Note: The job is a remote job and is open to candidates in USA. BJAK is building a proactive AI smart assistant for everyday users, focusing on high reliability for workflows and task completion. As a Staff Machine Learning Engineer, you will own the execution layer of the AI's intelligence, translating research into scalable ML systems and ensuring their performance in real-world applications.
Responsibilities
- Own end-to-end 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 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
Skills
- Own end-to-end 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 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
- You have built or shipped real ML systems used by people, not just demos
- You are comfortable working with large models and understanding their failure modes
- You write strong, production-grade code and care about system correctness
- You are self-directed, pragmatic, and take full ownership of outcomes
- You communicate clearly and collaborate well in small, high-trust teams
Company Overview