AI/ML Operations & Developer Engineer

ARM-posted about 2 months ago
Full-time • Mid Level
Hybrid • Austin, TX
5,001-10,000 employees
Professional, Scientific, and Technical Services
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We are seeking a passionate and versatile AI/ML Ops + Developer Engineer who thrives at the intersection of software engineering, semiconductor engineering, machine learning, and scalable deployment. The ideal candidate will have experience building, training, and deploying ML models in real-world environments, while also being fluent in modern data engineering workflows and DevOps practices. This role is best suited for individuals who are excited by AI-driven applications, optimization problems, and ML lifecycle automation.

  • ML Engineering: Hands-on experience with building, training, evaluating, and deploying ML/DL models using modern frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, XGBoost).
  • MLOps Foundations: Practical understanding of ML lifecycle orchestration using tools such as MLflow, SageMaker, or custom pipelines.
  • Data Infrastructure: Experience with structured and unstructured data ingestion, feature engineering, and pipeline construction using Python, SQL, and cloud-native tools (e.g., AWS Lambda, S3, DynamoDB).
  • Software Engineering: Proficiency in Python; familiarity with R, C++, or Java/Scala is a plus.
  • Model Deployment: Exposure to containerization (Docker), REST API development, and deploying models to production environments.
  • Version Control: Fluency with GIT for collaborative development and code management.
  • LLMs & GenAI: Exposure to large language models (e.g., OpenAI APIs, HuggingFace), prompt engineering, fine-tuning, or RAG pipelines using LangChain or similar frameworks.
  • Statistical & Time-Series Modeling: Knowledge of time-series forecasting techniques (ARIMA, LSTM), regression modeling, and statistical inference.
  • Visualization & Dashboards: Ability to present model outputs and insights via interactive dashboards (e.g., Plotly Dash, Tableau).
  • Reinforcement Learning: Familiarity with RL and decision-making systems, especially in constrained environments (e.g., IoT, robotics).
  • DevOps Practices: CI/CD pipelines, infrastructure-as-code, cloud-based automation, and monitoring solutions.
  • Visualization of Scientific Workflows: Experience working with high-dimensional biomedical, industrial, or simulation datasets.
  • Multimodal Data Fusion: Ability to integrate diverse data sources (e.g., imagery, tabular, textual) into a unified ML workflow.
  • Arm is committed to global talent acquisition, offering an attractive relocation package.
  • By enabling a dynamic, inclusive, meritocratic, and open workplace, where all our people can grow and succeed, we encourage all to share their unrivaled contributions to Arm's success in the global marketplace.
  • We value people as individuals and our dedication is to reward people competitively and equitably for the work they do and the skills and experience they bring to Arm.
  • Salary is only one component of Arm's offering. The total reward package will be shared with candidates during the recruitment and selection process.
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