Founding AI/ML Engineer

Reach Velocity - Emerging Technology ?? ??; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Reach Velocity - Emerging Technology is seeking a Founding AI/ML Engineer to design, build, and scale the AI backbone of its investigative platform. This hands-on role blends backend development, applied machine learning, retrieval systems, and production infrastructure to surface actionable leads across video, audio, text, and structured data.

You will own projects end to end—from architecture to deployment—while collaborating with the founding team on product direction and engineering

Qualifications

• Production-grade backend applications & distributed systems design.
• Experience deploying LLMs, VLMs in real-world deployments.
• Strong Python skills; cloud infra and production deployments.
• Experience with retrieval and data engineering for large datasets.

Responsibilities

• Design, build, and maintain secure, scalable backend services and distributed systems.
• Develop data processing and retrieval pipelines for large volumes of unstructured and structured data.
• Integrate and deploy LLMs, VLMs, and other ML capabilities into production applications.
• Build Retrieval-Augmented Generation (RAG) systems and other retrieval architectures for complex evidence datasets.
• Develop solutions for structured data retrieval, including query planning, semantic modeling, and structured reasoning.
• Build reliable data flows and integrations across AI models, backend services, and infrastructure.
• Deploy, monitor, and optimize production systems in security-sensitive environments.
• Evaluate emerging AI models, frameworks, and infrastructure approaches to improve platform capabilities.
• Take ownership of technical decisions from design to deployment and improvements.
• Collaborate closely with the founding team to define architecture, product direction, and engineering priorities.

Skills

Backend engineering

Python

Cloud infrastructure

Production deployments

LLMs

VLMs

RAG pipelines

Job description

Client is seeking a Founding AI/ML Engineer to help design, build, and scale the AI backbone of its investigative platform.

This is a hands-on engineering role combining backend development, applied machine learning, retrieval systems, and production infrastructure. You will work with large-scale video, audio, text, and structured datasets to develop systems that help investigators surface relevant evidence and actionable leads.

As an early engineering hire, you will take ownership of projects end to end—from system architecture and data pipeline design to AI integration, deployment, and production reliability. The ideal candidate is a strong backend engineer with practical experience deploying AI systems in real-world environments.

Key Responsibilities

• Design, build, and maintain secure, scalable backend services and distributed systems.
• Develop data processing and retrieval pipelines for large volumes of unstructured and structured data.
• Integrate and deploy Large Language Models (LLMs), Vision-Language Models (VLMs), and other machine learning capabilities into production applications.
• Build Retrieval-Augmented Generation (RAG) systems and other retrieval architectures for complex evidence datasets.
• Develop solutions for structured data retrieval, including query planning, semantic modeling, and structured reasoning.
• Build reliable data flows and integrations across AI models, backend services, and infrastructure.
• Deploy, monitor, and optimize production systems in security-sensitive environments.
• Evaluate emerging AI models, frameworks, and infrastructure approaches to improve platform capabilities.
• Take ownership of technical decisions, from initial design through implementation, testing, deployment, and ongoing improvements.
• Collaborate closely with the founding team to define architecture, product direction, and engineering priorities.

Required Qualifications

1. Backend Engineering and Distributed Systems

• Strong backend software engineering experience, particularly building production-grade applications.
• Ability to design resilient, secure, and scalable distributed systems.
• Strong programming skills, especially in Python.
• Experience working with cloud infrastructure providers, backend services, and production deployments.
• Ability to own technical solutions end to end.
• Hands-on experience deploying LLMs, VLMs, or other machine learning models in real-world applications.
• Understanding of integrating AI models into backend systems and production workflows.
• Experience building AI-powered applications beyond prototypes or proof-of-concept projects.
• Ability to evaluate and adopt new models, tools, and AI architectures as the technology evolves.

3. Retrieval and Data Engineering

• Experience designing RAG pipelines or comparable retrieval systems for unstructured data.
• Ability to work with structured datasets and develop effective retrieval and reasoning mechanisms.
• Understanding of data processing, indexing, search, and retrieval architectures.

4. Ownership and Startup Mindset

• Demonstrated bias toward action and strong end-to-end ownership.
• Ability to operate effectively in a fast-paced, early-stage startup.
• Comfort working through ambiguity and making technical decisions with incomplete information.
• Strong curiosity and willingness to experiment with emerging technologies.

5. Mission Alignment

• Genuine interest in applying technology to public safety and law enforcement.
• Motivation to build reliable, secure systems that support sensitive investigative workflows.
• Commitment to high engineering standards and meaningful real-world impact.

Previous experience at an early-stage startup or as an early engineering hire.