AI role · Open offer

IoT Engineer — edge ML & industrial telemetry

GE Vernova

Full-time
Senior
Edge & IoT AI
Hybrid
Boston, MA, United States · Hybrid
Boston, United States
USD 130k–175k + bonus

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.

3 skills2 tools

At a glance

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

Employment: Full-time
Workplace: Hybrid
Location: Boston, MA, United States · Hybrid
Compensation: USD 130k–175k + bonus
Experience level: Senior
Team: Edge & IoT AI
Benefits & perks

About this role

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

GE Vernova is hiring a Senior IoT Engineer — edge ML & industrial telemetry on the Edge & IoT AI team (Hybrid · Boston, United States). This is a hands-on role in robotics & industrial AI—not a generic “AI enthusiast” post.

You will own edge deployment, safety constraints, and physical systems 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.

GE Vernova connects industrial assets with AI for energy and grid reliability.

IoT Engineer (hybrid Boston): edge ML, telemetry pipelines, and predictive maintenance for industrial IoT careers.

Missions

Deploy edge inference with constrained compute and reliable sync.
Build anomaly detection on industrial time series.

Stack & environment

Python, AWS IoT stacks, Docker, SQL.

What we look for

IoT or industrial ML; eligible to work in the US.

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.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.
Machine Learning

Tools & stack

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

AWSTool
Regular use of AWS in the robotics & industrial AI stack—configuration, debugging, and team conventions.
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: Edge inference, MQTT/telemetry, or industrial IoT ML.
Several of these show up in recent shipped work—not only on your CV: Python, SQL, Machine Learning, AWS, and Docker.
You have owned edge deployment, safety constraints, and physical systems in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Boston, 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: USD 130k–175k + bonus, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Edge inference, MQTT/telemetry, or industrial IoT ML.

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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