Video Engineer

We are looking for an excellent Video Engineer to join one of Sweden's fastest growing start-ups which is backed by Nvidia, Microsoft and AWS. The company also operates under the Inspection for Strategic Products.

Location: Fully remote within India. From 2027 you join our Bangalore office as part of the founding local team

Employment: Full-time, permanent

Start: As soon as you are available

About the job

Antrino Labs is building the platform that makes every square meter intelligent.

The physical world runs on processes nobody can actually see. Goods move, people queue, machines idle, space goes unused, and the decisions made about all of it are based on samples, guesses, and reports written after the fact. Software solved this for the digital world twenty years ago. Everything online is measured, understood, and acted on in real time. The physical world is still dark.

We are building the execution layer that closes that gap. Antrino lets any organization deploy vision intelligence into a physical space and get back what is actually happening there — as structured, queryable, real-time information rather than footage. Not a research project, not a custom integration, not a team of ML engineers. A platform, where you describe what matters to you and deploy it.

What people build on it is broader than what we designed for. Operators use it to understand processes, find where time and space are being wasted, measure flow and utilization, catch problems while they are still happening, and turn all of it into data good enough to act on.

Everything is built on Privacy by Design. Data protection and GDPR compliance are part of the architecture, not a layer added afterwards.

We recently closed our Seed round, announced together with Dagens Industri, and we are scaling to meet demand. Antrino Labs is also registered with Inspektionen för strategiska produkter (ISP), the Swedish authority for strategic and dual-use products.

The role

We are hiring an experienced Video Engineer to own the pipeline that gets a live stream from a physical space into something our models can reason about, and back out again as something a person can watch.

This is a different problem from the other engineering roles here, and it is worth being precise about the boundary. MLOps owns whether a model runs well once it has frames to look at. AI Backend owns what happens to a model's output once it becomes an event. You own everything before and after that: getting a stream in reliably, in a form that is fast and cheap for models to consume, and getting live and archived footage back out to a customer at low latency without falling over as the number of streams grows into the thousands.

Concretely, every stream we ingest has to survive unreliable networks, cameras that drop and reconnect, and wildly inconsistent source quality, and still come out the other side as something our detection pipeline and our customers can depend on. We convert continuous ingestion into a live delivery format, manage that conversion at fleet scale, and generate evidence clips and archives that hold up when someone needs to look back at exactly what happened. None of this is a solved problem at our scale yet, and there is real room here to build the thing that becomes obviously correct rather than inherit someone else's answer.

Your main areas of responsibility will include:

• Owning our stream ingestion and conversion pipeline end to end, including the service that manages ingestion and live delivery across a growing fleet of concurrent streams
• Designing for unreliable source conditions — dropped connections, inconsistent bitrates, camera reboots, and network hiccups — so a stream recovers on its own and stays trustworthy
• Building and tuning the transcoding and packaging layer, balancing latency, bandwidth, storage cost, and the quality our models actually need to see
• Generating and managing evidence clips and archive footage that customers and operators can retrieve, review, and trust as an accurate record
• Optimizing frame delivery specifically for model consumption, including sampling strategy, resolution, and format choices that keep inference cheap without losing what matters
• Scaling live delivery to customers, including low-latency playback and the infrastructure decisions that keep thousands of concurrent streams performant
• Working with storage lifecycle and retention for both live and archived footage, including cost-aware tiering as volume grows
• Debugging video-specific production issues: corrupted streams, drift between audio and video where relevant, codec incompatibilities, and the class of bug that only shows up under real network conditions
• Working closely with the team building on-site compute, since some ingestion and processing happens on hardware inside a customer's facility, reached over a private network
• Partnering with the AI Backend and MLOps teams so what you deliver upstream is exactly the input their systems need, not a generic video feed they have to work around
• Keeping an eye on the video and streaming ecosystem and bringing in the tools, codecs, and protocols worth adopting

Our stack

You do not need experience with all of this, but you should recognize most of it and be able to argue about it.

• Streaming: RTSP ingestion, RTSP→HLS conversion, live and archive pipelines, our own ingestion and delivery management service
• Backend: Python, FastAPI, alongside TypeScript services on the product side
• AI: our own first-party detection models, backed by Anthropic and Google model APIs where a frontier model is the right tool
• Data: PostgreSQL through Supabase, with row-level security
• Infrastructure: Azure, object storage, GitHub Actions, on-site compute reached over Tailscale

Who we are looking for

• Substantial experience building or operating production video or streaming systems at real scale — live delivery, transcoding, or media pipelines that real users depended on
• Strong Python, and comfort working close to the network and protocol layer, not just calling a library
• Real working knowledge of streaming protocols and formats: RTSP, HLS, and the tradeoffs between them
• Experience with transcoding and codecs, and an informed opinion on when quality, latency, and cost genuinely trade off against each other
• A track record of building systems that degrade gracefully under bad network conditions rather than falling over
• Experience feeding video into a machine learning pipeline is a strong advantage — you understand that what a model needs from a frame is different from what a person needs
• Comfort with cloud infrastructure and storage systems at scale, including lifecycle and cost management
• You make decisions with incomplete information, take responsibility for the outcome, and say so clearly when something you built was wrong
• You work well asynchronously across time zones, write clearly, and raise problems early
• Excellent written and spoken English

What we offer

• Competitive salary, benchmarked to the top of the Indian market for this level and discussed openly early in the process
• Fully remote within India, with genuine ownership rather than delegated tickets — and a seat in the founding group of our Bangalore office from 2027
• Hardware of your choice and a budget for whatever else you need to work well
• Direct access to founders, to the Stockholm engineering team, and to customers
• Travel to Stockholm to work with the team in person

Process

We review applications continuously and contact candidates we think are a good match.

1. An introductory conversation about your background and the role
2. A technical conversation — we discuss systems you have built and how you made the decisions you made. No whiteboard algorithm puzzles
3. A working session with the engineering team on a real problem from our stack
4. A conversation with the founders about ownership, options, and terms

References may be requested in the final stage. We aim to keep the process short and to respect your time.

Apply with your CV and, if you have one, a link to something you have built. A repository, a shipped product, or a short note about a system you are proud of tells us more than a cover letter.