Principal Machine Learning Engineer

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 operate across training, inference, evaluation, and infrastructure, solving the hardest architectural and performance problems. While Technical Leads may own execution at the team level, you set the technical standard and shape how ML systems are built across the organization.

This is a hands-on, high-impact role focused on depth.

Focus

• 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.

• 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.

Ideal Experience

• 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).

Outcomes

• ML systems (training, inference, evaluation) are reliable, scalable, and meet defined performance targets.

• Models deployed to production achieve measurable quality improvements and meet user-impact goals.

• Production issues are proactively monitored, debugged, and resolved with clear root-cause analysis.

• Team and cross-functional collaborators benefit from clear guidance, best practices, and scalable ML solutions.

• Research-to-production cycles are efficient, safe, and continuously improve the product experience.