Software Engineer, Agents
Design and build agentic systems for AI-native video creation, integrating LLMs and evaluation frameworks to power creative workflows. Requires 5+ years building ML/agentic systems in production.
Applied AI Engineer
Designs, builds, and deploys generative AI systems including LLM-based code reviews, agentic workflows, and RAG pipelines for developer productivity tools. Requires 3+ years in ML/LLM production systems, Python/TypeScript proficiency, and AI frameworks like LangChain.
175k – 275kSan Francisco, CAML EngineeringHybrid3+ YOE
About the role
Responsibilities
- Design and optimize LLM-based systems for high-quality, context-rich code reviews
- Build and refine agentic workflows that reason across multiple steps and contexts
- Develop and maintain knowledge base and retrieval pipelines (e.g., chunking, embeddings, semantic search)
- Deploy generative AI models and pipelines into production and monitor performance
- Collaborate across teams to ensure that AI outputs align with user needs and product goals
- Analyze human-in-the-loop feedback and usage data to iteratively improve system performance
- Apply RLHF, ranking, and reward modeling techniques to improve response quality over time
- Stay current with the latest generative AI developments and apply them to new use cases
Qualifications
- Degree in Computer Science, Engineering, Artificial Intelligence, or related field, or equivalent practical experience
- 3+ years applying ML or LLM-based systems in real-world production environments, with at least 2 years of industry experience focused on generative AI
- Strong programming skills in TypeScript and Python
- AI Frameworks: Experience with tooling such as LangChain, LlamaIndex, OpenAI APIs, or vector databases like Pinecone or Lancedb
- Strong skills in prompt engineering
- Ability to extract insight from telemetry, logs, user signals, and structured feedback
- Comfortable applying research-inspired methods to solve concrete product challenges
- Experience working across product, engineering, and design to deliver production-grade systems
Bonus Points
- Experience optimizing RAG systems and tuning retrieval performance using custom embeddings or search strategies
- Hands-on experience with RLHF pipelines, reward modeling, or behavioral policy tuning in LLMs
- Experience integrating LLM systems into developer tooling or collaborative workflows
- Track record of contributions to open-source projects or publications in applied AI/ML
Compensation
Base pay range: $200k-275k (actual salary based on job-related skills, experience, and location). Strong salary, equity, and benefits package.
Skills
PythonTypeScriptLangChainLlamaindexOpenai ApisPineconeLancedbPrompt EngineeringRLHFRAG
Similar roles
ML Engineering jobsResearch Engineer
Leads pre-training and post-training of action-conditioned world models and VLA models for physical AI applications. Requires PyTorch expertise, distributed training, and ML fundamentals; robotics background preferred.
Software Engineer, Applied AI
Builds and improves core AI agent systems for retrieval, tool use, document understanding, and orchestration in production. Designs evals, analyzes traces, and iterates based on real enterprise workflows using Python and LLM expertise.
Machine Learning Engineer: LLM Interpretability & Systems
Develops systems for LLM interpretability and deterministic governance by working directly with model weights, activations, and architectures. Implements mechanistic interpretability techniques like activation patching and control vectors for enterprise policy enforcement in production.