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Blissway is a deep-tech startup that simplifies toll collection and improves highway safety through AI/ML, hardware, SaaS, and IoT. The company processes 11 million images daily across the US Interstate Highway System and operates with a lean team of fewer than 30 people.
As a Machine Learning Engineer, you will own the complete ML pipeline from raw sensor data to production inference. This is not a research role—you ship models to real roads. Your responsibilities include:
- End-to-end ownership: dataset curation, model training, deployment, monitoring, and iteration
- Working with real hardware deployed in the field, enabling rapid testing of ideas on actual roads
- Computer vision at scale: detection, segmentation, classification, embeddings, and re-identification across 11 million daily images
- Choosing the right tool for each problem, from classical computer vision algorithms to custom state-of-the-art models
- Balancing cloud and edge inference: optimizing models for power efficiency and speed on roadside hardware while maintaining accuracy
- Collaborating across a small team where you'll contribute beyond your core focus when needed
The role spans the full ML lifecycle: you've trained models, deployed them to production, monitored their performance, and iterated based on real-world results. You write production-quality code and have hands-on experience debugging both software and ML systems. You understand when a classical technique outperforms a heavy model and make pragmatic technical decisions.
Blissway operates with high autonomy and direct access to technical leadership. Work ships to production in weeks, not quarters. The team averages 55 hours per week with occasional 70+ hour bursts for major releases.
REQUIREMENTS:
- 2 to 6 years of software engineering with focus on machine learning and/or computer vision
- True end-to-end experience: taken models from raw data to production and owned post-deployment performance
- Strong software engineering fundamentals plus hands-on ML expertise; comfortable writing production code and training/debugging models
- Full lifecycle ownership: trained, deployed, monitored, and iterated models in production
- Real computer vision depth with judgment to apply classical techniques vs. modern approaches appropriately
