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.
About the job
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
Python, TypeScript, LangChain, Llamaindex, Openai Apis, Pinecone, Lancedb, Prompt Engineering, RLHF, RAG
Similar jobs
ML Engineering jobs
Mirage
New York, NY
Build and deploy agentic systems that power AI-driven creative video workflows. The role requires 5+ years of experience, production ML or agentic pipeline development, context engineering, and expertise in evaluation and agent infrastructure.
Mirage
New York, NY
Build and advance agentic machine-learning systems for multimodal creative tasks, with a focus on video understanding, reasoning, control, and tool use. The role requires strong production ML or agent-pipeline experience and deep knowledge of modern LLM techniques.
Taste Labs
San Francisco, CA
Build evaluation methods, RL environments, agent tooling, and scalable infrastructure that make subjective qualities such as design and taste measurable for frontier AI models. The role requires experience with evaluations, RL environments, ML or post-training, plus strong backend engineering skills.
Mirage
New York, NY
Build and scale generative video and multimodal models, optimizing training and inference for efficiency, throughput, and ultra-low latency. The role requires deep learning systems expertise, strong PyTorch/CUDA experience, and the ability to move research models into production.
Clay
New York, NY
Build and ship production AI agents and the platform infrastructure that makes them reliable, steerable, and measurable. The role requires strong backend fundamentals, production LLM or agent experience, and expertise in evaluations, retrieval, orchestration, or tool-use design.