Production ML Engineer: Turn Research into Trusted AI

Radical AI; New York (NY); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Employer-covered health insurance

Dental & Vision insurance

Unlimited PTO

401(k)

Company holidays

Mental health support

Job summary

You will own pipelines, experimentation, and the deployment lifecycle, balancing model quality with latency and cost. This is an on-site, hands-on role at the Brooklyn Navy Yard with a strong software engineering foundation.

Qualifications

• 4+ years in ML engineering, research engineering, or related software role.
• Strong Python and software engineering discipline: testing, reviews, packaging, system design.
• Hands-on PyTorch or comparable framework experience: training, evaluation, debugging.
• Experience building data pipelines on cloud with containers and GPU workloads.
• Experience deploying models via services or batch pipelines with observability and cost focus.

Responsibilities

• Turn ML research ideas into production-ready capabilities within the Radical AI platform.
• Build and run training pipelines: datasets, configurations, failure triage, efficiency.
• Make experiments reproducible: track code, data, environments, artifacts.
• Evaluate models for product use with datasets, tests, and failure mode exposure.
• Deploy and integrate models: production-ready components with product/ platform teams.
• Improve inference performance: optimize latency, throughput, memory, cost.
• Own post-launch performance: monitor, rollbacks, and learnings for next iterations.
• Improve team shipping of ML: build shared tooling and automation.

Skills

Machine learning engineering

Python

PyTorch

Data pipelines

Cloud infrastructure

Containers

GPU workloads

Model deployment

Observability

Software engineering discipline

Tools

None

Job description

You will own pipelines, experimentation, and the deployment lifecycle, balancing model quality with latency and cost. This is an on-site, hands-on role at the Brooklyn Navy Yard with a strong software engineering foundation.