ML Platform Engineer

tvScientific

ML Platform Engineer at tvScientific driving next-gen ML and measurement infrastructure. Working on decision-making processes, observability, and Kubernetes deployments.

Posted 4/23/2026full-timeRemote • California • 🇺🇸 United StatesMid-LevelSenior💰 $123,696 - $254,667 per yearWebsite

Tech Stack

Tools & technologies

KubernetesLinuxPythonScalaTerraform

About the role

Key responsibilities & impact

Scale the decision making process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments
Improve the developer experience for the data science team
Upgrade our observability tooling
Make every deployment smooth as our infrastructure evolves.

Requirements

What you’ll need

Deep understanding of Linux
Excellent writing skills
A systems-oriented mindset
Experience in high-performance software (RTB, HFT, etc.)
Software engineering experience + reliability (e.g. CI/CD) expertise
Strong observability instincts
Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables
Nice-To-Haves
Reverse-engineering experience
Terraform, EKS, or MLOps experience
Python, Scala, or Zig experience
NixOS experience
Adtech or CTV experience
Experience deploying a distributed system across multiple clouds
Experience in hard real-time low-latency (<10 ms) environments

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account tvScientific Website LinkedIn All Job Openings 51 - 200 employees Founded 2020 📱 Media ☁️ SaaS 🤝 B2B Media
SaaS
B2B tvScientific is a performance-focused Connected TV (CTV) advertising platform that uses AI-powered optimization to deliver measurable business outcomes like installs, sales, and traffic. The platform provides inventory access, targeting, creative management, measurement & attribution, and real-time reporting with a unified dashboard and transparent analytics. tvScientific positions itself as a pay-for-performance solution for consumer brands, offering guaranteed outcomes, brand safety controls, and integrations for scale. ML Platform Engineer Job not on LinkedIn 🔥 11 minutes ago 🏄 California – Remote 💵 $123.7k - $254.7k / year ⏰ Full Time 🟡 Mid-level 🟠 Senior 🏗️ Platform Engineer Kubernetes Linux Python Scala Terraform Apply Now Find Hiring Managers Customize resume for this job Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Scale the decision making process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments
Improve the developer experience for the data science team
Upgrade our observability tooling
Make every deployment smooth as our infrastructure evolves. 🎯 Requirements
Deep understanding of Linux
Excellent writing skills
A systems-oriented mindset
Experience in high-performance software (RTB, HFT, etc.)
Software engineering experience + reliability (e.g. CI/CD) expertise
Strong observability instincts
Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables
Nice-To-Haves
Reverse-engineering experience
Terraform, EKS, or MLOps experience
Python, Scala, or Zig experience
NixOS experience
Adtech or CTV experience
Experience deploying a distributed system across multiple clouds
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Hard Skills & Tools

LinuxCI/CDPythonScalaZigTerraformEKSMLOpsreverse-engineeringdistributed systems

Soft Skills

writing skillssystems-oriented mindsetobservability instinctscritical evaluationverificationintegrityownershipaccountability