Perle.ai is looking for an Ops Engineer to build the first working prototypes of tools and workflows for annotation projects, turning ideas and customer requirements into functioning systems that prove what's possible, and ultimately shaping the product roadmap, annotation features, and operational capabilities of the company.
Requirements
- Proficiency in Python, SQL, and scripting, with the ability to build, automate, and troubleshoot quickly.
- Strong full-stack development skills (React, Node.js, Django, Flask, or similar).
- Ability to design and implement UI/UX for internal and external web tools.
- Experience with data annotation tools, QA frameworks, and pipelines.
- Comfort working across cloud infrastructure such as AWS, GCP, or Azure, and connecting APIs.
- Strong debugging, problem-solving, and “fix it now” mentality.
- Bonus: exposure to medical or legal datasets, compliance workflows, or sensitive data annotation.
Responsibilities
- Set up, configure, and launch data annotation projects across text, audio, video, image, and specialized domains such as medical and legal.
- Build prototypes of tools, workflows, and annotation systems, including UI/UX design and full-stack web applications that validate customer needs and internal hypotheses before they’re scaled by EPD.
- Build scrappy web-based dashboards, internal tools, and annotation interfaces that are functional and usable on day one.
- Write scripts, connect APIs, and automate pipelines to accelerate how we launch and scale projects.
- Be the first line of defense when pipelines break or projects stall.
- Troubleshoot live, fix things fast, and keep projects moving under pressure.
- Prototype, test, ship, and iterate constantly.
Other
- 3+ years of hands-on engineering experience, ideally in annotation projects, ML ops, or startup environments.
- A scrappy engineer who’s just as comfortable writing quick Python scripts as building functional web tools with clean UI/UX.
- You thrive in ambiguity and fast-moving environments, figuring things out as you go without waiting for perfect processes.
- You’ve worked with annotation projects, training data, or ML workflows before and know what good data looks like.
- You understand the nuances of labeling across text, speech, and vision, and how to set projects up for success.
