The role
- COMP
- $220K - $320K
- EQUITY
- Early stage equity
- LOCATION
- Los Angeles
- WORKPLACE
- On-site
- EXPERIENCE
- 2+ years
- VISA
- None
- STACK
- AWS, Python, PyTorch
- INDUSTRY
- AI, Hardware, Robotics, Software Development
/roles — ROLE_476
Applied AI Engineer
Newly founded startup building an AI-driven platform for engineering machine control systems
The company
A newly founded startup building an AI-driven software platform for engineering the control systems that run machines, working across hardware, robotics, and software.
- STAGE
- Series A-stage
- FUNDING
- $7M raised
- TEAM
- founding team of <10
- FOUNDED
- 2026
JD — the work
About the role
You would join a team of about six, on-site in Los Angeles, at a funded startup building a new software platform for engineering machine control systems. The role centers on agentic AI: building the harnesses, integrations, and original techniques that let LLM-based agents do real engineering work, and choosing architectures that will scale. It suits someone who can link classical computer science and statistical ML with current LLM methods, and who has already put that theory to work on concrete projects.
What you'll do
- Set the architecture for the product's AI agents and LLM-driven workflows, choosing approaches that scale
- Build the integrations and harnesses that let agents operate inside real engineering workflows
- Develop original techniques for agent-driven workflows where standard patterns fall short
- Combine classical methods such as Bayesian statistics and regression with LLMs and retrieval
- Turn AI and ML theory into concrete, working implementations
What they're looking for
- 2+ years of software engineering on a solid computer science foundation
- Grounding in classical ML, including Bayesian statistics and regression
- Working knowledge of current LLM techniques such as retrieval-augmented generation and agents
- A clear understanding of how the components of an AI system interact
- Comfort with Python, PyTorch, and AWS
Nice to have
- Research experience or graduate study in LLMs, AI, or machine learning
- Fine-tuning or post-training models for narrow, specialized tasks
- Optimization techniques in both supervised and unsupervised settings
- Distributed systems, large-scale data processing, and semantic or numeric search and indexing