Machine Learning Engineer, Sequence Models (project Sequoia)

As the Machine Learning Engineer on the team, you will deliver on our engineering vision to streamline the model development lifecycle from research to production, building ML infrastructure and establishing MLOps practices that enables rapid experimentation and deployment of ML models. You will invent and design new solutions to solve complex challenges that come with petabyte scale storage.

Key job responsibilities
- Build and scale ML infrastructure across data processing, distributed training, and model serving. Optimize GPU utilization, training throughput, serving latency, and Infra costs.
- Own the data pipelines that feed model training, including ingestion of structured and unstructured inputs, schema evolution, backfills, and data quality checks across upstream sources.
- Partner with Applied Scientists to shorten the time from experiment to production.
- Evolve model serving and feature delivery to support continuous experimentation.
- Establish automated, repeatable processes for large-scale data analysis, model training, validation, and deployment.
- Own operational excellence for high-volume, low-latency production systems, including monitoring, alarming, troubleshooting, and on-call.

Basic Qualifications
- 3+ years of non-internship professional software development experience
- 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware

Preferred Qualifications
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Knowledge of machine learning model architecture and inference
- Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
- Experience in debugging, profiling, and implementing software engineering best practices in large-scale systems