Member of Technical Staff, Machine Learning

Summary

bjakcareer is building a proactive AI chat app for everyday users, focusing on high reliability for workflows and task completion. As a Member of Technical Staff, Machine Learning, you will develop core ML components and work on real production systems, enhancing your understanding of large-scale ML in practical settings.

Responsibilities

• Build and improve ML components across data, training, evaluation, and inference
• Fine-tune and adapt models as part of larger production systems
• Implement evaluation and testing to understand model behavior
• Help build and maintain data pipelines for real-world and synthetic data
• Debug model issues, performance problems, and production incidents
• Ship improvements iteratively and learn from real user feedback
• Work closely with senior ML engineers and product teams
• Work under real production constraints: latency, cost, reliability, and safety

Skills

• Strong foundations in machine learning and modern neural architectures
• Some hands-on experience training, fine-tuning, or deploying ML models
• Comfortable writing production-quality code and learning new tools quickly
• Curious, coachable, and eager to learn from real systems in production
• Able to work through ambiguity with guidance and grow ownership over time
• Bias toward shipping, iteration, and continuous improvement
• Build and improve ML components across data, training, evaluation, and inference
• Fine-tune and adapt models as part of larger production systems
• Implement evaluation and testing to understand model behavior
• Help build and maintain data pipelines for real-world and synthetic data
• Debug model issues, performance problems, and production incidents
• Ship improvements iteratively and learn from real user feedback
• Work closely with senior ML engineers and product teams
• Work under real production constraints: latency, cost, reliability, and safety

Qualifications

Must Haves

• Strong foundations in machine learning and modern neural architectures
• Some hands-on experience training, fine-tuning, or deploying ML models
• Comfortable writing production-quality code and learning new tools quickly
• Curious, coachable, and eager to learn from real systems in production
• Able to work through ambiguity with guidance and grow ownership over time
• Bias toward shipping, iteration, and continuous improvement
• Build and improve ML components across data, training, evaluation, and inference
• Fine-tune and adapt models as part of larger production systems
• Implement evaluation and testing to understand model behavior
• Help build and maintain data pipelines for real-world and synthetic data
• Debug model issues, performance problems, and production incidents
• Ship improvements iteratively and learn from real user feedback
• Work closely with senior ML engineers and product teams
• Work under real production constraints: latency, cost, reliability, and safety