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Software Engineer, ML Infrastructure

Exclusive opportunity

Onsite

Software Engineer, ML Infrastructure

Value Driven Solutions

Software Engineer, ML Infrastructure

Skills

RustPythonPyTorchTensorFlowMachine LearningKubernetesApache Airflow

1 day ago

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Important information


Contract type:

Permanent

Salary:

$166,000 – $220,000 / year

Location:

Costa Mesa, CA, USA

Work mode:

Onsite

Published on:

19 October 2026

What they need


Costa Mesa, CA

Full Time

Manager/Supervisor

Type: Direct hire, full-time · Base: $166,000–$220,000 + equity · Clearance: U.S. Person required; Top Secret eligibility desirable

VDS is recruiting an ML infrastructure engineer to build the training and data platform behind autonomous perception systems used across a defense-technology company.

What you'll do

  • Build and run training and dataset infrastructure, including multi-sensor data indexing.
  • Improve tools and SDKs used by perception engineers.
  • Use frontier models for data curation, labeling, synthetic data and search.
  • Track training jobs, evaluations and model versions.
  • Partner with engineering teams in each business line.

What you bring

  • Strong modern Python; some Rust (PyO3) is a plus.
  • Distributed training in TensorFlow or PyTorch.
  • Kubernetes and workflow orchestration (Flyte, Airflow or Dagster).

Nice to have

  • Experiment tracking with Weights & Biases or MLflow.

Senior Machine Learning Engineer, Core Development (Verify)

Type: Direct hire, full-time · Base: [confirm range] + equity · Clearance: [confirm; U.S. Person likely]

VDS is recruiting a senior ML engineer to build core machine-learning capabilities shared across a defense-technology company's autonomy products.

What you'll do

  • Develop and ship models used across multiple product lines.
  • Build reusable ML components, pipelines and evaluation tools.
  • Work with autonomy, perception and platform teams.

What you bring

  • [confirm years] years of production ML engineering.
  • Python and PyTorch or TensorFlow; experience deploying models at scale.

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