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About the Role

As the Staff Machine Learning Engineer, you own the execution layer of intelligence. You translate research direction into reliable, scalable, production-grade Machine Learning (ML) systems. This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints.

Responsibilities

• Own end-to-end Machine Learning (ML) system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
• Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.
• Architect and operate scalable inference systems, balancing latency, cost, and reliability.
• Design and maintain data systems for high-quality synthetic and real-world training data.
• Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.
• Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• Collaborate closely with application engineering to integrate Machine Learning (ML) systems cleanly into backend, mobile, and desktop products.
• Make pragmatic trade-offs and ship improvements quickly, learning from real usage.
• Work under real production constraints: latency, cost, reliability, and safety.

Requirements

• Experience building or shipping real Machine Learning (ML) systems used by people, not just demos.
• Artificial Intelligence (AI) experience required.
• Experience working with large models and understanding their failure modes.
• Experience writing strong, production-grade code.
• Self-directed, pragmatic, and take full ownership of outcomes.
• Experience communicating clearly and collaborate well in small, high-trust teams.

Skills

• LoRA
• QLoRA
• SFT
• DPO
• distillation
• GPU-based training and inference system
• JAX
• Python
• PyTorch

Location

• San Francisco, CA
• Remote

Work Type

• Work From Home
• Remote

Experience Level

• Staff

Salary/Compensations

• USD 150000 - USD 170000

Benefits

• medical insurance
• Dental
• Vision
• Savings Plan Options
• PTO