Principal Machine Learning Engineer

BJAK is building a proactive smart assistant for everyday users that can manage conversations, errands, organization, and workflows with reliable long-running task execution. The Principal Machine Learning Engineer will serve as a deep technical authority, architecting and evolving large-scale ML systems across training, inference, evaluation, data, and deployment while setting technical standards across the organization.

Responsibilities:

• Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment
• Design reproducible, high-performance training pipelines across GPU infrastructure
• Architect inference systems that balance latency, throughput, cost, and reliability at scale
• 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
• • 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)