Vinci

Machine Learning Engineer

• Scale the data generation pipeline from thousands to millions of simulations on 3D parts
• Own dataset lineage, reproducibility, and versioned simulation recipes
• Build data-quality controls, including acceptance criteria, bad-sample flagging, coverage and distribution checks, and per-field statistics
• Extend the GPU job platform using Temporal as the durable control plane and Ray as the execution plane
• Manage priority, admission, cancellation, and isolated per-job environments as generation, training, and validation share compute
• Own training-job launch with Ray Train and torchrun
• Manage checkpoints, the model registry, and reintegration of trained checkpoints into generation or production inference pipelines
• Collaborate with the infrastructure team to align compute and data resources with workload needs
• Measure and optimize generation, storage, and serving costs by physics type and configuration
• Identify GPU waste and evaluate the value of future data generation
• Write tests, maintain fast CI, create safe schema migrations, and document systems for operational handoff
• Write, test, and ship production code hands-on

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Member of Technical Staff – ML Ops