Data Scientist- Machine Learning

Machine Learning Engineer 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. You'll be working at the intersection of ML research, systems engineering, and AI agent behavior — transforming ideas into robust, scalable learning pipelines.

• Have a strong background in machine learning, deep learning, or reinforcement learning.
• Are proficient in Python and familiar with frameworks such as PyTorch, Tensor Flow, 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.
• Are curious about AI agents and how models can simulate human‑like reasoning, problem‑solving, and collaboration.

To develop, optimize, and deploy machine learning systems that enhance agent performance, learning efficiency, and adaptability. You'll design model architectures, training workflows, and evaluation pipelines that push the frontier of autonomous intelligence and real‑time reasoning.

• 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.
• Maintain model reproducibility and version control, integrating with experiment tracking systems.
• Contribute to cross‑functional research efforts to improve learning strategies, fine‑tuning methods, and generalization performance.

• Build the core learning systems that power next‑generation AI agents.
• Combine ML research, engineering, and systems‑level optimization in one role.
• Work on uncharted challenges, designing models that can reason, plan, and adapt autonomously.
• Collaborate with a world‑class AI team redefining how autonomous systems learn and evolve.

• You'll be classified as an hourly contractor.
• Paid weekly via Stripe Connect, based on hours logged.
• Part‑time (20 hrs‑40 hrs/week) with fully remote, async flexibility — work from anywhere, on your own schedule.
• Weekly Bonus of $500‑$1,000 per 5 tasks created.