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Founding Software Engineer, Platform & Infrastructure - DeepFrame - at Infinimind
Cox Exponential
Founding Software Engineer, Platform & Infrastructure - DeepFrame - at Infinimind
Be an Early Applicant
Build and operate DeepFrame’s GPU-powered video intelligence platform, including model serving, video ingestion, indexing, search, APIs, customer connectors, and enterprise deployments across AWS, single-tenant, and on-prem environments. Own reliability, observability, performance, and infrastructure costs while collaborating with Tokyo engineering and joining customer technical meetings. Serve as the technical owner for initial US pilots and deliver customer-driven product improvements.
What DeepFrame is:
What this role really is:
How the US team works:
What you will do:
Your first 90 days:
Must Haves:
Strong pluses:
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.
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
- 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
- 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.
- 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
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