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AI role · Open offer

Senior ML Platform Engineer

Modal

Full-time
Senior
Serverless ML
Hybrid
169 Madison Avenue, New York, NY 10016, United States · Hybrid
New York, United States
$170k–$225k + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 169 Madison Avenue, New York, NY 10016, United States · Hybrid
Compensation: $170k–$225k + equity
Experience level: Senior
Team: Serverless ML
Benefits & perks: Equity, NYC hub, GPU credits.
How to apply: Serverless GPU workloads or batch inference platform experience.
Company site: modal.com
Listed: Jul 26, 2026
Updated: Aug 3, 2026

About this role

What the team is looking for and how this position fits.

Modal is hiring a Senior Senior ML Platform Engineer on the Serverless ML team (Hybrid · New York, United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Modal provides serverless GPU infrastructure for AI teams. Platform engineers make it trivial to run training, batch jobs, and inference at scale.

Senior ML Platform Engineer (NYC): schedulers, cold-start optimisation, and developer experience for Python-first ML workflows.

Hybrid NYC — equity and generous GPU credits for personal projects.

What you will do

Improve container scheduling, autoscaling, and GPU utilisation.
Build SDK ergonomics for LangChain, PyTorch, and batch inference.
Partner with customers running large fine-tuning and eval workloads.
Own reliability SLOs for multi-tenant GPU clusters.

Stack & environment

Python, Kubernetes, AWS, Docker.
Hybrid Madison Avenue NYC.

What we look for

ML platform or infra engineering with GPU workloads.
Strong Python and systems debugging.
US work authorization.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
Kubernetes
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
AWS
Cloud primitives for training, storage, IAM, and cost-aware LLM & generative AI deployments.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Serverless GPU workloads or batch inference platform experience.
Several of these show up in recent shipped work—not only on your CV: Python, Kubernetes, AWS, and Docker.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · New York, United States)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: $170k–$225k + equity, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Serverless GPU workloads or batch inference platform experience.

Sign in with a candidate account to apply

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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