Member of Technical Staff – ML Ops

Vinci

. Scale the data generation pipeline from thousands to millions of simulations on 3D parts .

Posted 10/30/2026full-timePalo Alto • California • United StatesLead💰 $185,000 - $245,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and managing production data and machine learning pipelines, with a strong focus on GPU-based systems and simulation workloads. Proficient in Python, data quality controls, and workflow orchestration, ensuring efficient and scalable data generation and processing.

Highest-signal resume keywords

GPU-Based Pipeline ManagementProduction Data Pipeline DevelopmentWorkflow Orchestration FrameworksPyTorch Distributed TrainingInfrastructure as Code

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonData Pipeline DevelopmentGPU ManagementRelational DatabasesNoSQL DatabasesWorkflow OrchestrationPyTorchCUDATerraformAnsible

Tools & Technologies

TemporalRayAirflowKubernetesPrometheusOpenTelemetry

Industry Keywords

Simulation DataCFDFEAEDACADNumerical Solvers

Tech Stack

Tools & technologies

AirflowAnsibleCloudKubernetesNoSQLPrometheusPythonPyTorchRayTerraform

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

5+ years building production data or ML pipelines
At least 2 years owning a GPU-based pipeline at meaningful scale
Experience running large batch or simulation workloads, including partial failures, retries, preemption, and avoiding lost or duplicated work
Strong Python and software engineering fundamentals
Clean abstractions, typed code, and trusted test suites
Experience with relational and NoSQL databases at scale, including schema design, live-data migrations, bulk writes, and query performance on tables with hundreds of millions of rows
Experience with workflow orchestration or distributed execution frameworks such as Temporal, Ray, Airflow, Argo, Kubernetes Jobs, or Slurm
Working knowledge of cloud object storage and high-throughput training file formats
PyTorch distributed training, checkpointing, and experiment tracking
MPI, CUDA or HIP, or operating numerical solvers in production
Infrastructure as code using Terraform or Ansible and private networking for multi-environment deployments
Scientific or simulation data experience, including CFD, FEA, EDA, CAD, or other large numerical or geometric datasets
Parameter sampling or curriculum strategies for training-data coverage
Prometheus, OpenTelemetry, or similar observability stacks
Desire to understand physics simulation and AI sufficiently to connect them

Benefits

Comp & perks

Real impact through deployment on production programs at leading semiconductor companies
Work on a hard, unsolved problem at the intersection of AI, first-principles physics, and enterprise engineering
Work alongside world-class engineers and researchers
Speed and ownership with direct influence on the product and company