Do I need hardware experience for this seat?

Some. You do not design actuators, but you must understand sensors, latency, and why a model that is right in simulation can be wrong on the floor. Teams hire software engineers who respect the physics, not only roboticists.

What is the fastest way in from a software background?

Perception and simulation. Computer vision depth plus one sim to real project you can demo is the most common bridge from ordinary ML work into an embodied AI team.

Robotics AI Engineer, embodied systems edition

Evaluation and engineering, a senior role

What does Robotics AI Engineer do?

Puts models on machines that move: perception stacks, learned control, sim to real pipelines, and the evaluation that proves a robot is ready for the environment it will work in.

What it decides: Which model goes on which machine, what simulation evidence is enough, and when a behavior is ready to leave the lab.

At physical AI employers: 39 postings match this seat as of September 10, 2026, counted weekly by the Physical AI Hiring Index.

Where the jobs are Interview questions Check my readiness

The competencies employers name

Embodied AI and robotics systemscore, depth expected
Understands AI that acts in the physical world: perception stacks, vision language action models, sim to real transfer, sensor fusion, and where each of them fails.
10 graded topics teach this
Applied AI engineeringrequired, working knowledge
Builds production systems on foundation models: retrieval augmented generation, structured tool use, evaluation harnesses, guardrails, and cost and latency budgets.
4 graded topics teach this
AI evaluation and testing designrequired, working knowledge
Designs tests for factuality, robustness, fairness, safety and abuse resistance with rubrics, baselines and thresholds, and says what a score misses.
19 graded topics teach this
Physical AI safety assurancerequired, working knowledge
Assures AI systems that can cause physical harm: hazard analysis, safety cases, runtime monitors, and the standards evidence a regulator or a court will ask for.
5 graded topics teach this
Post-deployment monitoring and drift detectionpreferred, working knowledge
Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.
14 graded topics teach this
How models work, at a governance depthpreferred, working knowledge
Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.
20 graded topics teach this

Where it is taught

Counted from the graded topics that teach this role's competencies. Your own path is shorter: it skips what you already cover.

Certified AI Practitioner: Workplace Foundations19 topics
Certified Agentic AI Governance Professional (CAAGP)14 topics
The AI Lobbyist: Certified AI Policy Strategist10 topics
EU AI Act Implementation Expert9 topics
Certified AI Governance Professional (CAIGP)8 topics
Certified AI Transformation Professional (CATP)4 topics
Certified AI Data Governance Professional (CADGP)4 topics

Check your readiness for this role

The role you want

What you already have: your background and your CV (both optional, both count)

Where you are coming fromA background carries competencies before a single lesson. They count as described or shown, never as proven.Your CV or resume

Attach a PDF, Word or text fileor paste the text below

Not stored, file or text. Read in this request, then gone. A named credential counts more than a described skill, and a work product counts more than a sentence.

Signed in? Every topic you have passed already counts as proof.

Roles that feed into it

Robotics Engineer
Controls Engineer
Computer Vision Engineer
Mechatronics Engineer
Machine Learning Engineer

Where it leads

Physical AI Safety Engineer, autonomous systems edition
Robotics AI Lead

What postings tend to name

Frameworks: ROS 2, NVIDIA Isaac Sim, ISO 10218.

Questions

Do I need hardware experience for this seat?: Some. You do not design actuators, but you must understand sensors, latency, and why a model that is right in simulation can be wrong on the floor. Teams hire software engineers who respect the physics, not only roboticists.
What is the fastest way in from a software background?: Perception and simulation. Computer vision depth plus one sim to real project you can demo is the most common bridge from ordinary ML work into an embodied AI team.