Machine Learning Engineer

What you'll do

• Architect, deploy, and scale open-source models using distributed orchestration frameworks like Kubernetes, Ray, or Slurm.
• Design and build machine learning systems and data pipelines for production-grade AI agents.
• Perform prompt tuning, model evaluation, and benchmarking to ensure high performance across various use cases.
• Conduct deep error and gap analysis on model outputs and create analytics dashboards for stakeholders.
• Manage GPU memory management and infrastructure-level tuning for high-throughput, low-latency inference workflows.
• Build automated testing frameworks and secure CI/CD pipelines using GitLab to ensure code quality.
• Own the full lifecycle of features from initial ideation through production deployment and community interaction.

What we're looking for

• Master’s or PhD in Computer Science, Electrical Engineering, or a related field (or equivalent experience).
• 3+ years of professional experience writing production-grade, asynchronous Python with a focus on decoupled system architecture and design patterns.
• Deep experience building with LangChain, Hugging Face libraries, vLLM, and SGLang.
• Experience with machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn.
• Proficiency in data analysis using pandas or NumPy to communicate findings to technical and non-technical stakeholders.
• Hands-on experience with production-grade model deployment and scaling using Kubernetes, Ray, or Slurm.
• Strong understanding of GPU memory management and infrastructure tuning for high-throughput AI inference.
• Advanced knowledge of GitLab CI/CD pipelines, PyTest, and advanced Git workflows (preferred); experience with LLM alignment/fine-tuning (preferred).