Machine Learning / Robotics Engineer — Simulation

Marvik; Buenos Aires; On-site; ARS 1,200,000 - 2,100,000; Full time

Job summary

Marvik is seeking a Machine Learning or Robotics Engineer with strong simulation experience to build AI solutions that interact with the physical world. You will work across ML, perception, robotics, and simulated environments, contributing to multiple industries with hands-on engineering.

You will develop and test models in simulated environments, build data pipelines, and integrate algorithms with sensors and hardware as required.

Qualifications

• Professional experience in machine learning engineering, robotics, or a closely related field.
• Hands-on experience with simulation platforms (e.g., Isaac Sim, MuJoCo, Gazebo, PyBullet).
• Strong Python programming skills and solid software engineering fundamentals.
• Experience developing and evaluating ML models or robotics algorithms with validation metrics.
• Familiarity with sensor modeling, coordinates, kinematics, dynamics, or physics-based simulation.
• Proficiency with Linux, Git, and collaborative workflows.
• Ability to navigate open-ended problems, communicate trade-offs, and take ownership of implementation and validation.

Responsibilities

• Develop and evaluate ML and robotics solutions from experiments to production.
• Create and use simulated environments to test algorithms and generate data.
• Build data pipelines for collection, processing, training, and evaluation.
• Integrate models with sensors, software, and hardware as needed.
• Design experiments to assess performance and transfer from simulation to real-world.

Skills

ML engineering

Robotics

Python programming

systems thinking

open-ended problem solving

Tools

NVIDIA Isaac Sim

Isaac Lab

MuJoCo

Gazebo

PyBullet

Job description

At Marvik, we’re looking for a Machine Learning or Robotics Engineer with experience in simulation to help us build AI solutions that interact with the physical world.

You’ll work on projects combining machine learning, perception, robotics, and simulated environments. We’re looking for a strong engineering foundation, hands‑on simulation experience, and the flexibility to contribute across different industries and technical challenges as our projects evolve.

What you'll do:

• Develop and evaluate machine learning and robotics solutions, from early experimentation to production integration.
• Build and use simulated environments to test algorithms, generate synthetic data, and evaluate system behavior.
• Develop pipelines for data collection, processing, training, and evaluation.
• Integrate models with sensors, software systems, and hardware when required.
• Design experiments to assess performance, understand failure modes, and measure how well simulation results transfer to real‑world conditions.
• Collaborate with engineers, researchers, and clients to translate project requirements into practical solutions.
• Write maintainable code and contribute reusable tools and engineering practices across projects.

What we’re looking for:

• Professional experience in machine learning engineering, robotics, or a closely related field.
• Hands‑on experience with a simulation platform such as NVIDIA Isaac Sim, Isaac Lab, MuJoCo, Gazebo, PyBullet, or a comparable tool.
• Strong Python programming skills and solid software engineering fundamentals.
• Experience developing and evaluating ML models or robotics algorithms, with a clear understanding of validation and performance metrics.
• Familiarity with relevant foundations such as sensor modeling, coordinate transformations, kinematics, dynamics, or physics‑based simulation.
• Comfort working with Linux, Git, and collaborative development workflows.
• Ability to navigate open‑ended problems, communicate trade‑offs, and take ownership of implementation and validation.

We welcome both ML engineers who have used simulation for model development and robotics engineers with experience integrating machine learning. Depth in one area and the ability to work across disciplines matter more than expertise in every part of the stack.

It’s a major plus:

• Computer vision, 3D perception, or multimodal learning.
• Synthetic data generation, domain randomization, or sim‑to‑real evaluation.
• Reinforcement learning, imitation learning, motion planning, or control.
• ROS / ROS 2 and integration with physical sensors or robots.
• Deployment on edge devices such as NVIDIA Jetson.
• C++, GPU optimization, Docker, or cloud infrastructure.
• Experience bringing prototypes into reliable production systems.