effectively machine learning models are deployed and maintained in real-world applications. Your infrastructure will enable scalable AI operations, reduce time-to-market for data science teams, and ensure robust and secure systems that drive key business outcomes. You’ll be instrumental in laying the technical foundation for the company’s AI initiatives.
6. What is the team and work environment like?
The company offers a collaborative and fast-paced environment where engineers, data scientists, and product teams work closely to deliver high-impact solutions. The team values open communication, innovation, and continuous learning. You’ll be supported in your technical growth and encouraged to contribute ideas that improve systems, workflows, and overall team productivity.
What Remuneration Can You Expect from This Job?
As an ML Systems Engineer based in Dallas, TX, you can expect a competitive compensation package that reflects your technical expertise, experience level, and the strategic importance of your role in enabling scalable AI solutions. The remuneration typically includes:
1. Base Salary
The base salary for an ML Systems Engineer in Dallas, TX typically ranges from $110,000 to $160,000 annually, depending on your experience, skill set, and the size and maturity of the organization. Senior-level candidates with advanced MLOps expertise or experience in high-scale environments may command higher base salaries.
2. Performance-Based Bonuses
Many employers offer annual performance-based bonuses, which are tied to individual contributions, successful project delivery, system performance, and team outcomes. Bonus potential generally ranges from 10% to 20% of base salary, with additional incentives for exceeding targets or delivering mission-critical infrastructure.
3. Equity & Stock Options
Tech-forward companies, especially startups or mid-sized enterprises, may include equity or stock option grants as part of their total compensation packages. These long-term incentives align engineers with company growth and provide financial upside as the business scales.
4. Professional Development & Certifications
Employers often support ongoing learning and certification programs in MLOps, cloud platforms (AWS, GCP, Azure), or security standards. This can include direct reimbursement, stipends, or access to training platforms, ensuring you remain on the cutting edge of ML systems engineering.
5. Benefits & Perks
In addition to salary and bonuses, the total rewards package often includes:
Health, dental, and vision insurance
401(k) plans with company match
Paid time off (PTO) and paid holidays
Flexible working hours and hybrid work options
Annual tech budgets or equipment stipends
Employee wellness programs and mental health support
6. Relocation Assistance & Signing Bonuses
For candidates relocating to Dallas or bringing highly specialized experience, some companies offer signing bonuses and relocation packages ranging from $5,000 to $20,000 to facilitate a smooth transition.
Total Compensation Potential
Depending on experience level and company size, total annual compensation for an ML Systems Engineer in Dallas, TX—including base, bonuses, equity, and benefits—can range from $130,000 to $200,000+. Senior engineers and those in high-impact roles may earn significantly more in fast-scaling or venture-backed organizations.
How to Apply
If you are a highly skilled and innovative engineer with a strong background in building scalable machine learning systems, we encourage you to apply for the ML Systems Engineer role based in Dallas, TX. This is a remarkable opportunity to join a forward-thinking team, design impactful infrastructure, and contribute directly to real-world AI deployments.
To apply, please submit your updated resume and a cover letter outlining your experience in ML systems engineering, MLOps, cloud infrastructure, and machine learning lifecycle management. Be sure to highlight your proficiency in tools such as Docker, Kubernetes, MLFlow, TensorFlow/PyTorch, and your contributions to deploying and maintaining ML models at scale.
This role offers a high-growth, high-impact environment where your technical skills will directly support the deployment of intelligent solutions across mission-critical systems. Apply today to take the next step in your career as an ML Systems Engineer in Dallas, TX!
For more information or to explore similar roles in machine learning infrastructure and AI systems, visit our AI & ML Engineer Recruiters Page.
Tags:
ML Systems Engineer | Machine Learning Infrastructure | MLOps Engineer | AI Engineering | Cloud ML Deployment | Python | Kubernetes | ML Pipelines | Scalable AI Systems | Engineering Jobs in Dallas TX
