Forward Deployed Engineer, Video

Posted 2 Days Ago
Be an Early Applicant
Hiring Remotely in MEX
Remote
Mid level
Artificial Intelligence • Big Data • Big Data Analytics
The Role
Own technical delivery for video customers from feasibility through post-delivery support. Translate ambiguous requirements into technical plans, build and operate scalable video data processing and delivery systems, improve dataset measurement and quality tooling, and turn recurring customer needs into reusable platform capabilities. The role requires managing multiple concurrent engagements, partnering across product, engineering, data, and commercial teams, and developing video-specific playbooks, standards, and infrastructure.
Summary Generated by Built In

Company Overview:

We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.

Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.

We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.

About the Role

Protege is hiring an FDE to support our video vertical. This is an engineering role with two key mandates: own the technical success of our video customers, and build the reusable pipes that enable future scale. You will partner closely with the GM of the vertical, product and platform engineering, Data Lab, and commercial teams to navigate external requirements and develop solutions to execute on those requirements. Protege’s video catalog already possesses hundreds of thousands of hours of raw video, this role will be instrumental in continuing to iterate on ways we curate, process, and deliver often bespoke and highly specific datasets to our customers.

This role is ideal for engineers who prioritize both constantly learning through solving difficult problems and interfacing and directly owning customer outcomes. You'll be responsible for managing customer requests, newly ingested partner datasets and architecture decisions all at the same time, often across multiple deals. The pace is fast, the ambiguity is real, and when deals are live, availability outside standard hours is part of the job. If this type of environment and ownership excites you, there could be a strong fit.

What You'll DoOwn Customer Engagements End to End
  • Work with the GM of the vertical, core technical teams, and commercial stakeholders from feasibility through post-delivery support.

  • Translate a customer's model-development goals into an executable technical plan with clear acceptance criteria.

  • Own the implementation and operation of the engagements you lead.

Evolve the Measurement Layer
  • Build the tooling that turns raw footage into something we can describe and sell. Partner metadata is usually thin and inconsistent, so most of what we know about a dataset is what we measured ourselves.

  • Our existing catalog is far too large for any one person to watch and curate from themselves. This role will continue to expand the ways in which we respond to volume requests on new and unique data requests

  • Iterate on solutions for rapidly characterizing and quality checking unknown datasets to continuously expand our catalog offerings

Turn Deal Work Into Product Leverage
  • Work in tandem with the product and core engineering teams to identify areas of growth for the platform based on customer requests and learnings from the front lines.

  • Identify recurring patterns that should become shared cross-vertical platform capabilities and contribute directly to designing and building them.

  • Build the audio vertical's technical playbooks and quality standards so the function scales with every new request.

What Success Looks Like30 Days: Learn and Ship
  • Learn Protege's platform, our existing video catalog, active customer and partner portfolio, and current processing and delivery systems.

  • Pair with an engineer on a live video deal to understand what a delivery actually looks like here.

  • Map the largest gaps in the vertical's tooling and operating model, and propose a prioritized plan for closing them.

60 Days: Own End-to-End
  • Operate and run an active video deal as the primary FDE.

  • Build or meaningfully extend a tool or workflow that came out of a live customer request.

  • Establish a communication cycle with other FDEs and product to surface patterns worth generalizing.

90 Days: Operate Independently
  • Serve as the default technical owner across the video vertical's active portfolio, including multiple concurrent deals.

  • Own end-to-end architecture and delivery for video customers, including post-delivery support and iteration.

  • Establish the first version of the video FDE playbook and reusable toolkit.

  • Maintain a concrete roadmap of platform investments aimed at increasing the vertical's delivery capacity.

What You BringMust Haves
  • 3+ years of experience as an engineer, including meaningful exposure to customers or external technical stakeholders.

  • Experience working directly with media data, with a strong preference for video specifically.

  • Experience building and operating systems that process, analyze, or deliver data at scale

  • Customer-facing ability, including translating ambiguous requirements, communicating trade-offs, and building trust with technical stakeholders.

  • Demonstrated end-to-end ownership, from initial problem definition through implementation, validation, and support.

  • High ambiguity tolerance and bias to action, with the judgment to know when to investigate further or push back.

  • Comfort with the intensity and pace of a fast-moving environment, including multiple concurrent priorities and time-sensitive customer work.

Nice to Haves
  • Hands-on experience with video processing at scale: codecs and transcoding, ffmpeg, shot detection, frame sampling strategies, or perceptual quality measurement.

  • Experience at an early-stage company, as a founding or early engineer, or in another startup-like environment with broad ownership.

  • Hands-on experience with Python and SQL.

  • Experience with search, vector embeddings, semantic retrieval, or ML-assisted data curation.

  • Experience evaluating or deploying vision-language models, including building the evaluation harness rather than just calling the model.

  • Product engineering experience or a strong product mindset developed in close partnership with users.

  • Experience with our cloud and data infrastructure (AWS, Databricks, Dagster, Vercel are the key tools).

Protege Values

Pass the Loved Ones’ Test

We act with integrity and do the right thing — especially when it’s hard and no one is watching.

Always Find a Way

We are resourceful, resilient builders who solve hard problems and push through obstacles.

Go Fast and Grow Fast

Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.

Practice Kindness and Candor

We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.

Deliver Together

We win as one team. Collaboration, accountability, and shared ownership drive our success.

Own the Outcome. Hone the Craft.

We take pride in our work, sweat the details, and continuously raise the bar for excellence.

Skills Required

  • 3+ years of experience as an engineer, including meaningful exposure to customers or external technical stakeholders
  • Experience working directly with media data, preferably video
  • Experience building and operating systems that process, analyze, or deliver data at scale
  • Customer-facing ability to translate ambiguous requirements, communicate trade-offs, and build trust with technical stakeholders
  • Demonstrated end-to-end ownership from problem definition through implementation, validation, and support
  • High ambiguity tolerance and bias to action, with judgment to investigate further or push back
  • Comfort with a fast-moving environment, multiple concurrent priorities, and time-sensitive customer work
  • Hands-on experience with video processing at scale, including codecs, transcoding, FFmpeg, shot detection, frame sampling, or perceptual quality measurement
  • Experience at an early-stage company, as a founding or early engineer, or in a startup-like environment with broad ownership
  • Hands-on experience with Python and SQL
  • Experience with search, vector embeddings, semantic retrieval, or ML-assisted data curation
  • Experience evaluating or deploying vision-language models, including building evaluation harnesses
  • Product engineering experience or a strong product mindset developed in partnership with users
  • Experience with AWS, Databricks, Dagster, or Vercel
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The Company
HQ: Ciudad de Mexico
26 Employees
Year Founded: 2024

What We Do

The biggest unmet need in AI today is getting access to the right training data. Data holders often don’t know where to start and are rightly concerned about governance, intellectual property, and security implications. AI companies can spend years finding and negotiating access to the data they need. Protege is solving these problems by providing an easy-to-use platform to connect data holders with vetted data users.

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