Principal Machine Learning Engineer, Artificial Intelligence – AI

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

📋 Description

• Design and construct extensive machine learning (ML) systems that encompass data, training, evaluation, inference, and deployment.

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

• Develop inference systems that effectively balance latency, throughput, cost, and reliability on a large scale.

• Design and manage data systems that provide high-quality synthetic and real-world training data.

• Implement evaluation pipelines that address 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 strategies.

• Work closely with application engineering to seamlessly integrate ML systems into backend, mobile, and desktop products.

• Make pragmatic trade-offs and deliver enhancements rapidly while learning from actual usage.

• Operate within real production constraints, including latency, cost, reliability, and safety.

⛳️ Requirements

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

• Experience in Artificial Intelligence (AI) is essential.

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

• Proficiency in at least one modern ML framework (such as PyTorch or JAX) with the ability to quickly learn others.

• Experience with distributed training and inference frameworks (including DeepSpeed, FSDP, Megatron, ZeRO, or Ray).

• Strong fundamentals in software engineering; capable of writing robust, maintainable, production-grade systems.

• Expertise in GPU optimization, which includes memory efficiency, quantization, and mixed precision.

• Comfortable taking ownership of ambiguous, end-to-end ML systems from inception to execution.

• A tendency to ship quickly, learn rapidly, and enhance systems through iteration.

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

• Contributions to open-source ML or systems libraries are a plus.

• A background in scientific computing, compilers, or GPU kernels is beneficial.

• Experience with RLHF pipelines (PPO, DPO, ORPO).

• Background 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

• Vision

• Savings Plan Options

• PTO