# Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging) at Midjourney

Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging) at Midjourney in San Francisco and 1 more. On-site, full-time, paying $290k–$302k. Posted June 30, 2026.

- Company: Midjourney
- Location: San Francisco and 1 more
- Workplace: On-site
- Job type: Full-time
- Pay: $290k–$302k
- Posted: 2026-06-30
- Apply: https://jobs.ashbyhq.com/midjourney/9d54d241-0824-405a-bc8a-a64483e67cee/application
- Source: https://jobmagic.ai/jobs/principal-machine-learning-engineer-reconstruction-quantitative-imaging-midjourn-jf92ww

## Requirements

- Strong applied ML experience plus comfort with signal processing / imaging or adjacent domains.
- Ability to move fluidly between research prototypes and production-quality systems.
- Strong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility.
- A demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.)

## Description

### What you’ll do

- Partner with medical image reconstruction scientists / engineers to build ML components that improve reconstruction quality, speed, robustness, or quantitative accuracy.

- Define training/evaluation pipelines, datasets, and metrics that map to user needs and design requirements.

- Productionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks.

- Collaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation.

- Help build tooling for rapid experimentation as well as rigorous verification of algorithm changes.

### What we’re looking for

- Strong applied ML experience plus comfort with signal processing / imaging or adjacent domains.

- Ability to move fluidly between research prototypes and production-quality systems.

- Strong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility.

- A demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.)

### Useful experience

- ML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints.

- Pragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts.

- A background in computational physics or scientific computing.

- Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging.

- Experience in Agentic-SciML is a plus.

- Hands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines.

- Background in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned variants).