Staff Machine Learning Engineer

Role Description

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. This position is 100% Remote.

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.

Outcomes

• Research and models reliably translate into production-ready solutions with clear performance and quality targets.
• Machine Learning (ML) pipelines, training loops, and inference systems are stable, efficient, and maintainable.
• Production issues are detected, debugged, and resolved quickly, minimizing user impact.
• Team members are supported, aligned, and able to deliver high-impact Machine Learning (ML) work with minimal friction.
• Iterations on models and systems are measurable, safe, and improve user experience over time.

Qualifications

• 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.
• You are self-directed, pragmatic, and take full ownership of outcomes.
• Experience communicating clearly and collaborating well in small, high-trust teams.
• Tech Stack: GPU-based training and inference system, JAX, Python, and PyTorch.

Benefits

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

Compensation

USD 150,000 - USD 170,000 yearly