Principal Machine Learning Engineer (AI)

Location: Remote, United States

Company

Building a proactive AI assistant for everyday conversations, errands, organization, and workflows.

What you will do

• Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment.
• Design reproducible, high-performance training pipelines and scalable inference systems across GPU infrastructure.
• Build data systems for synthetic and real-world training data and implement evaluations for performance, robustness, safety, and bias.
• Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• Integrate ML systems with backend, mobile, and desktop products while making pragmatic trade-offs under production constraints.
• Provide technical guidance and establish scalable ML engineering practices across the organization.

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 a modern ML framework such as PyTorch or JAX.
• Experience with distributed training and inference frameworks such as DeepSpeed, FSDP, Megatron, ZeRO, or Ray.
• Strong software engineering fundamentals and experience building robust, maintainable, production-grade systems.
• Experience with GPU optimization, including memory efficiency, quantization, and mixed precision, plus ownership of ambiguous ML systems end to end.

Nice to have

• 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, multimodal or diffusion models, and large-scale data processing.

Culture & Benefits

• Work remotely from the United States.
• Join a small, high-talent-density, hands-on team.
• Work in an environment focused on collective decision-making, rapid execution, independent judgment, and continuous learning.
• Interview virtually and/or onsite with a prompt decision process.

Hiring process

• Complete 3, and no more than 4, interviews if selected for further consideration.
• Applications are evaluated by technical team members.
• Interviews are conducted via virtual meetings and/or onsite.