Applied AI Engineer

Rheaction; San Francisco (CA); On-site; USD 180,000 - 325,000; Full time

Job summary

Rheaction in San Francisco is seeking an Applied AI Engineer to own and evolve production AI systems used by real customers. You will turn modern AI models into reliable, scalable production products across model selection, fine-tuning, evaluations, AI infrastructure, and product development.

The role demands strong AI fundamentals, excellent software engineering, and product instincts, with potential growth into an AI leadership role. This is an in-person, full-time position in San Francisco.

Qualifications

• 4–8 years of applied AI, ML, or software engineering experience.
• Production AI systems experience with real users.
• Strong Python and PyTorch or TensorFlow experience.
• Hands-on with LLMs, fine-tuning, evaluations, and model selection.
• Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, DSPy, Instructor.

Responsibilities

• Build, deploy, and improve production LLM-powered applications.
• Evaluate and select models based on performance, cost, latency, and product needs.
• Fine-tune models using proprietary datasets.
• Build evaluation frameworks to measure AI performance.
• Design prompting and orchestration strategies for production AI agents.
• Develop feedback loops to improve AI systems from real-world use.
• Build scalable AI workflows, APIs, and data pipelines.
• Monitor model performance and run experiments against quality metrics.
• Partner with product and engineering to translate business problems into AI solutions.

Skills

Python

PyTorch

TensorFlow

LLMs

Fine-tuning

Model evaluation

LangChain

LlamaIndex

AI workflows

Software engineering

Tools

LangChain

LlamaIndex

DSPy

Instructor

Job description

Location: San Francisco, CA — In Person
Employment: Full-time
Experience: 4–8 years
Compensation: $180,000–$325,000 + equity
Visa Sponsorship: H-1B transfer and TN supported

About the Role

We're looking for an exceptional Applied AI Engineer to own and evolve production AI systems used by real customers.

This is a hands‑on role for an engineer who knows how to take modern AI models beyond prototypes and turn them into reliable, scalable production products. You'll work across model selection, fine‑tuning, evaluations, AI infrastructure, and product development.

The ideal candidate combines strong AI fundamentals with excellent software engineering and product instincts, with the opportunity to grow into a broader AI leadership role.

What You'll Do

• Build, deploy, and continuously improve production LLM-powered applications.
• Evaluate and select models based on performance, cost, latency, and product requirements.
• Fine-tune models using proprietary datasets.
• Build robust evaluation frameworks to measure and improve AI performance.
• Design prompting and orchestration strategies for production AI agents.
• Develop feedback loops that allow AI systems to improve from real‑world interactions.
• Build scalable AI workflows, APIs, and data pipelines.
• Monitor model performance and run experiments against defined quality metrics.
• Partner closely with product and engineering teams to translate business problems into AI solutions.

What We're Looking For

• 4–8 years of applied AI, ML, or software engineering experience.
• Track record of deploying production AI systems that real users depend on.
• Strong Python and experience with PyTorch or TensorFlow.
• Hands‑on experience with LLMs, fine‑tuning, evaluations, and model selection.
• Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, DSPy, Instructor, or similar.
• Strong software engineering and systems‑design fundamentals.Product mindset and ability to turn business requirements into practical AI solutions.
• Strong ownership, communication, and ability to operate in a fast‑moving startup.
• Able to work in person in San Francisco.

Strong Plus

• AI agent or conversational AI experience.
• Voice AI, telephony, or real‑time AI systems.
• RLHF / RLAIF experience.
• Experience building AI evaluation infrastructure or feedback loops.
• Experience with AI systems in regulated or compliance‑heavy industries.
• Founding engineer or early‑stage startup experience.
• Open‑source AI projects, technical writing, or other independently shipped work.