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Junior Machine Learning Compiler Engineer Jobs in Radford, VA

AgileEngine

MLOps Engineer ID72409

Blacksburg, VA · On-site

... Machine Learning, or Software Engineering ; - Degree in Computer Science, Software Engineering, or a related technical discipline (or equivalent practical experience); - Engineers located in the US ...

Virginia Tech

Research Associate

Blacksburg, VA · On-site

Substantial programming experience. Experience with research projects in the fields of AI and/or Machine Learning Excellent communication skills. Preferred Qualifications Demonstrated record of ...

Tekni-Plex

CNC Machinist II

Dublin, VA

$22 - $30/hr

Intermediate CNC programmer responsible for independently programming and running both LB3000 Okuma ... Coaching/mentoring ability with junior staff. * Ownership of assigned tasks with minimal ...

Tekni-Plex

CNC Machinist II

Dublin, VA · On-site

$22 - $30/hr

Intermediate CNC programmer responsible for independently programming and running both LB3000 Okuma ... Coaching/mentoring ability with junior staff. * Ownership of assigned tasks with minimal ...

Varsity Tutors

Data Science Tutor

Blacksburg, VA · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA · On-site

College of Engineering Department: Computer Science Location: Blacksburg, Virginia Categories ... Machine Learning (SciML). They will be expected to collaborate with members of the group as well as ...

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All JobsMachine Learning Compiler Engineer JobsJunior Machine Learning Compiler Engineer JobsJunior Machine Learning Compiler Engineer Jobs in Radford, VA

Junior Machine Learning Compiler Engineer information

See Radford, VA salary details

$31.8K

$68.2K

$104K

How much do junior machine learning compiler engineer jobs pay per year?

As of Sep 5, 2026, the average yearly pay for junior machine learning compiler engineer in Radford, VA is $68,202.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,100.00 and $76,000.00 per year, depending on experience, location, and employer.

What does a junior machine learning compiler engineer do?

A Junior Machine Learning Compiler Engineer helps design, develop, and optimize compilers for machine learning models. Their work involves translating high-level machine learning code into efficient low-level code that can run on various hardware platforms, such as CPUs, GPUs, or specialized AI chips. They often collaborate with software engineers and data scientists to ensure that machine learning workloads run efficiently and correctly. This role typically involves programming, debugging, and performance tuning, often using languages like C++, Python, and specialized frameworks.

What are typical projects and responsibilities for a junior machine learning compiler engineer in a collaborative team setting?

As a Junior Machine Learning Compiler Engineer, you can expect to work on projects that focus on optimizing machine learning models for performance and deployment across various hardware platforms. Typical responsibilities include assisting in developing and debugging compiler passes, implementing optimizations, and contributing to code reviews. You'll frequently collaborate with senior engineers, data scientists, and hardware specialists to ensure that models are efficiently translated and executed. This role offers valuable learning opportunities through hands-on coding, exposure to state-of-the-art ML frameworks, and regular team meetings for knowledge sharing and mentorship.

What are the key skills and qualifications needed to thrive as a junior machine learning compiler engineer, and why are they important?

To thrive as a Junior Machine Learning Compiler Engineer, you need a solid background in computer science fundamentals, programming (especially C++ and Python), and foundational knowledge of machine learning and compiler theory. Familiarity with frameworks and tools such as LLVM, TensorFlow, MLIR, and version control systems is typically required, along with a relevant bachelor’s or master’s degree. Strong problem-solving abilities, attention to detail, and effective teamwork and communication skills set standout candidates apart. These skills and qualities are crucial for efficiently optimizing machine learning models for various hardware targets and collaborating on innovative compiler solutions.

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

AspectJunior Machine Learning Compiler EngineerData Scientist
Required CredentialsBachelor's in Computer Science, Software Engineering, or related field; knowledge of compiler design and ML frameworksBachelor's or higher in Data Science, Statistics, Computer Science, or related field; strong analytical skills
Work EnvironmentSoftware development teams, focusing on compiler optimization for ML modelsData analysis teams, focusing on data interpretation and model development
Employer & Industry UsageTech companies, AI startups, hardware firmsTech firms, finance, healthcare, research institutions

The Junior Machine Learning Compiler Engineer primarily focuses on developing and optimizing compilers for machine learning models, requiring programming and compiler knowledge. In contrast, a Data Scientist analyzes data, builds models, and provides insights. Both roles are essential in AI and tech industries but differ in technical focus and daily tasks.

What are the most commonly searched types of Machine Learning Compiler Engineer jobs in Radford, VA?

The most popular types of Machine Learning Compiler Engineer jobs in Radford, VA are:

What cities near Radford, VA are hiring for Junior Machine Learning Compiler Engineer jobs?

Cities near Radford, VA with the most Junior Machine Learning Compiler Engineer job openings:

Junior Machine Learning Compiler Engineer jobs near you
Infographic showing various Junior Machine Learning Compiler Engineer job openings in Radford, VA as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $68,202 per year, or $32.8 per hour.

MLOps Engineer ID72409

AgileEngine

Blacksburg, VA • On-site

Full-time

Posted 7 days ago

Job description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE
We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle transition from AI/ML experimentation to reliable production deployment, building and maintaining the infrastructure, pipelines, and automation needed to deploy models efficiently at scale. You will implement production monitoring systems, drift detection, experiment tracking, and model versioning, while managing cloud environments and GPU compute resources for cost-effective scalability. The role is based onsite in Dallas, TX, and requires close collaboration with data scientists and AI researchers to translate experimental models into production-ready solutions.

WHAT YOU WILL DO
- Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment;
- Build, maintain, and scale the infrastructure, automation, and CI/CD workflows necessary for rapid and efficient model deployment;
- Implement robust production monitoring systems, build visibility dashboards, and set up data and concept drift detection to ensure ongoing model accuracy and system reliability;
- Manage experiment tracking and model versioning to ensure full reproducibility and traceability of all models in production;
- Partner closely with data scientists and AI researchers to translate experimental models into robust, production-ready solutions;
- Manage cloud environments and GPU compute resources to ensure systems are not only highly scalable but also cost-effective.

MUST HAVES
- You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;
- 3+ years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering;
- Degree in Computer Science, Software Engineering, or a related technical discipline (or equivalent practical experience);
- Engineers located in the US must reside in Dallas, TX, and be willing to work onsite;
- Hands-on experience with experiment tracking, model registry/versioning, drift detection, and production monitoring;
- Strong practical experience navigating cloud environments and managing/provisioning GPU compute resources;
- Deep understanding of containerization (e.g., Docker, Kubernetes) and designing robust CI/CD pipelines for automated deployments;
- A solid conceptual understanding of AI/ML fundamentals to effectively communicate, troubleshoot, and collaborate with applied model developers;
- Upper-intermediate English level.

PERKS AND BENEFITS
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
- Well-being & support: access local well-being programs and people-focused support tailored to your location