Senior AI Engineer ( Annotation & VLA Models)

CNTXT AI; Abu Dhabi; On-site; AED 240,000 - 320,000; Full time

Job summary

You will design evaluation protocols, run experiments, and produce technical reports. The role requires strong PyTorch skills, experience with LeRobot/openpi/Isaac-GR00T, and a track record in robotics ML; leadership experience is a plus.

Qualifications

• 5+ years of deep learning experience; 3+ years in robot policies or embodied AI.
• Fine-tuning VLA or imitation policies; operate on real robots.
• Strong knowledge of robot imitation learning and RL for robots.
• Expert PyTorch, multi-GPU with mixed precision and LoRA.
• Familiarity with open-source VLA bases (e.g., LeRobot, openpi, Isaac-GR00T).
• Experience with large multimodal datasets, data pipelines and dataset versioning.
• Knowledge of evaluation design: fixed tests, multiple trials, confidence intervals.
• Experience with fast real-time inference: TensorRT, ONNX, quantization.

Responsibilities

• Lead VLA model selection, fine-tuning, and testing with the team.
• Own annotation of robot data; ensure label quality and policy relevance.
• Define evaluation protocols for simulation and real robots; oversee experiments.
• Write technical reports for each experiment and method.
• Collaborate with teams on data capture and annotation requests.
• Mentor junior engineers as the team grows.

Skills

Deep learning

Robot policies

PyTorch

Imitation learning

Reinforcement learning

LoRA

Multi-GPU training

Data annotation

Model fine-tuning

Communication results

Education

PhD in robotics or ML

Tools

LeRobot

openpi

Isaac-GR00T

TensorRT

ROS 2

MuJoCo

Job description

About the team

We build robot learning systems for humanoid robots. We collect teleoperation and egocentric data, label it, and use it to train vision-language-action (VLA) models. We train and test these models in simulation with reinforcement learning (RL), in simulation with human corrections in VR, and on real robots. We have our own annotation model for robot data, and a data catalogue that keeps episodes in the LeRobot format. The core team is small and senior, and it does the technical work together. Our goal is to find gaps in the current state-of-the-art robotics methods. Then we propose improvements to these methods.

The role

Lead the VLA and annotation work of the team, with help from the other engineers. Most of your time goes to the VLA models: with the team, select them, fine-tune them, make them better and test them. You also own the annotation of our robot data. Our annotation model is already in production, so this part of the role is about label quality and about labels that make the policies better, not about building annotation tools from zero.

Use published VLA research to make policies that do useful tasks on our robots. Repeat published experiments with the team and compare the results. Design and test new methods for post-training, data efficiency and inference speed. When the team grows, lead a team in this area.

Responsibilities

VLA models

• With the team, select the base VLA models for our robots and tasks. Compare different models on the same tasks and data. Examples of these models are pi0.5, GR00T N1.7 and Psi0.
• Fine-tune VLA models on teleoperation demonstrations from simulation and from real robots. Use the code base of each model. Examples of code bases are LeRobot, openpi and Isaac-GR00T.
• Do RL post-training of VLA models in simulation, for example with RECAP-style advantage conditioning in BiGym on MuJoCo. Measure each new policy against its baseline.
• Use human corrections to make the policies better. For example, use VR corrections in simulation and HG-DAgger with the RaC rules on real robots.
• Define the evaluation protocol for simulation and for real robots. Use fixed test sets, blind A/B trials and statistical tests, for example Wilson intervals and McNemar tests. Find the causes of failures in rollouts, logs and videos.
• Run the policies on real robots with the robotics team. For example, use the Unitree G1 with Unitree SDK2 and ROS 2.
• Make the inference of the policies fast enough for real-time control. For example, use TensorRT, mixed precision and fewer integration steps in the action expert.
• Measure how many demonstrations are necessary for each task. Test methods that get the same result with fewer demonstrations. For example, use generated data from HumanoidMimicGen.
• Test new research ideas on our robots. Examples of these ideas are cross-embodiment transfer and egocentric human video in pre-training. Help the simulation engineer test world models as policy evaluators.
• Write a technical report on each experiment and on each new method.

Annotation and training data

• Own the annotation model and the annotation QA for our robot data. Work with the annotation team, which does the daily operations.
• Define the label schema for VLA training: language instructions, subtask segmentation, success and failure labels, and hand-pose and action QA.
• Do data ablations to find which data and which labels improve the policies most.
• Use the results of VLA experiments to decide what to label next and what data to capture next. Send these requests to the annotation team and to the data capture team.
• Improve the annotation model where label errors cause policy failures. For example, use vision-language models (VLMs) for automatic labels and human review for the difficult cases.
• Keep the labels versioned in the data catalogue, so that each training run uses a known dataset.

Necessary skills and experience

• 5 or more years of experience with deep learning, and 3 or more years with robot policies or embodied AI.
• Experience with the fine-tuning of a VLA model or an imitation policy, and with the operation of that policy on a real robot. Examples of these policies are pi0, pi0.5, GR00T, OpenVLA, ACT and Diffusion Policy.
• Strong knowledge of robot imitation learning, for example behavior cloning and DAgger-style corrections. Good knowledge of reinforcement learning for robots.
• Expert knowledge of PyTorch. Experience with the fine-tuning of large transformer models on multi-GPU systems, with mixed precision and LoRA.
• Good knowledge of one open-source code base for VLA models, for example LeRobot, openpi or Isaac-GR00T.
• Experience with large multimodal or video datasets for model training: data pipelines, data quality checks and dataset versioning.
• Experience with the design of evaluations for robot policies: fixed test sets, enough trials and confidence intervals.
• Experience with fast inference for real-time control. Examples of tools and methods are TensorRT, ONNX, quantization and mixed precision.
• Evidence of senior work: papers at robotics or machine learning conferences, open-source code that other teams use, or robot systems that went into production.
• Experience with clear explanations of results and risks to engineers and to managers.

Experience that helps

• Experience with annotation of robot or video data: label schema design, annotation QA and agreement between annotators.
• Experience with model-assisted automatic labels, for example episode segmentation and language annotation with VLMs.
• Experience with humanoid robots, dexterous hands and their software, for example the Unitree G1, the Dex3-1 hand, Unitree SDK2 and ROS 2.
• Experience with offline RL or advantage-weighted post-training of VLA models. Examples of these methods are RECAP and AWR.
• Experience with policy evaluation in robot simulation tools, for example MuJoCo, BiGym, Isaac Sim or Isaac Lab.
• Experience with VR teleoperation, for example xr_teleoperate or WiVRn. Experience with human-in-the-loop corrections, for example RaC-style recovery data.
• Experience with humanoid whole-body control, for example NVIDIA GR00T-WholeBodyControl.
• Experience with teleoperation datasets, egocentric human video, cross-embodiment transfer or world models for robots.
• Experience with JAX for openpi, and with inference on NVIDIA Jetson hardware.
• Experience as the lead of a small technical team.
• A PhD in robotics or machine learning, or research experience at the same level.