Founding AI Engineer

RiseMe; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Job summary

Coach is building an AI coach for the physical world, starting with in-person sales. We are a SF-based team creating a company brain from field conversations to coach teams and improve performance.

You will own the AI harness, design ML models, and build production-grade backend systems and data pipelines to deliver coaching solutions at scale.

Qualifications

• Strong backend engineering experience.
• Hands-on experience building AI applications.
• Experience developing ML models on real data and moving to product.

Responsibilities

• Own our AI harness and context gathering tooling.
• Create datasets, benchmarks, and feedback loops to measure model quality.
• Design, train, evaluate, and deploy ML models for behavioral analysis.
• Build and operate the backend, data pipelines, and services behind Coach.
• Improve reliability, security, observability, latency, and cost.
• Collaborate to build the software factory enabling team collaboration.
• Engage with customers to understand their needs and translate them into solutions.

Skills

Backend engineering

AI app development

ML modelling

Python

SQL

Cloud

Tools

PostgreSQL

Supabase

FastAPI

Pydantic

Docker

GitHub Actions

LiveKit

Deepgram

ElevenLabs

Google Cloud Run

Job description

About Us

Coach is building the AI coach for the physical world. Starting with in-person sales.

For centuries, field organizations have relied on layers of management to relay information, coach teams, and understand performance. We believe AI will radically change how these companies operate and how people learn and improve at work.

Today, understanding why a sales rep succeeds or struggles still requires being alongside them in the field. That doesn’t scale.

Coach helps employees learn faster, improve, and close more. Managers get a clear view of what’s happening, where people need help, and why, so they can focus on solving problems.

Our custom AI harness turns field conversations and business context into sales intelligence, grounding coaching in what actually happens on the ground. Over time, that accumulated knowledge becomes a company brain: a shared understanding of how the business works.

What you’ll own

• Our AI harness. Build how agents gather context, access tools, retain knowledge, deliver and track coaching grounded in real conversations.
• Evaluation and experimentation. Create datasets, benchmarks, and feedback loops to measure agent and model quality. Investigate failures, challenge apparent patterns, and test whether improvements translate into customer value.
• Machine learning models. Design, train, evaluate, and deploy models for behavioral analysis, performance forecasting, and personalized coaching from dataset construction and feature engineering to validation and monitoring.
• Production infrastructure. Build and operate the backend, data pipelines, and services behind Coach. Improve reliability, security, observability, latency, and cost.
• Our software factory. Work with the team to build the tools, environments, and verification workflows that let our team and coding agents collaborate effectively.
• Forward deploying. You will meet our customers and spend time with them to better understand how to satisfy their needs

Who you’ll work with

We're a small team in SF and Paris, growing fast. Our CTO has been building software and AI across many industries for the last 10 years. Our CEO and COO are repeat founders who built and ran in-person sales teams selling to pharmacies. We've lived the problem firsthand and built Coach to solve it.

With just two employees (Customer Success + Engineering), we took Coach to $1.5M ARR in 8 months — and we're scaling aggressively from here.

What you bring

• Strong backend engineering skills and experience shipping and operating production systems.
• Hands-on experience building AI applications: context management, retrieval, tool use, orchestration, and evaluation.
• Experience developing ML models on real data and taking them beyond experimentation into a usable product.
• Solid foundations in probability, statistics, and machine learning. You can recognize confounding, prevent data leakage, quantify uncertainty, and choose an appropriate baseline.
• Fluency in Python and SQL, and confidence working with cloud infrastructure.
• An AI-native approach to development, with the judgment to understand and verify what your tools produce.
• Curiosity about the people using your work, and the ability to turn an ambiguous business problem into a focused implementation.

Our current stack

• Backend: Python, FastAPI, Pydantic.
• Agent systems: a custom harness built with PydanticAI and frontier model APIs.
• Data: PostgreSQL and Supabase.
• Infrastructure: Google Cloud Run, Cloud Tasks, Docker, and GitHub Actions.
• AI evaluation and observability: Braintrust.
• Voice: LiveKit, Deepgram, and ElevenLabs.

SF-based, relocation supported (visa sponsorship available)