Machine Learning Engineer

This is a fully remote position, open to applicants in California.

📋 Description

• Architect, implement, and expand open-source models utilizing Kubernetes, Ray, Slurm, or other distributed orchestration frameworks.

• Design and develop machine learning systems along with data pipelines.

• Create experiments, perform prompt-tuning, assess, and deploy production-ready models and AI agents.

• Establish adaptable methods for benchmarking performance and swapping models to meet changing requirements.

• Conduct thorough model benchmarks and carry out error and gap analyses on model outputs.

• Develop analytics dashboards to convey system performance insights to stakeholders.

• Take ownership of features from the ideation phase through to production.

• Oversee architectural decisions and coordinate updates across both accessible and restricted code repositories.

• Engage in community interactions.

• Create automated testing frameworks and secure GitLab CI/CD pipelines.

• Deploy and scale models across distributed infrastructures.

• Manage GPU orchestration and construct advanced AI workflows.

⛳️ Requirements

• Master’s degree or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience.

• Over 3 years of professional experience in writing production-grade, asynchronous Python.

• Extensive experience with LangChain, Hugging Face libraries, vLLM, and SGLang.

• Proficient in TensorFlow, PyTorch, and Scikit-learn.

• Skilled in data analysis using Python, pandas, NumPy, or similar tools.

• Practical experience with deploying production-grade models, monitoring performance, and scaling using Kubernetes, Ray, or Slurm.

• Strong comprehension of GPU memory management and infrastructure-level optimization for high-throughput, low-latency AI inference workflows.

• Advanced knowledge of GitLab pipelines, automated test jobs, and vulnerability scanners in merge request workflows.

• Expertise in Python testing frameworks such as PyTest, along with mocking libraries and automated test generation frameworks for AI workloads.

• High proficiency in advanced Git workflows, including rebase strategies, cryptographic commit signing, and public/private repository mirroring.

• Experience with alignment/fine-tuning of LLMs, VLMs, or any-to-text is advantageous.

• Prior experience in scientific research and publication is a plus.

🏝️ Benefits

• Competitive salaries.

• Comprehensive benefits package.

• Equity.