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About the Role
As a Principal Machine Learning Engineer, you are a deep technical authority responsible for designing and evolving the most critical ML systems in the company. You will operate across training, inference, evaluation, and infrastructure, solving the hardest architectural and performance problems. This is a hands-on, high-impact role focused on depth, shaping how ML systems are built across the organization.
Responsibilities
- Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment.
- Design reproducible, high-performance training pipelines across GPU infrastructure.
- Architect inference systems that balance latency, throughput, cost, and reliability at scale.
- Design and maintain data systems for high-quality synthetic and real-world training data.
- Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.
- Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
- Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.
- Make pragmatic trade-offs and ship improvements quickly, learning from real usage.
- Work under real production constraints: latency, cost, reliability, and safety.
Requirements
- Strong background in deep learning and transformer-based architectures.
- Artificial Intelligence (AI) experience required.
- Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
- 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.
- 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).
Skills
- Deep learning
- Transformer-based architectures
- Artificial Intelligence (AI)
- ML frameworks (e.g. PyTorch, JAX)
- Distributed training
- Inference frameworks
- Software engineering
- GPU optimization
- LLM inference frameworks (vLLM, TensorRT-LLM, FasterTransformer)
- Open-source ML or systems libraries
- Scientific computing
- Compilers
- GPU kernels
- RLHF pipelines (PPO, DPO, ORPO)
- Multimodal models
- Diffusion models
- Large-scale data processing (Apache Arrow, Spark, Ray)
Location
- San Francisco, CA
- Remote
Work Type
- Work From Home
- Remote
Experience Level
- Principal
Salary/Compensations
- USD 170000 - USD 200000 - yearly
Benefits
- Medical insurance
- Dental
- Vision
- Savings Plan Options
- PTO
About the Company
- We help companies that are looking to hire Principal Machine Learning Engineers for jobs in San Francisco, California and in other cities too. Please contact our IT recruiting agencies and IT staffing companies today!