AIML - Senior Applied Machine Learning Engineer, ML Lifecycle (MLPT)

We're building the foundation for intelligent, adaptive AI systems from multi-agent platforms and RAG pipelines to advanced evaluation and reasoning frameworks. We're looking for a Senior Applied ML Engineer to design, build, and scale machine learning systems that power next-generation AI applications. In this role, you'll work at the intersection of machine learning, software engineering, helping develop foundational components that enable AI systems to perceive, reason, and act in dynamic, real-world contexts. You'll be part of a high-impact team shaping how we build, evaluate, and deploy intelligent systems at scale. This is a hands-on individual contributor role ideal for an engineer who thrives in ambiguity, moves seamlessly between prototyping and production, and is excited to push the frontier of applied AI through practical, elegant engineering.

Description

As a Senior Applied Machine Learning Engineer, you will:

Design and implement core systems that enable scalable development and deployment of AI applications including agent platforms, RAG frameworks, and adaptive ML services.

Build reusable infrastructure for model training, evaluation, and inference emphasizing observability, reproducibility, and modularity.

Collaborate cross-functionally with product, infra, and research teams to translate AI concepts into production-ready systems.

Develop intelligent tooling for data processing, simulation, and experimentation to accelerate applied AI innovation.

Contribute to architectural direction for our broader AI ecosystem designing for flexibility across future projects.

Prototype new capabilities using large language models, retrieval systems, and agentic workflows.

* Partner with infrastructure and product teams to operationalize new AI capabilities

You'll help bridge research and engineering bringing rigor, scalability, and real-world validation to the way we build intelligent systems.

Preferred Qualifications

Experience with LLM-based systems, RAG pipelines, or AI agent frameworks

Familiarity with MLOps tools (e.g., MLflow, Weights & Biases, Ray, Airflow)

Knowledge of evaluation methodologies for generative or agentic AI

Background in simulation systems, reinforcement learning, or continuous learning

Experience with data-centric AI data curation, labeling, and feedback loops

Proven ability to move between research-driven prototyping and production-scale engineering

Enthusiasm for emerging areas like multimodal AI, reasoning agents, and AI safety evaluation

Minimum Qualifications

7+ years of experience in ML engineering, software engineering or applied AI roles

Solid understanding of machine learning fundamentals, especially around large models, embeddings, and retrieval systems