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Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging)

Midjourney

San Francisco, CAHybridFull-timeNo compensation foundPosted 1mo agoVerified open 6 days ago
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At a glance

Compensation
No compensation found
Location
San Francisco, CAHybrid
Schedule
Full-time
Work Authorization
Not specified

Job overview

Midjourney is hiring a Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging). The Principal Machine Learning Engineer will collaborate with medical image reconstruction scientists to develop ML components that enhance reconstruction quality, speed, robustness, and quantitative accuracy. This role involves defining training and evaluation pipelines, productionizing models for performance and reproducibility, and contributing to hybrid algorithms that integrate physics and learned priors. The engineer will also help build tooling for rapid experimentation and rigorous verification of algorithm changes.

Key focus areas include Partner with medical image reconstruction scientists/engineers to build ML components, Define training/evaluation pipelines, datasets, and metrics, and Productionize models for inference performance, reproducibility, and monitoring.

Important skills include ML Components, Define Training/Evaluation Pipelines, Productionize Models, Hybrid Algorithms, Build Tooling For Rapid Experimentation, and Strong Applied ML Experience. Preferred (not required): GPU Performance Constraints, Pragmatic Production Mindset, Reproducible Training/Inference, and Regression Testing.

Skills & qualifications

RequiredNice to have

Skills

ML ComponentsDefine Training/Evaluation PipelinesProductionize ModelsHybrid AlgorithmsBuild Tooling for Rapid ExperimentationStrong Applied ML ExperienceSignal ProcessingImagingMove Fluidly Between Research Prototypes and Production-Quality SystemsStrong Evaluation DisciplineGPU Performance ConstraintsPragmatic Production MindsetReproducible Training/InferenceRegression TestingSafe Deployment in High-Stakes ContextsComputational PhysicsScientific ComputingML-Based Methods Such as PiNNs and Neural OperatorsSolve Partial Differential EquationsAgentic-SciMLData Curation for MLBuilding Datasets From Messy, Real-World SourcesDefining Ground TruthManaging Labeling or Simulation PipelinesData AssimilationKalman FilteringVariational MethodsEnsemble Approaches

Qualifications

Demonstrated Track Record of Applying ML to Physics-Based or Inverse Problems

Full job description

WHAT YOU’LL DO

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

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

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

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

  5. 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).

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