Principal Machine Learning Engineer

About the Company

A1 is on a mission to build a proactive smart assistant for everyday users, bringing intelligence to conversations, errands, organizing, and workflows. The product focuses on high reliability for long-running workflows, persistent context, and real-world task completion.

Responsibilities

• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment
• Fine-tune and adapt models using 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
• Own production deployment including GPU optimization, memory efficiency, and latency reduction
• Collaborate with application engineering to integrate ML systems into backend, mobile, and desktop products

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 modern ML frameworks like PyTorch or JAX
• Experience with distributed training and inference frameworks (e.g., DeepSpeed, FSDP, Megatron, Ray)
• Strong software engineering fundamentals for building production-grade systems
• Experience with GPU optimization, quantization, and mixed precision
• Ability to own ambiguous, zero-to-one ML systems end-to-end

Preferred Qualifications

• 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)