Lead Engineer, Machine Learning
At a glance
Senior Machine Learning Engineering role at Bjak. Remote, Indonesia · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Run the ML lifecycle end to end, from preparing data and training models through evaluation, inference and deployment
- Build training and fine-tuning pipelines for large models, then assess capability, robustness, safety and product results
- Improve inference performance across latency, GPU use, memory, cost and reliability
- Create systems for real-world and synthetic training data, and keep deployed models monitored and improving
- Work with research and application engineers to turn model capabilities into product changes and iterate on measured results
What you bring
- You have shipped ML systems into production for real users, rather than stopping at research prototypes or demos
- You understand training, fine-tuning, evaluation and inference for modern large models
- You bring strong software engineering and systems skills, including experience running GPU-based ML workloads at scale
Who this fits
This is a hands-on senior role with broad technical ownership and independent decision-making. It suits an engineer who can bridge research, systems and product work, and who supports teammates delivering ML projects. The role is listed as remote in Jakarta, Indonesia.
From the employer
About the Role
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.
As Lead Engineer, Machine Learning, you own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems.
You will work across the model lifecycle: data, training, evaluation, inference, and deployment. This is a hands-on leadership role for someone who wants to operate at the intersection of research, systems, and product.
What You'll Own
Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment.
Build and evolve training and fine-tuning pipelines for large models.
Design evaluation systems that measure capability, robustness, safety, and real-world product performance.
Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.
Build data pipelines and systems for high-quality real-world and synthetic training data.
Establish reliable production infrastructure for deploying, monitoring, and continuously improving models.
Partner closely with research and application engineering to turn model capabilities into product improvements.
Make pragmatic technical trade-offs and rapidly iterate based on real-world performance.
What We're Looking For
Experience building and shipping ML systems used in production, not just research prototypes.
Strong understanding of modern large-model training, fine-tuning, evaluation, and inference.
Strong software engineering and systems fundamentals.
Experience operating ML workloads at meaningful scale, particularly GPU-based systems.
Strong technical judgment and the ability to navigate ambiguous problems independently.
A bias toward experimentation, measurement, and shipping.
High standards for correctness, reliability, and production quality.
Outcomes
Research and models reliably translate into production-ready solutions with clear performance and quality targets.
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 ML work with minimal friction.
Iterations on models and systems are measurable, safe, and improve user experience over time.
Tech Stack
Python
PyTorch / JAX
GPU-based training and inference system
Ideal Experience
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.
How We Work
We are a small, high-talent-density, hands-on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently.
We make decisions quickly, work closely together, and balance speed with engineering fundamentals. We care less about process and more about building something exceptional.
Interview process
If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.
We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.