Principal Machine Learning Engineer, Artificial Intelligence – Remote

This is a fully remote position, open to applicants in California.

📋 Description

• Design and construct large-scale machine learning systems encompassing data management, training, evaluation, inference, and deployment.

• Create reproducible, high-performance training pipelines utilizing GPU infrastructure.

• Develop inference systems that effectively balance latency, throughput, cost, and reliability at scale.

• Design and oversee data systems that ensure high-quality synthetic and real-world training data.

• Implement evaluation pipelines that assess performance, robustness, safety, and bias in collaboration with research leadership.

• Take ownership of production deployment, focusing on GPU optimization, memory efficiency, latency reduction, and scaling policies.

• Work alongside application engineering to incorporate machine learning systems into backend, mobile, and desktop products.

• Make practical trade-offs and deliver enhancements swiftly, gaining insights from actual usage.

• Operate within production constraints related to latency, cost, reliability, and safety.

• Actively monitor, troubleshoot, and resolve production challenges with a clear root-cause analysis.

• Offer guidance, best practices, and scalable machine learning solutions to teams and cross-functional collaborators.

• Enhance the research-to-production cycle and improve product experience.

⛳️ Requirements

• Solid foundation in deep learning and transformer-based architectures.

• Experience in Artificial Intelligence (AI) is a must.

• Practical experience in training, fine-tuning, or deploying large-scale machine learning models in a production environment.

• Proficient in at least one contemporary machine learning framework, such as PyTorch or JAX.

• Ability to quickly adapt to other machine learning frameworks.

• Familiarity with distributed training and inference frameworks like DeepSpeed, FSDP, Megatron, ZeRO, or Ray.

• Strong software engineering principles.

• Capability to develop robust, maintainable, production-quality systems.

• Experience with GPU optimization, including memory efficiency, quantization, and mixed precision techniques.

• Comfort in managing ambiguous, zero-to-one machine learning systems from start to finish.

• Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.

• Contributions to open-source machine learning or systems libraries.

• Background in scientific computing, compilers, or GPU kernels.

• Experience with Reinforcement Learning from Human Feedback (RLHF) pipelines, including PPO, DPO, or ORPO.

• Experience in training or deploying multimodal or diffusion models.

• Experience in large-scale data processing using tools like Apache Arrow, Spark, or Ray.

🏝️ Benefits

• Medical insurance

• Dental insurance

• Vision insurance

• Savings Plan Options

• PTO

• Remote work / work from home