Principal Machine Learning Engineer at Bjak

Position Principal Machine Learning Engineer
Posted 17 Jun 2026
Expired 17 Jul 2026
Company Bjak
Location Singapore | SG
Job Type Full Time

Job Description:

Latest job information from Bjak for the position of Principal Machine Learning Engineer. If the Principal Machine Learning Engineer vacancy in Singapore matches your qualifications, please submit your latest application or CV directly through the updated Jobkos job portal.

Please note that applying for a job may not always be easy, as new candidates must meet certain qualifications and requirements set by the company. We hope the career opportunity at Bjak for the position of Principal Machine Learning Engineer below matches your qualifications.

About the Role
A1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time. You will be responsible for turning research direction into working, production-grade ML systems. This role owns the execution layer of A1’s intelligence – training pipelines, inference systems, evaluation tooling, and deployment. Focus
Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, 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
Requirements
Strong background in deep learning and transformer-based architectures.
Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.
Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).
Strong software engineering fundamentals – you write robust, maintainable, production-grade systems.
Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.
Comfort owning ambiguous, zero-to-one ML systems end-to-end.
A bias toward shipping, learning fast, and improving systems through iteration.
Ideal Experience
Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.
Contributions to open-source ML or systems libraries.
Background in scientific computing, compilers, or GPU kernels.
Experience with RLHF pipelines (PPO, DPO, ORPO).
Experience training or deploying multimodal or diffusion models.
Experience with large-scale data processing (Apache Arrow, Spark, Ray).
How We Work
The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product #J-18808-Ljbffr

Job Info:

  • Company: Bjak
  • Position: Principal Machine Learning Engineer
  • Work Location: Singapore
  • Country: SG

How to Submit an Application:

After reading and understanding the criteria and minimum qualification requirements explained in the job information Principal Machine Learning Engineer at the office Singapore above, immediately complete the job application files such as a job application letter, CV, photocopy of diploma, transcript, and other supplements as explained above. Submit via the Next Page link below.

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