> Markdown version of [/jobs/ext/3655501-ml-engineer](https://www.wearedevelopers.com/jobs/ext/3655501-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# ML Engineer

- **Company:** Anduril Industries
- **Location:** Santa Ana, CA, United States
- **Experience:** Expert
- **Salary:** $220,000.0 - $292,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Optical Character Recognition (OCR), Computer Vision, Cloud Computing, Nvidia CUDA, Information Systems, Continuous Integration, Information Engineering, Data Governance, Data Systems, Distributed Systems, Monitoring of Systems, Supervisory Control and Data Acquisition (SCADA), Python (Programming Language), Software Architecture, Inference Optimization, Tensorflow, Azure Machine Learning, Software Engineering, Speech Recognition, Digital Twin, Pinecone, Feature Store, Enterprise Software Applications, Real Time Systems, Pytorch, Retrieval-Augmented Generation, System Availability, Delivery Pipeline, Large Language Models, Deep Learning, Model Validation, AI Coding Agents, Event Driven Architecture, Kubernetes, Information Technology, Low Latency, Weaviate, Milvus, Triton Inference Server, Machine Learning Operations, TensorRT, Software Version Control, Docker
- **Published:** October 9, 2026
- **Apply:** https://www.thejobnetwork.com/job/16f0f20f-3010-422e-a03b-9033e42ed467/senior-machine-learning-engineer-applied-intelligence

## About the Role

* 8+ years of experience in a software engineering role building production systems, ideally in a fast-paced environment.
\n

 * Deep expertise in MLOps with end-to-end experience delivering production-grade AI/ML systems.
\n

 * Strong technical fluency in modern software architectures, APIs, distributed systems, CI/CD, and cloud or edge infrastructure.
\n

 * Deep experience with MLOps: data acquisition, labeling, curation, pipeline management, model versioning, continuous integration, and model monitoring.
\n

 * Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow).
\n

 * Experience building and deploying containerized ML services using Docker and Kubernetes.
\n

 * Proficiency in data engineering, time-series data modeling, and working with semantic/ontology-driven data systems.
\n

 * Experience implementing observability for model performance, inference accuracy, and data drift.
\n

 * Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration.
\n

 * Experience building systems that must operate reliably under real-world operational constraints (high availability, low latency, or constrained environments).
\n

 * Strong stakeholder management skills with proven experience aligning engineering, data, and manufacturing teams.
\n

 * Excellent written and verbal communication skills; able to bridge research, platform, and production domains and collaborate across engineering, manufacturing, and operations teams.
\n

 * Degree in Computer Science, Information Systems, Engineering, or related technical field, or equivalent practical experience.
\n

 * U.S. Person status is required as this position needs to access export controlled data.
\n, * Experience in manufacturing, industrial, or OT-adjacent domains (MES, SCADA, PLC integration, factory automation, IoT).
\n

 * Experience applying AI/ML within manufacturing, logistics, industrial control, or production environments.
\n

 * Background with digital twins, predictive maintenance, OCR/IDP, computer vision, or speech-to-text model integrations.
\n

 * Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal.
\n

 * Familiarity with GPU acceleration (CUDA) and inference optimization (TensorRT, Triton Inference Server).
\n

 * Experience building RAG (Retrieval-Augmented Generation) systems, vector databases (Pinecone, Weaviate, Milvus), and LLM deployment pipelines.
\n

 * Familiarity with frontier AI tooling, AI coding assistants, and AI-enabled software development workflows.
\n

 * Experience in hyper-growth startup-like environments, with demonstrated success balancing speed, ambiguity, and long-term system health.
\n

 * Familiarity with enterprise systems such as ERP, MES, WMS, PLM, or manufacturing planning systems.
\n

 * Experience in regulated environments (NNPI/ITAR) and secure model/data governance.
\n

 * Demonstrated ability to mentor engineers and set technical direction for AI/ML infrastructure at scale.
\n

 * Experience with MLOps tools including experiment tracking (MLflow, Weights & Biases), feature stores (Feast, Tecton), and model registries.