System Engineer

System Engineer at In The Loop System Engineer at In The Loop in Mount Rainier, Maryland Posted in 3 days ago.

Type:

full-time

Job Description:

About In The Loop (ITL) We're In The Loop, a vertical software suite transforming the massive, overlooked world of secondhand retail. Thrift stores move billions of items every year, yet many still rely on handwritten tags, guess-based pricing, and fragmented systems. We're changing tht. Over the past year, we've partnered with some of the largest thrift operations in the country and have helped process 85,000+ garments per month. Now, with demand surging and new product modules shipping, we're officially in scaling mode. We're backed by leaders in the industry: including eBay, the former CTO of Depop, and the former CTO of Hepsiburada. What We Build Our system plugs directly into the workflows of thrift processors, POS systems, and e-commerce marketplaces.

We help stores go from:

handwritten tags ? automated listings, guess-based pricing ? dynamic, data-driven pricing, lost revenue ? recovered value external, messy data dashboards ? dynamic, daily views Why This Space Matters Massive, broken industry: $211B re-commerce market and growing.

Complex workflows:

Every store and warehouse looks different, making it the perfect playground for problem-solvers.

First movers:

No one else is building a purpose-built production system for thrift.

Clear ROI:

Customers see velocity increases and revenue lift in months, not years. About the Team We're a small, product-obsessed team with deep experience in AI, resale, reverse logistics. We've built and scaled startup products from 0?1. At ITL, everyone wears multiple hats and is passionate about driving success for our customers. Systems Engineer The Role We are looking for someone who has shipped ML or computer vision systems in physical, operational environments and who is comfortable owning hardware and software together. If you have spent your career purely in web backends or SaaS, this is likely not the right role. What You'll Do / Requirements Computer Vision & Physical Capture Systems Designing and deploying camera-based capture systems in real operational environments Understanding of optics, lighting, and image quality requirements for accurate CV inference Experience evaluating and specifying hardware

• cameras, mounting, edge compute
• not just consuming camera feeds in software Familiarity with the gap between lab performance and production-floor reliability Exposure to wearable or embedded capture systems is a plus ML Infrastructure & LLM Pipeline Engineering Production-grade deployment of ML models and LLM pipelines on cloud infrastructure LLM orchestration: prompt design, caching, retry and fallback logic, provider routing Latency optimization across inference pipelines•identifying and eliminating bottlenecks from capture to prediction to UI Cost management at scale•token consumption, GPU instance economics, reserved capacity vs.

on-demand tradeoffs Technical Leadership & Ownership Leading engineering team with accountability for outcomes Can set direction for other engineers clearly and without micromanaging Moves fast without leaving things broken

• ships iteratively but with engineering judgment Communicates technical decisions to non-technical stakeholders clearly Ownership orientation: you finish what you start and you care about whether it works in production What We're Looking For We care about demonstrated evidence over credentials.

The following are signals we weight heavily, regardless of years of experience. Non-negotiable You have shipped a CV or ML system that runs in a real physical environment and is central to a client's operations You have made consequential architecture decisions and can speak to the tradeoffs clearly You are comfortable owning a problem end-to-end

• from hardware spec to cloud deployment to production monitoring You are based in Washington, DC or Northern Virginia and available to work in-office Strong signal Background in robotics, computer vision at the edge, warehouse or logistics automation, manufacturing systems, or any domain where ML meets physical environments You have specified or evaluated camera hardware, capture rigs, or edge compute for a real deployment You have worked at a startup or small team where you had to make decisions without a playbook and live with the consequences You have optimized a high-throughput inference pipeline for latency or cost at production scale Experience with frontier LLM APIs (Gemini, OpenAI) in production, including quota management and fallback handling How To Apply your resume to zahra@intheloopai.

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