Join a cutting-edge AI research initiative at the forefront of Generative AI and contribute to the development of next-generation Large Language Models. We are seeking experienced MLOps Engineers with deep expertise in modern machine learning frameworks, large-scale training infrastructure, and kernel-level optimization.
In this role, you'll leverage your knowledge of JAX, PyTorch, and custom GPU kernel programming (Pallas/Triton) to create, evaluate, and refine high-quality technical tasks that help train frontier AI systems. You'll collaborate with AI researchers and engineering teams to improve model reasoning across MLOps, distributed training, and ML infrastructure topics.
This is a full-time, 40-hour-per-week remote engagement requiring full weekday availability.
- Partner with research and engineering teams to strengthen AI model capabilities in MLOps, ML infrastructure, and large-scale training systems.
- Design challenging, real-world MLOps and machine learning systems tasks that reflect production engineering scenarios.
- Develop accurate, well-documented solutions to complex ML infrastructure and training pipeline problems.
- Review and evaluate technical tasks and AI-generated solutions, providing clear and actionable written feedback.
- Create detailed evaluation rubrics and scoring frameworks for topics including:
- Distributed training architectures
- ML pipeline design
- Infrastructure optimization
- Kernel-level programming
- Performance tuning
- Collaborate with fellow subject matter experts to maintain consistency, quality, and technical accuracy across training datasets.
- Contribute domain expertise to improve the reasoning capabilities of advanced AI systems.
- Minimum 2 years of professional experience in MLOps, Machine Learning Infrastructure, or ML Systems Engineering within a recognized technology organization.
- Hands-on production experience with JAX and/or PyTorch in large-scale machine learning environments.
- Practical experience developing or optimizing custom GPU kernels using Pallas (JAX) or Triton .
- Strong understanding of distributed training systems, model optimization, and scalable ML infrastructure.
- Demonstrated career growth and increasing technical responsibility.
- Availability to work 40 hours per week during standard weekday business hours.
- Excellent written communication skills with the ability to clearly explain technical concepts and architectural decisions.
- Experience designing and optimizing large-scale ML training pipelines.
- Knowledge of distributed computing and GPU performance optimization.
- Familiarity with evaluation methodologies for AI models and ML systems.
- Experience collaborating with research teams on advanced machine learning projects.
- Passion for advancing AI infrastructure and frontier model development.
- Help build and improve next-generation Large Language Models.
- Work alongside leading AI researchers and experienced machine learning engineers.
- Apply your expertise to high-impact projects involving large-scale ML systems and infrastructure.
- Contribute directly to the development of cutting-edge AI technologies.
- Enjoy a fully remote engagement with meaningful technical challenges.
We welcome applications from qualified professionals regardless of legally protected characteristics and are committed to providing reasonable accommodations throughout the application and engagement process upon request.
- Engagement is offered on an independent contractor basis.
- This is a fully remote opportunity that can be completed according to your own schedule.
- Project duration may be extended, shortened, or concluded based on project requirements and individual performance.
- The engagement does not require access to confidential or proprietary information belonging to any current employer, client, or institution.
- Payments are processed weekly through Stripe or Wise based on approved work completed.
- Please note: Applicants requiring H-1B sponsorship or participating in the STEM OPT program are not eligible for this opportunity. Key Responsibilities
- Partner with research and engineering teams to strengthen AI model capabilities in MLOps, ML infrastructure, and large-scale training systems.
- Design challenging, real-world MLOps and machine learning systems tasks that reflect production engineering scenarios.
- Develop accurate, well-documented solutions to complex ML infrastructure and training pipeline problems.
- Review and evaluate technical tasks and AI-generated solutions, providing clear and actionable written feedback.
- Create detailed evaluation rubrics and scoring frameworks for topics including:
- Distributed training architectures
- ML pipeline design
- Infrastructure optimization
- Kernel-level programming
- Performance tuning
- Collaborate with fellow subject matter experts to maintain consistency, quality, and technical accuracy across training datasets.
- Contribute domain expertise to improve the reasoning capabilities of advanced AI systems.
- Minimum 2 years of professional experience in MLOps, Machine Learning Infrastructure, or ML Systems Engineering within a recognized technology organization.
- Hands-on production experience with JAX and/or PyTorch in large-scale machine learning environments.
- Practical experience developing or optimizing custom GPU kernels using Pallas (JAX) or Triton .
- Strong understanding of distributed training systems, model optimization, and scalable ML infrastructure.
- Demonstrated career growth and increasing technical responsibility.
- Availability to work 40 hours per week during standard weekday business hours.
- Excellent written communication skills with the ability to clearly explain technical concepts and architectural decisions.
- Experience designing and optimizing large-scale ML training pipelines.
- Knowledge of distributed computing and GPU performance optimization.
- Familiarity with evaluation methodologies for AI models and ML systems.
- Experience collaborating with research teams on advanced machine learning projects.
- Passion for advancing AI infrastructure and frontier model development.
- Help build and improve next-generation Large Language Models.
- Work alongside leading AI researchers and experienced machine learning engineers.
- Apply your expertise to high-impact projects involving large-scale ML systems and infrastructure.
- Contribute directly to the development of cutting-edge AI technologies.
- Enjoy a fully remote engagement with meaningful technical challenges.
We welcome applications from qualified professionals regardless of legally protected characteristics and are committed to providing reasonable accommodations throughout the application and engagement process upon request.
- Engagement is offered on an independent contractor basis.
- This is a fully remote opportunity that can be completed according to your own schedule.
- Project duration may be extended, shortened, or concluded based on project requirements and individual performance.
- The engagement does not require access to confidential or proprietary information belonging to any current employer, client, or institution.
- Payments are processed weekly through Stripe or Wise based on approved work completed.
- Please note: Applicants requiring H-1B sponsorship or participating in the STEM OPT program are not eligible for this opportunity. This role is for one of our clients
Join a cutting-edge AI research initiative at the forefront of Generative AI and contribute to the development of next-generation Large Language Models. We are seeking experienced MLOps Engineers with deep expertise in modern machine learning frameworks, large-scale training infrastructure, and kernel-level optimization.
In this role, you'll leverage your knowledge of JAX, PyTorch, and custom GPU kernel programming (Pallas/Triton) to create, evaluate, and refine high-quality technical tasks that help train frontier AI systems. You'll collaborate with AI researchers and engineering teams to improve model reasoning across MLOps, distributed training, and ML infrastructure topics.
This is a full-time, 40-hour-per-week remote engagement requiring full weekday availability.