S
AI Summary
We are seeking a Machine Learning Engineer to design, train, and deploy large-scale learning systems for autonomous AI agents. The ideal candidate will have a strong background in machine learning, deep learning, or reinforcement learning and be proficient in Python and frameworks such as PyTorch, TensorFlow, or JAX.
Key Highlights
Design and implement scalable ML pipelines
Build and fine-tune deep learning models
Collaborate with data scientists to collect and preprocess training data
Technical Skills Required
Benefits & Perks
Weekly Bonus of $500-$1000
Fully remote role
Competitive compensation
Job Description
We are seeking Machine Learning Engineers to help design, train, and deploy large-scale learning systems powering autonomous AI agents for its AI lab partner. This role is ideal for engineers passionate about building models that think, adapt, and perform complex tasks in real-world environments.
Compensation: $ 14 per hour
Bonus: Weekly Bonus of $500 - $1000 per 5 task created.
(*Note: This is a referral opportunity for one of Scoutit's partners)
Responsibilities
- Design and implement scalable ML pipelines for model training, evaluation, and continuous improvement.
- Build and fine-tune deep learning models for reasoning, code generation, and real-world decision-making.
- Collaborate with data scientists to collect and preprocess training data, ensuring quality and representativeness.
- Develop benchmarking tools that test models across reasoning, accuracy, and speed dimensions.
- Implement reinforcement learning loops and self-improvement mechanisms for agent training.
- Work with systems engineers to optimize inference speed, memory efficiency, and hardware utilization.
- Have a strong background in machine learning, deep learning, or reinforcement learning.
- Are proficient in Python and familiar with frameworks such as PyTorch, TensorFlow, or JAX.
- Understand training infrastructure, including distributed training, GPUs/TPUs, and data pipeline optimization.
- Can implement end-to-end ML systems, from preprocessing and feature extraction to training, evaluation, and deployment.
- Are comfortable with MLOps tools (e.g., Weights & Biases, MLflow, Docker, Kubernetes, or Airflow).
- Have experience designing custom architectures or adapting LLMs, diffusion models, or transformer-based systems.
- Think critically about model performance, generalization, and bias, and can measure results through data-driven experimentation.
- You will be engaged as an independent contractor.
- This is a fully remote role that can be completed on your own schedule.
- Projects can be extended, shortened, or concluded early depending on needs and performance.
- Your work will not involve access to confidential or proprietary information from any employer, client, or institution.
- You will work on projects that focus on training and enhancing AI systems. You will be paid competitively, collaborate with leading researchers, and help shape the next generation of AI systems in your area of expertise.
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