AI/ML Operations & Developer Engineer

ARM-posted 5 months ago
$126,100 - $170,500/Yr
Hybrid • Austin, TX
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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.

  • Hands-on experience with building, training, evaluating, and deploying ML/DL models using modern frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, XGBoost).
  • Practical understanding of ML lifecycle orchestration using tools such as MLflow, SageMaker, or custom pipelines.
  • 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).
  • Proficiency in Python; familiarity with R, C++, or Java/Scala is a plus.
  • Exposure to containerization (Docker), REST API development, and deploying models to production environments.
  • Fluency with GIT for collaborative development and code management.
  • Exposure to large language models (e.g., OpenAI APIs, HuggingFace), prompt engineering, fine-tuning, or RAG pipelines using LangChain or similar frameworks.
  • Knowledge of time-series forecasting techniques (ARIMA, LSTM), regression modeling, and statistical inference.
  • Ability to present model outputs and insights via interactive dashboards (e.g., Plotly Dash, Tableau).
  • Familiarity with RL and decision-making systems, especially in constrained environments (e.g., IoT, robotics).
  • CI/CD pipelines, infrastructure-as-code, cloud-based automation, and monitoring solutions.
  • Experience working with high-dimensional biomedical, industrial, or simulation datasets.
  • Ability to integrate diverse data sources (e.g., imagery, tabular, textual) into a unified ML workflow.
  • Attractive relocation package.
  • Dynamic, inclusive, meritocratic, and open workplace.
  • Support for personal wellbeing and high performance through hybrid working.
  • Commitment to equal opportunities and mutual respect.
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