Senior Machine Learning Engineer

Scope of Responsibilities

• Design and build LLMOps infrastructure supporting the development, evaluation, deployment, and continuous improvement of production language models.
• Build scalable training and fine-tuning infrastructure for commercial and open-weight language models.
• Develop pipelines supporting supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, and other post-training techniques.
• Build infrastructure for distributed training and GPU-accelerated ML workloads.
• Develop data pipelines for training, fine-tuning, evaluation, and synthetic data generation.
• Build systems for dataset versioning, lineage, quality validation, transformation, and reproducible experimentation.
• Develop experiment management infrastructure that enables engineers to compare models, datasets, hyperparameters, prompts, and training techniques.
• Build automated evaluation pipelines that determine whether new models or model versions are ready for production deployment.
• Design model registries, artifact management, versioning, and promotion workflows across development and production environments.
• Build and operate scalable model-serving and inference infrastructure for open-weight and fine-tuned models.
• Develop abstractions that allow product and AI engineering teams to use multiple models and inference providers without tightly coupling applications to a single model or vendor.
• Build observability for model training and inference, including metrics, tracing, logging, resource utilization, model quality, latency, throughput, and cost.
• Optimize training and inference workloads for GPU utilization, throughput, latency, reliability, and infrastructure cost.
• Build automated workflows for model deployment, rollback, canarying, and production validation.
• Investigate model and infrastructure failures across data pipelines, training jobs, inference services, distributed systems, and production environments.
• Evaluate emerging models, training techniques, inference frameworks, and ML infrastructure and determine where they can improve our production systems.
• Partner closely with AI engineers building agentic systems to provide the model, evaluation, and training infrastructure required to continuously improve those systems.