Senior Machine Learning Engineer

About Oway

Freight is the circulatory system of the global economy. Every product on every shelf, every component in every factory, every meal in every restaurant, moved on a truck to get there. And for decades, that system has been running at half capacity. Every year, up to 50% of US truck space goes unused, a $100B+ annual inefficiency that acts as a hidden tax on everything Americans buy, sell, and manufacture. It slows reindustrialization, inflates goods prices, suppresses carrier earnings, and makes US supply chains more fragile than they need to be.

Oway is fixing it. We're an industrial automation and research company building AI systems that automatically price, match, and sell unused freight capacity in real time, turning dead miles into productive ones and giving businesses access to dramatically faster, cheaper distribution than anything that's existed before. When freight moves more efficiently, goods cost less, manufacturers can compete more aggressively, and the carriers who are the backbone of the US economy earn more for the work they already do.

At the center of this is Juno, our AI-native coordination engine. Autonomous freight coordination at scale is one of the hardest real-world AI problems being worked on today: it requires reasoning over live telemetry from thousands of vehicles, pricing under uncertainty, handling edge cases that no rulebook anticipated, and doing all of it in real time without human intervention. Juno does this. And as it improves, it compounds. Each solved edge case, each routing insight, each pricing calibration makes the entire network smarter. The data moat Oway is building through Juno isn't just defensible in logistics; it's the kind of industrial intelligence that could underpin US competitiveness across physical industries for decades.

We're backed by YC, General Catalyst, and leading investors in AI, industrials, and automation, and we're just getting started.

About This Role

At the heart of Oway's platform is Juno, our AI-native coordination engine that automates large-scale supply chain coordination with native edge-case intelligence. As a Senior ML Engineer, you'll own both the systems that power Juno and the underlying data infrastructure that makes them possible. You'll work directly alongside our engineering leads to build, train, and ship the models that determine how freight is priced, routed, and dispatched across thousands of live vehicles nationwide.

This is a high-ownership, production-first role. The work you do here directly impacts carrier margins, shipper costs, and the efficiency of America's freight network.

What You'll Do

• Own the development, training, and productionization of the ML systems powering Oway's pricing, routing, and matching engine, including our optimization models.
• Build and iterate on Juno AI, our autonomous LLM-based dispatch agent, including reinforcement learning workflows, prompt engineering, and edge-case handling in live operations.
• Design and maintain the data infrastructure and pipelines that feed our ML systems, from ELD telemetry ingestion to feature engineering, model monitoring, and observability.
• Collaborate closely with engineering, operations, and carrier teams to translate real-world freight problems into reliable, scalable ML solutions.
• Instrument model performance in production using LangFuse and similar tools, and iterate rapidly based on live data.
• Contribute to ML architecture decisions as an early senior hire with significant ownership.

Who You Are

• 4-7 years of experience in ML engineering with a strong track record of shipping models to production.
• Proficient in Python, with hands-on experience across the ML stack, from pipeline design to model serving.
• Experience with tree-based or ensemble methods (e.g. Cubist, XGBoost) and/or LLM fine-tuning and RLHF workflows.
• Strong understanding of data pipeline design, including ETL, feature stores, streaming ingestion, or similar.
• Energized by unstructured problems in physical systems, and motivated by real-world impact over research novelty.
• Bonus: experience in logistics, routing, geospatial ML, or agent-based systems.
• Bonus: familiarity with LangChain, LangFuse, or similar LLM observability tooling.

About the Interview

We run a three-step process:

1. Meet Phillip, the founder of Oway.
2. Meet our engineering leads to get to know the team.
3. Technical evaluation, details disclosed after step 2.

Oway was built by non-conventional thinkers who value diverse backgrounds and creative problem-solving. If you don't check every box, we still encourage you to apply.