ML Compression Engineer

You will build the compression technology at the core of The Compression Company. You'll design and train neural compression models, improve compression ratio while preserving quality and downstream utility, and make our training system fast and repeatable so we can produce SOTA codecs for new datasets with minimal manual effort. Your work will directly drive product performance and the pace at which we can ship into new customer environments.

You might be a fit if...

• -You've trained and shipped ML models end-to-end
• -You care about reliability, performance, and clean experimentation
• -You're strong in PyTorch and comfortable owning training + evaluation
• -You enjoy turning research work into usable systems

Sample projects include:

• -Improving rate/quality performance for real customer datasets
• -Building automated training runs that explore model + training settings and select strong candidates
• -Extending our evaluation suite to reflect real user needs, not just offline metrics
• -Improving reproducibility and speed across the training + benchmarking loop

Salary: $180k–$240k Equity: Founding equity with meaningful ownership Full relocation & visa support

The Compression Company is building the next generation of compression: systems that radically reduce the cost of moving and storing high-value, high-volume data without destroying the information people rely on. We create codecs tailored to specific datasets and deployment environments, and ship them as production-ready encoder/decoder artifacts that run on real hardware - from edge devices to cloud GPUs. We're a small, highly technical team with high ownership and a strong bias toward shipping real systems. We care about measurable performance, strong engineering discipline, and building a platform that compounds with every deployment.

If it looks like there may be a fit, we'll start with a short conversation with our CEO. Next you'll have a technical discussion with our CTO focused on your prior work and how you approach hard engineering problems. Finally, we'll invite you onsite to work on a small project and spend time with the team.