Senior Machine Learning Systems Engineer at Reddit

Confirmed open

Reddit is hiring a US-remote Senior ML Infrastructure Engineer to build MLOps, graph-ML and model platforms for recommendations and discovery.

Reddit

Location: Remote
Work arrangement: remote
Technologies: Python, GCP, Kubernetes, PyTorch, TensorFlow

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Role overview

Reddit’s Machine Learning Platform team owns infrastructure powering recommendations, content discovery and user/content quantification. This Senior ML Infrastructure Engineer role builds platforms and lifecycle patterns for large-scale models.

Responsibilities

Design end-to-end MLOps patterns for data preparation, model management and experiment tracking.
Build and support graph-ML codebases and platforms that abstract common patterns and improve scalability.
Partner with ML engineers on training performance, efficiency and GPU cost optimisation.
Develop reliable production infrastructure for model serving, monitoring and iteration.
Improve developer velocity, tooling, documentation and operational practices for ML teams.
Collaborate with Growth, Ads, Feeds and Core ML teams on shared platform capabilities.

Qualifications

Strong machine-learning systems or infrastructure engineering experience.
Experience designing MLOps, model lifecycle, training or serving platforms at scale.
Knowledge of distributed systems, data pipelines, GPUs, performance tuning and production operations.
Ability to build reusable abstractions and work with ML engineers on ambiguous platform problems.
Strong programming, communication and cross-functional collaboration skills.

Technology in this role

Python: Python GCP Kubernetes PyTorch .
GCP: Python GCP Kubernetes PyTorch .
Kubernetes: Python GCP Kubernetes PyTorch .
PyTorch: Python GCP Kubernetes PyTorch .
TensorFlow: Python GCP Kubernetes PyTorch .
Spark: • Optimize batch data processing within a data warehouse and with tools such as Apache Beam, Apache Spark, Ray Data, and .
Google Cloud: • Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and .
Terraform: • Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and .
BigQuery: • Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This Senior Machine Learning Systems Engineer position at Reddit is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “Reddit’s Machine Learning Platform team owns infrastructure powering recommendations, content discovery and user/content quantification. This Senior ML Infrastructure Engineer role builds platforms and lifecycle…” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the AI and data area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Design end-to-end MLOps patterns for data preparation, model management and experiment tracking.”; “Build and support graph-ML codebases and platforms that abstract common patterns and improve scalability.”; “Partner with ML engineers on training performance, efficiency and GPU cost optimisation.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions from the group’s contribution. Avoid presenting a technology keyword as proof by itself: connect it to the problem you solved, the scale or complexity involved, and what changed after your work. The source may contain additional duties; review it directly before preparing your application.

How to read the requirements. The listed qualifications include “Strong machine-learning systems or infrastructure engineering experience.”; “Experience designing MLOps, model lifecycle, training or serving platforms at scale.”; “Knowledge of distributed systems, data pipelines, GPUs, performance tuning and production operations.”. Mark each requirement as demonstrated, adjacent experience or a genuine gap, then use a concise example for the first two categories. A strong example explains what you personally owned, how you made trade-offs, how you validated the result and what you learned. If the posting specifies years of experience, language ability, certifications, location or eligibility, address those facts plainly in your CV or application rather than relying on the employer to infer them. Do not claim a qualification from exposure alone. For a requirement you do not meet, describe relevant transferable experience accurately and ask the recruiter whether it is mandatory.

Technology and working evidence. The technology terms associated with this listing are Python, GCP, Kubernetes, PyTorch, TensorFlow. Treat this as a set of posting-derived signals, not a guarantee that every tool is used daily or that the list is complete. For each term relevant to your experience, prepare one concrete example: the system or workflow, the part you owned, the alternatives considered, and how you tested reliability, security, performance or.

Benefits

US-remote work under Reddit’s country and physical-presence rules.
Benefits and leave programmes supporting health, wellbeing and family needs.
Opportunity to build shared ML infrastructure used across recommendations, discovery and Ads.

About Reddit

Reddit is a community platform. Its Machine Learning Platform team provides infrastructure for recommendations, discovery and other ML organisations.

Employer website

How to apply

Review the original posting and apply through the employer’s careers page.

View source and apply

Source checked: 2026-10-09

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