Machine Learning Engineer - Applied AI (Fully Remote)

Key Responsibilities:

• End-to-End System Execution: Own the full ML lifecycle: data preparation, training workflows, evaluation, inference architecture, and deployment for long-horizon AI capabilities.
• Model Fine-Tuning & Adaptation: Adapt and optimize modern LLMs and foundation models using techniques like LoRA, QLoRA, SFT, DPO, and model distillation.
• Inference Optimization: Architect and operate low-latency, cost-effective inference pipelines, managing GPU memory efficiency and scaling policies.
• Evaluation & Robustness: Design and implement rigorous evaluation and benchmarking frameworks covering model quality, safety, bias, and edge-case failure modes using real production signals.
• Data Systems Engineering: Build and maintain pipelines for high-quality synthetic and real-world training/evaluation datasets.
• Technical Leadership & Integration: Collaborate closely with product, backend, and frontend teams to integrate ML capabilities cleanly into products while mentoring junior engineers through code reviews and technical guidance.

Key Requirements:

• Production ML Experience: Proven track record of building, fine-tuning, and shipping production ML systems used by real users.
• Deep Model Knowledge: Strong understanding of how modern transformer models and LLMs behave—and misbehave—in production environments.
• Core Tech Stack: Expert proficiency in Python and deep learning frameworks (PyTorch or JAX).
• Adaptation & Inference Mastery: Hands-on experience with model adaptation (LoRA/SFT/DPO) and GPU inference systems/optimization.