---
title: 'Founding Software Engineer, Platform & Infrastructure - DeepFrame - at Infinimind at Cox Exponential'
canonical: 'https://feeny.ai/job/founding-software-engineer-platform-infrastructure-deepframe-at-infinimind-cox-n4e5z9ejsdre'
type: 'job'
last_seen: '2026-11-09'
---

# Founding Software Engineer, Platform & Infrastructure - DeepFrame - at Infinimind at Cox Exponential

- **Company:** Cox Exponential
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-10-23
- **Last confirmed live:** 2026-11-09
- **Apply:** https://jobs.gem.com/cox-exponential/am9icG9zdDrpRrCeAMXClT5oxMaHfR_t

## Job description

What DeepFrame is:

DeepFrame is InfiniMind's video intelligence platform. Enterprises point it at video they already have - broadcast programming, in-store footage, operations video - and get evidence-backed answers: search across thousands of hours, questions answered with timestamped clips, and rulebook reviews that turn a company's own standards into findings with severity and proof.

We are not an AI wrapper. We build the full stack ourselves: our own video-understanding foundation models, ingestion and indexing across visual, speech, on-screen text, and time, a search engine, and agents on top. It runs as SaaS or single-tenant in the customer's own AWS account, with on-prem options for footage that cannot leave the building.

DeepFrame has paying enterprise customers in Japan in broadcasting and retail. You will be the first engineer on the ground in the US, working with a US GTM lead hired in parallel and with our core engineering and research team in Tokyo.

What this role really is:

An infrastructure and platform engineer with a feel for AI - not an "AI engineer" who wraps someone else's API. Roughly:

- ~75% building the platform - GPU inference and video-processing infrastructure, ingestion and indexing pipelines, search and serving, performance and cost at scale, shipped into the shared DeepFrame codebase with the Tokyo team
- ~25% with customers - joining technical meetings with the GTM lead, answering integration and security questions, and getting pilots running on the customer's own video When a US customer needs something, you build it properly in the core product, not as a one-off fork.

How the US team works:

You and the US founding GTM lead are DeepFrame's US team. They lead the customer conversation; you join technical discovery and make sure what we commit to can be built and run.

- Daily with the CTO on the technical side - architecture, priorities, code review, and how US needs land in the core product with the Tokyo engineering team
- Daily with the CEO on the business side - which customers and pilots matter most, and what we commit to
- A short daily sync across San Francisco and Tokyo (late afternoon Pacific / morning Japan), plus a weekly US pipeline and pilot review
- You report to the CEO. The CTO owns architecture, technical direction, and code review; when commercial and technical priorities conflict, the CEO decides what we commit to a customer, with the CTO owning technical feasibility

What you will do:

- Build and run GPU infrastructure for video understanding on AWS: model serving, batching, scheduling, and cost per video-hour
- Scale ingestion and indexing pipelines for large video libraries: decoding, transcoding, frame, audio, and on-screen text processing, embeddings
- Work on the search and retrieval layer and the APIs customers integrate with
- Build connectors to where US customers keep video (S3, media asset management systems, camera exports) and to where they want results (APIs, webhooks)
- Deploy for enterprise customers - SaaS, single-tenant in their AWS account, or on-prem - and own reliability and observability
- Join customer technical meetings with the GTM lead and turn what you hear into product requirements

Your first 90 days:

- Day 30 - DeepFrame running in a test AWS environment. First production change merged. A measured view of where our GPU and pipeline costs go.
- Day 60 - One infrastructure improvement owned end to end (throughput, cost, or reliability). Pilot environment ready for the first US customer.
- Day 90 - Technical owner of the first US pilot, from data access through first results review. At least one US-driven feature or connector shipped to the core product.

Must Haves:

- 5+ years building production infrastructure or backend platforms, with real distributed-systems depth
- Strong Python, plus a systems language (Go, Rust, or C++)
- GPU workloads in production - you have run and optimized model inference or ML pipelines on cloud GPUs (CUDA, TensorRT, Triton, or similar); AWS preferred; containers and Kubernetes
- Large data or media pipelines - you have moved and processed a lot of data reliably and cheaply
- Can explain technical tradeoffs to a customer's engineers in a meeting
- Authorized to work in the United States by your start date; we do not sponsor visas for this role
- Based in the Bay Area, in person in San Francisco at least 3 days a week, and able to travel to customers occasionally

Strong pluses:

