Applied ML/AI Systems Engineer

Title: Applied ML/AI Systems Engineer
Location: San Francisco, CA (Hybrid or Onsite Preferred)
Industry: Technology – AI Product Development
Employment Type: Full-Time

About the Company:
Join a stealth-mode, venture-backed innovator pushing the boundaries of artificial intelligence. This is your chance to work at the intersection of applied research and real-world deployment, contributing to next-gen AI products with immediate, tangible impact. Be part of a nimble, ambitious team building groundbreaking solutions in a collaborative, high-growth environment.

Position Summary:
We are hiring an Applied ML/AI Systems Engineer to architect and scale intelligent systems powering production-ready machine learning solutions. This is a foundational role on a highly technical team, where you’ll collaborate closely with product, infrastructure, and research leaders. Your work will shape real-world deployments across multiple ML domains.

Key Responsibilities:

• Build scalable ML pipelines, from data ingestion through deployment.
• Train, optimize, and evaluate models in domains such as LLMs, time-series forecasting, and computer vision.
• Integrate models with backend infrastructure to support efficient, low-latency inference.
• Monitor live ML systems, respond to performance issues, and drive operational excellence.
• Collaborate cross-functionally with engineering, product, and DevOps teams to improve ML tooling and infrastructure.
• Support model governance, performance benchmarking, and data quality assurance initiatives.

Qualifications:

• 3+ years of hands-on experience deploying ML models in production environments.
• Proficiency in Python and ML frameworks like PyTorch or TensorFlow.
• Experience in at least one area: large-scale generative models, applied ML for cybersecurity, or ML infrastructure.
• Deep understanding of modern techniques like transformers, embeddings, and self-supervised learning.
• Experience with MLOps tools (e.g., MLflow, Airflow, Ray, Docker, Kubernetes).
• Familiarity with cloud-based ML workflows (AWS, GCP, or Azure).
• Strong system-level thinking with a track record of solving real-world engineering problems.

Preferred:

• Experience with RAG architectures or scaling LLM applications.
• Contributions to open-source machine learning projects.
• Prior experience in a high-velocity startup or product-focused AI team.

Compensation:

• Base Salary: $160,000–$220,000
• Equity Options
• Comprehensive Benefits Package

Why You Should Apply:

• Work on cutting-edge, production-scale AI systems.
• Join a world-class engineering team with deep domain expertise.
• Enjoy high-impact work with ownership and autonomy in a startup setting.