Production ML Engineer — Build & Deploy Models On-site

Nodi; New York (NY); On-site; USD 190,000 - 245,000; Full time

Benefits offered by this job

100% employer-covered medical, dental,

unlimited PTO

14+ company holidays

2 weeks of company shutdown at EOY

401(k)

mental health support

Job summary

Radical AI in Brooklyn Navy Yard is seeking a generalist Machine Learning Engineer to turn ML research into production-ready capabilities inside our platform that runs our autonomous lab. A model that can't be trained reliably, served efficiently, and trusted by the scientists using it doesn't count.

Your job is to make sure it does. This hands-on role spans the full ML lifecycle, from debugging training runs to deploying services, with a focus on reliability, latency, and cost while maintaining

Qualifications

• 4+ years in ML engineering, research engineering, or a closely related software role.
• Strong Python and software engineering discipline: testing, packaging, and maintainable design.
• Hands-on experience with PyTorch: training, evaluation, and debugging models.
• Experience building data pipelines on cloud infrastructure with GPUs.

Responsibilities

• Turn research ideas into working implementations and surface constraints early.
• Build training pipelines, datasets, configurations, failure triage, and compute efficiency.
• Make experiments reproducible by tracking code, data, environments, and artifacts.
• Evaluate models for product use with data-driven decisions on readiness to ship.
• Deploy and integrate models with services, ensuring observability and cost awareness.
• Improve inference performance and partner with product and platform teams.

Skills

Python

PyTorch

ML production

Training pipelines

Cloud data pipelines

Tools

Containers

GPU workloads

Experiment tracking

Model deployment

Job description

Radical AI in Brooklyn Navy Yard is seeking a generalist Machine Learning Engineer to turn ML research into production-ready capabilities inside our platform that runs our autonomous lab. A model that can't be trained reliably, served efficiently, and trusted by the scientists using it doesn't count.

Your job is to make sure it does. This hands-on role spans the full ML lifecycle, from debugging training runs to deploying services, with a focus on reliability, latency, and cost while maintaining