- Video and media processing (ffmpeg, codecs, GStreamer, NVIDIA DeepStream / NVDEC)
- Search infrastructure: vector databases, retrieval, and ranking at scale
- ML systems: serving foundation models, evaluation pipelines
- Enterprise deployment: VPC / on-prem installs, SSO, security reviews
- Worked with a team across time zones, especially Japan; working proficiency in Japanese is a plus, not required

## About Cox Exponential

## Company Overview
- **One-liner**: Cox Exponential (CX2) is the early-stage AI venture vehicle for Cox Enterprises, partnering with exceptional entrepreneurs to build breakthrough AI/ML companies.
- **Entity Type**: Private (venture capital / accelerator; part of Cox Enterprises)
- **Headquarters**: Silicon Valley, California, United States (with remote presence and an international outlook)
- **Founded**: 2025
- **Founders**: Founded by technologists with decades of experience leading startups and building AI/ML products; key executives include Managing Partner Radu B. Rusu, General Partners Pantelis Kalogiros, Yukiko Jenkins, and others.

## Core Business
- **Primary industry**: Venture Capital & Private Equity (AI-focused early-stage investing)
- **Target customers**: B2B – exceptional entrepreneurs and early-stage AI companies seeking capital, mentorship, and access to the Cox family of businesses.
- **Mission**: Develop breakthrough technology that helps people flourish and bring firsts to market.

## Products & Services
Cox Exponential does not sell a product; it operates as an investment vehicle. Its main “offering” is its accelerator program and venture partnership:
- **AI Accelerator & Venture Program**: Provides capital, technical mentorship, business planning, code/prototype development, and connection to the Cox Enterprises ecosystem.
- **Portfolio Support**: Invests in and supports portfolio companies such as Spacer Robotics (autonomous rebar-tying robot), TrustMind, TrustFoundry.ai, and others.

## Market Standing
- **Valuation / Funding**: Not publicly disclosed (backed by Cox Enterprises, a large private conglomerate).
- **Key Metric**: Total employees ~20 (as of mid-2026); headcount grew ~144% year-over-year since founding in 2025.
- **Notable Investors / Partners**: Cox Enterprises (parent); key executives from Scale Venture Partners, Fyusion, AGI House, etc.
- **Growth Signals**: Rapid team expansion, active hiring for senior engineering roles (AI infra, robotics, efficient ML), multiple open remote positions.

## Competitive Advantages
- **Cox Enterprises Backing**: Access to the breadth of a Fortune 500 company’s resources, industry connections, and potential pilot customers.
- **Founder-Led by Technologists**: The team comprises experienced AI/ML startup builders, offering hands-on technical guidance to portfolio companies.
- **Global Outlook**: Based in Silicon Valley with an international perspective, attracting talent from the US and Portugal.
- **Early-Stage Focus**: Dedicated to pre-seed and seed AI startups, differentiating from larger, less specialized corporate venture arms.

## Strategic Focus
- **Current priorities**: Scaling the accelerator, expanding portfolio companies, and building in-house AI infrastructure talent to support portfolio needs.
- **Direction for growth**: Deepening ties with Cox business units, investing in frontier AI (LLMs, multimodal models, agentic RL, robotics), and hiring top-tier engineers to work on internal AI platforms and portfolio support.

## Why Work Here
- **Culture**: Early-stage, high-autonomy environment within a stable corporate parent. Employees work on cutting-edge AI challenges across multiple portfolio companies.
- **Remote/Hybrid**: Fully remote workspaces for many roles; some positions require in-office presence in San Francisco (e.g., robotics roles). The company has 8 locations but emphasizes remote flexibility.
- **Notable Perks**: Opportunity to work on diverse AI/ML projects (LLMs, robotics, efficient ML systems), exposure to multiple startups, and a flat hierarchy with direct access to experienced partners.
- **Engineering Culture**: Strong emphasis on technical excellence, with open roles for Founding Engineer (AI Infra), Research Scientist (Efficient ML), Robotics Software Engineer, and Systems Engineer.

## Sources
1. [LinkedIn – Exponential by Cox](https://linkedin.com/company/exponential-by-cox)
2. [Built In – Cox Exponential](https://builtin.com/company/cox-exponential)
3. [Built In – Cox Exponential Jobs](https://builtin.com/company/cox-exponential/jobs)
4. [The Org – Cox Exponential (CX2)](https://theorg.com/org/cox-exponential-cx2)
5. [RemoteFront – Remote Jobs at Cox Exponential](https://www.remotefront.com/companies/Cox%20Exponential)

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