Catapult Solutions Group
Machine Learning Engineer (Hybrid – Mountain View, 6-Month Contract)
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer in Mountain View, CA, on a 6-month contract with a pay rate of "TBD." Key skills include 2+ years in ML ops, strong Python, PySpark, Git, and cloud experience with AWS.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
448
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🗓️ - Date
October 3, 2025
🕒 - Duration
More than 6 months
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🏝️ - Location
Hybrid
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Mountain View, CA
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🧠 - Skills detailed
#Security #Batch #Linux #ML (Machine Learning) #Code Reviews #GitHub #Data Engineering #Java #C++ #Kubernetes #ML Ops (Machine Learning Operations) #SageMaker #GIT #Databricks #Python #Cloud #AWS (Amazon Web Services) #PySpark #AI (Artificial Intelligence) #Deployment #Spark (Apache Spark)
Role description
Location: Mountain View, CA (Hybrid – 3 days onsite, 2 remote)
Length: 6 months (through March 2026, potential extension/convert)
We’re looking for a Machine Learning Engineer (MLE) to join our AI engineering team. This role is focused on operational excellence — maintaining and redeploying ML models already in production, not building from scratch.
What You’ll Do
• Maintain 10–20 production ML pipelines
• Redeploy and upgrade models (security & CVEs)
• Manage GitHub PRs and collaborate in code reviews
• Monitor real-time and batch pipelines, ensure alerts are in place
• Support on-call incident handling and fixes
What You’ll Need
• 2+ years industry experience in ML ops/backend
• Strong Python, PySpark/Spark, Git/GitHub
• Hands-on Linux & Kubernetes (deployments)
• Cloud experience with AWS & Databricks
• Familiarity with ML lifecycle (train, deploy, inference)
Nice to Have: Kubeflow, SageMaker, data engineering background, Java/C++.
If you thrive in production ML environments and want to strengthen your engineering skillset, we’d love to connect.
Location: Mountain View, CA (Hybrid – 3 days onsite, 2 remote)
Length: 6 months (through March 2026, potential extension/convert)
We’re looking for a Machine Learning Engineer (MLE) to join our AI engineering team. This role is focused on operational excellence — maintaining and redeploying ML models already in production, not building from scratch.
What You’ll Do
• Maintain 10–20 production ML pipelines
• Redeploy and upgrade models (security & CVEs)
• Manage GitHub PRs and collaborate in code reviews
• Monitor real-time and batch pipelines, ensure alerts are in place
• Support on-call incident handling and fixes
What You’ll Need
• 2+ years industry experience in ML ops/backend
• Strong Python, PySpark/Spark, Git/GitHub
• Hands-on Linux & Kubernetes (deployments)
• Cloud experience with AWS & Databricks
• Familiarity with ML lifecycle (train, deploy, inference)
Nice to Have: Kubeflow, SageMaker, data engineering background, Java/C++.
If you thrive in production ML environments and want to strengthen your engineering skillset, we’d love to connect.
