Principal Machine Learning Engineer
RemoteUnited States
Job Summary
Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment across GPU infrastructure. Design reproducible, high-performance training pipelines and inference systems that balance latency, throughput, cost, and reliability at scale. Implement evaluation pipelines covering performance, robustness, safety, and bias, then own production deployment including GPU optimization, memory efficiency, and scaling policies. Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products while making pragmatic trade-offs to ship improvements quickly under real production constraints.
Required Qualifications
- 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
Desired 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)
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