Senior AI Application Engineer
Share
Job Location:
Milpitas, CA - USA
Monthly Salary:
Not Disclosed
Posted on:
3 hours ago
Vacancies:
1 Vacancy
Job Summary
We are seeking an Sr. AI Application Engineer to develop and deploy advanced AI systems that bridge data-driven intelligence with real-world engineering. The ideal candidate will have a strong foundation in machine learning agentic AI and physics-informed modeling and will apply these techniques to complex engineering problems such as multi-objective design optimization AI model fine-tuning simulation acceleration and autonomous decision-making systems.
This role requires both theoretical depth in AI/ML algorithms and practical engineering insight to create robust deployable solutions in domains such as mechanical electrical aerospace or materials engineering.
Key Responsibilities
- AI Model Development & Optimization
- Design train and fine-tune machine learning and deep learning models for engineering applications.
- Implement physics-informed neural networks (PINNs) surrogate modeling or hybrid AI-physics systems.
- Develop and apply multi-objective optimization algorithms (e.g. genetic algorithms reinforcement learning Bayesian optimization).
- Agentic AI & Autonomous Systems
- Build and integrate AI agents capable of autonomous reasoning decision-making and goal-directed task execution in engineering workflows.
- Employ agentic architectures (e.g. multi-agent systems LLM-based reasoning agents) for simulation orchestration and design automation.
- Apply reinforcement learning or control-oriented AI for adaptive engineering systems.
- AI Deployment & Integration
- Implement scalable inference pipelines for real-time or batch processing in production environments (cloud or edge).
- Collaborate with DevOps/MLOps teams to deploy AI models using frameworks like TensorFlow Serving TorchServe ONNX or NVIDIA Triton.
- Integrate AI systems into engineering tools and environments (e.g. CAD/CAE software simulation platforms or digital twins).
- Research & Innovation
- Explore novel applications of physics-based AI agentic reasoning and self-improving systems within engineering contexts.
- Stay current with advancements in foundation models multi-modal AI and generative design.
- Contribute to white papers patents and internal R&D initiatives.
- Collaboration & Communication
- Work closely with domain engineers data scientists and software developers to translate engineering challenges into AI-driven solutions.
- Communicate complex technical concepts to multidisciplinary teams and stakeholders.
Required Qualifications
- Education:
- M.S. degree with 5 years of industry experience or Ph.D. degree with 3 years of relevant experience in Artificial Intelligence Computer Science Electrical or Mechanical Engineering Applied Physics or a related field.
- Technical Expertise:
- Strong proficiency in Python and familiarity with PyTorch TensorFlow or JAX.
- Experience with machine learning algorithms deep learning architectures and optimization methods.
- Solid background in numerical methods physics simulation or computational modeling.
- Familiarity with agent frameworks (e.g. LangChain AutoGen CrewAI OpenDevin or custom LLM orchestration systems).
- Proficiency in MLOps data pipelines and AI deployment on cloud or edge infrastructure (AWS Azure GCP).
- Soft Skills:
- Strong analytical thinking problem-solving and communication skills.
- Ability to work in multidisciplinary teams and adapt to evolving technologies.
Preferred Qualifications
- Experience with reinforcement learning evolutionary algorithms or active learning for design optimization.
- Background in multi-physics simulation (CFD FEA thermal or electromagnetic).
- Prior exposure to generative AI for engineering design or AI-driven control systems.
- Familiarity with digital twins and real-time sensor integration.
- Publications or patents in AI for engineering or computational sciences.
