Staff Machine Learning Engineer
Bjak · Remote · Indonesia ·
COMPENSATION
€•• – ••k
Listed on the employer's job board (Ashby)
- Staff level role
- Fully remote
- Python + PyTorch
- GPU-based systems
- High autonomy
Join a team building proactive, AI-native applications for email, notes, and tasks that require minimal prompting. As a Staff Machine Learning Engineer, you will own the execution layer of the intelligence stack, bridging the gap between research capabilities and product reliability.
You will work across the entire model lifecycle, including data, training, evaluation, inference, and deployment. This hands-on leadership role requires strong systems fundamentals to optimize performance and ensure high-quality real-world task completion.
What you'll do
- Own end-to-end ML systems from data and training to deployment
- Build and evolve training and fine-tuning pipelines for large models
- Design evaluation systems for capability, robustness, and safety
- Architect high-performance inference systems optimizing latency and cost
- Create data pipelines for high-quality real-world and synthetic data
- Establish infrastructure for deploying, monitoring, and improving models
- Partner with research and engineering to integrate model capabilities
- Make pragmatic technical trade-offs based on real-world performance
What you'll need
- Experience shipping production ML systems, not just research prototypes
- Strong understanding of large-model training, fine-tuning, and inference
- Solid software engineering and systems fundamentals
- Experience operating ML workloads at scale on GPU-based systems
- Ability to navigate ambiguous problems with strong technical judgment
- Bias toward experimentation, measurement, and shipping code
- High standards for correctness, reliability, and production quality
Nice to have
- Comfortable working with large models and understanding failure modes
- Experience writing strong, production-grade code with focus on correctness
Stack
- Python
- PyTorch
- JAX
- GPU-based training and inference systems
How you'll work
- Small, high-talent-density team with broad ownership
- Engineers expected to exercise strong judgment and execute independently
- Fast decision-making balancing speed with engineering fundamentals
- Interviews conducted via virtual meetings or onsite
Hiring process
- Applications evaluated by technical team members
- Three to four interviews maximum if there is a fit
- Prompt decision process valuing transparency and efficiency