ML Engineer, Manipulation

Diligent Robotics

United StatesRemoteFull-time$135–270K/yrPosted 5mo agoVerified open 4 days ago

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At a glance

Compensation

$135–270K/yr

Location

United StatesRemote

Schedule

Full-time

Work Authorization

Not specified

Job overview

Diligent Robotics is hiring a ML Engineer, Manipulation. The team at Diligent Robotics builds AI that enables service robots to collaborate with people in dynamic human environments, focusing on learning‑based manipulation systems that improve robustness, generalization, and safety for real‑world tasks at scale.

Key focus areas include Develop learning‑based manipulation models for end‑to‑end sensor‑driven interaction in dynamic environments, Build and maintain manipulation training pipelines including dataset creation, augmentation, and distributed training, and Design evaluation metrics and regression tests to quantify manipulation reliability, recovery behavior, and safety.

Successful candidates bring Bachelor's Degree In Robotics, Bachelor's Degree In Computer Science, and Bachelor's Degree In Electrical Engineering. Important skills include Robotics, Python, Machine Learning, Learning-Based Manipulation Models, Manipulation Training Pipelines, and Dataset Creation. Preferred (not required): PyTorch, Vision-Language-Action, Behavior Cloning, and Transformer.

Skills & qualifications

RequiredNice to have

Skills

RoboticsPythonMachine LearningPyTorchVision-Language-ActionBehavior CloningTransformerDiffusionSim-to-RealONNXTensorRTQuantizationPerformance ProfilingSafety-Critical IntegrationTransformer DiffusionONNX TensorRTDomain RandomizationSynthetic DataSim‑to‑Real TrainingSafety‑Critical Robotics IntegrationLearning-Based Manipulation ModelsManipulation Training PipelinesDataset CreationAction RepresentationsAugmentationDistributed TrainingEvaluation Metrics DesignRegression TestingSim-to-Real WorkflowsSimulation EnvironmentsFailure-Mode TestingModel OptimizationModel DistillationLatency BenchmarkingMemory Use BenchmarkingStability BenchmarkingField Performance AnalysisFailure Mode IdentificationIterative ImprovementsData CollectionTargeted RetrainingTraining/Evaluation PipelinesSoftware EngineeringCollaborationVision-Language-Action ModelsTransformer/Diffusion Policies for Robotic ControlDevelop Learning-Based Manipulation ModelsBuild and Maintain Manipulation Training PipelinesDesign Evaluation MetricsDevelop Sim-to-Real WorkflowsOptimize and Distill Models for Edge DeploymentBenchmark LatencyBenchmark Memory UseBenchmark Stability on Target HardwarePartner With AI Platform TeamIntegrate Policies With Control and Safety SystemsValidate End-to-End Performance on RobotsAnalyze Field PerformanceIdentify Dominant Failure ModesDrive Iterative Improvements Through Data CollectionApplying ML to Robotics ManipulationApplying ML to Visuomotor ControlApplying ML to Sequential to Sequence ModelsBuilding Reliable Training/Evaluation PipelinesCollaborate Across ML and Robotics TeamsSim-to-Real Training for ManipulationDeploying ML Models to Edge HardwareLearning-Based Manipulation SystemsPerception-to-Action ModelsTraining DatasetsEvaluation ToolingDeployment PipelinesBuilding Training/Evaluation PipelinesIsaac SimMujocoDesigning Fallback/Recovery BehaviorsRegression Tests DesignBenchmarkingDrive Iterative Improvements Through Targeted Retraining

Qualifications

Bachelor's or Master's Degree in Robotics, Computer Science, Electrical Engineering, or Related Field3+ Years of Experience Applying ML to Robotics Manipulation, Visuomotor Control, or Sequential to Sequence ModelsPhD

Full job description

What we’re doing isn’t easy, but nothing worth doing ever is.

We envision a future powered by robots that work seamlessly with human teams. We build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic human environments. Join our mission-driven team as we build out current and future generations of robots.

As an ML Engineer, Manipulation, you will develop and deploy learning-based manipulation systems that enable mobile robots to interact reliably with the physical world in dynamic human environments. You’ll build perception-to-action models, training datasets, evaluation tooling, and deployment pipelines that improve robustness, generalization, and safety for real-world manipulation tasks at scale. Your work will directly impact the robot’s ability to perform complex interactions consistently across real sites with minimal special-case engineering.

Responsibilities

Develop learning-based manipulation models for end to end sensor-driven interaction (e.g., reaching, motion generation, and execution in dynamic environments).
Build and maintain manipulation training pipelines: dataset creation from robot logs/teleop, action representations, augmentation, and distributed training.
Design evaluation metrics and regression tests that quantify manipulation reliability, recovery behavior, and safety in real environments.
Develop sim-to-real workflows for manipulation learning, including simulation environments, domain randomization, and failure-mode testing.
Optimize and distill models for edge deployment; benchmark latency, memory use, and stability on target hardware.
Partner with the AI platform team to integrate policies with control and safety systems, and validate end-to-end performance on robots.
Analyze field performance, identify dominant failure modes, and drive iterative improvements through data collection and targeted retraining.

Basic Qualifications

Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering, or related field (PhD a plus).
3+ years of experience applying ML to robotics manipulation, visuomotor control, or sequential to sequence models.
Strong proficiency in PyTorch and experience building reliable training/evaluation pipelines.
Strong software engineering skills in Python; ability to collaborate across ML and robotics teams.

Preferred Qualifications

Experience with Vision-Language-Action (VLA) models, behavior cloning, and/or transformer/diffusion policies for robotic control.
Experience with sim-to-real training for manipulation (Isaac Sim/Mujoco or similar), including domain randomization and synthetic data.
Experience deploying ML models to edge hardware (ONNX/TensorRT, quantization, performance profiling).
Familiarity with safety-critical robotics integration and designing fallback/recovery behaviors.

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