Machine Learning Engineer

About this role

Work at Blissway: Opportunity for Impact Every Day 🚀✨ Blissway is a startup that simplifies toll collection and dramatically improves highway safety. We are multiple startups in one: Deep Tech, AI/ML, Hardware, SaaS, and IoT. For the past five years, we have built a nearly insurmountable technological lead in tolling, an industry that is quietly bigger than football. While our competitors have thousands of employees, we operate with a lean but growing team of less than 30. We’ve stayed under the radar, but our impact is visible on the massive Interstate Highway System connecting every major US metro (except Juneau, AK—sorry, Juneau). YOU ARE A GOOD FIT IF... - You love the grind: You take ownership and put in the time to meet deadlines. Our recent team survey showed an average of 55 hours/week, with occasional 70+ hour bursts for major releases. - You are detail obsessed: You have experience writing code that stands up to the unpredictability of the physical world, where the small details are the difference between success and failure. - You are adaptable: We are a lean team. If you only want to work on a "niche thing" or are uncomfortable helping other teams when they need a boost, this isn't the place for you. We expect you to figure out what you need to get stuff done. - You code really well: We mostly use Python and TypeScript, but we don’t care what you’re most proficient in today—as long as you learn fast.   MACHINE LEARNING ENGINEER The Mission: You are the engineer who ships the model, not just the one who trains it. At Blissway, we process 11 million images every single day, running detection, segmentation, classification, embeddings, and re-identification across everything in the camera’s frame. You will own the full arc: raw sensor data to production inference, dataset curation to deployment monitoring, cloud to edge. This role is for the engineer who sees a model sitting in a notebook and feels the itch to put it to the test, or in this case, on the road.   The Work - Own the Whole Pipeline: You decide which problems are worth solving, then take them from raw sensor data all the way to production: collection, dataset curation, training, deployment, monitoring, and iteration. We wire it all together and run our own servers, so the pipeline is yours end to end. - Real Hardware in the Real World: This is the part that makes us special. We own the devices in the field. This means any idea you have can actually get built and tested on real roads. - Vision at Real Scale: We process 11 million images every single day, running detection, segmentation, classification, embeddings, and re-identification across everything in the frame: vehicles, license plates, wheels, even lane markings. Beyond images, we have multiple other sensors on the road pulling in different data making the problem space wide open. At this volume, the right model can drastically improve accuracy and cut cost at the same time. - Classical CV to Custom SOTA: Our toolbox spans the full range, from traditional computer vision algorithms to custom-trained state-of-the-art models (detection, segmentation, embeddings, classifiers). You pick the right tool, and when nothing off-the-shelf is good enough, you train your own. - Build the Best Models That Exist: We read the papers, go to the conferences, and hold our work to the current frontier. ML has become essential to Blissway over the past year, and this team is where that bet gets made real. - Edge and Cloud: Most of our compute lives in the cloud where power is effectively unlimited. We're now pushing more inference onto the roadside hardware itself, a completely different problem: the models have to be fast, small, and power-efficient without giving up accuracy. You'll work both sides of that constraint.   Requirements - Experience: 2 to 6 years of software engineering with a focus on machine learning and/or computer vision. We want someone with true end-to-end experience: you've taken models from raw data to production and owned what happens after they ship. - Both Sides: Strong software engineering fundamentals plus hands-on ML. You write production-quality code and you train and debug models. The two overlap, and we want someone comfortable across both. - Full Lifecycle: You've owned models beyond the notebook: trained, deployed, monitored, and iterated in production. - Real CV depth. Experience with modern computer vision and the judgment to know when a classical technique beats a heavy model. You're not reaching for a transformer when a filter will do.   How We Work We're a small team, which means a more deliberate interview process than you might be used to. In exchange, you get real autonomy from day one, direct access to the people setting technical direction, and work that ships to real roads in weeks, not quarters.   The Process: We hire software engineers in cohorts. Rather than evaluate candidates on a rolling basis, we collect applications over a two-week window and assess everyone together. This allows us to make fair, considered comparisons and move the strongest candidates through the process efficiently and fairly. The process has five stages: 1. Application Review: We (a human) read every application carefully at the close of the approximately two-week window. 2. Ezra: A brief AI-assisted screening to make sure we're a mutual fit before investing more of your time. Think of this as a way to augment your application. 3. Screening Interview: A focused conversation to understand your background and motivations. 4. Technical Interview: A deeper evaluation of your skills and how you think through problems. 5. Experience Interview: A conversation about how you've worked and what you've built. 6. Final In-Person: A chance to meet the team and see the work environment firsthand. We'll be in touch at the close of the application window with next steps. The Essentials (Health & Wealth) - Relocati