Machine Learning Engineer

Gatik; Santa Clara (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Gatik seeks a high‑impact Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle stack in Santa Clara, CA. You will collaborate with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient, reliable, and production‑ready for both on‑vehicle and cloud workflows.

This role is onsite 5 days a week at our Santa Clara, CA office, inviting engineers who enjoy building models end‑to‑end—from data

Qualifications

• MS or PhD in CS, ML, robotics, electrical engineering, statistics, or related field.
• Open to all experience levels; leveling based on depth of technical skills.
• Strong Python skills with PyTorch or TensorFlow experience.
• Experience deploying real-time, embedded ML for autonomous systems.
• Experience with model optimization techniques: quantization, pruning, compression.

Responsibilities

• Develop and improve models supporting perception, prediction, planning, and scene understanding.
• Efficient neural network design to meet latency, compute, memory, and power constraints.
• Profile and optimize neural networks using CUDA, TensorRT, and related tech.
• Analyze model performance with simulation and real-world driving data.
• Build scalable ML pipelines for training, evaluation, data processing, and offline inference.
• Develop data workflows for dataset curation, annotation, preprocessing, visualization, and diagnostics.
• Collaborate with autonomy, systems, hardware, and infrastructure teams to integrate ML components.

Skills

Python

PyTorch

TensorFlow

CUDA

Model optimization

Real-time systems

Education

MS or PhD in CS/ML/Robotics/EE

Tools

TensorRT

Keras

Azure

Job description

Weareseekingahigh-impact,technicallydeepMachineLearningEngineertodevelop,optimize,anddeployproductionMLmodelsacrossourautonomousvehicle(AV)stack.Thisroleisidealforengineerswhoenjoybuildingmodelsend-to-end-fromdataandtrainingthroughoptimizationandreal-timedeploymentonautonomousvehicles.

You will work closely with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient, scalable, reliable, and production-ready for both on-vehicle and cloud workflows.

This role is onsite 5 days a week at our Santa Clara, CA office!

Whatyou'lldo

• AutonomousDrivingModels:Developandimprovemodelssupportingperception,prediction,planning,andsceneunderstanding.
• EfficientNeuralNetworkDesign:Optimizemodelsusingtechniquessuchasquantization,pruning,sparsification,compression,andefficientarchitecturedesigntomeetstrictlatency,compute,memory,andpowerconstraints.
• ModelOptimization:ProfileandoptimizeneuralnetworksusingCUDA,TensorRT,andrelatedtechnologies.
• SimulationandEvaluation:Analyzemodelperformanceusingsimulationandreal-worlddrivingdata,identifyfailuremodes,anddriveimprovements.
• ScalableMLInfrastructure:Buildhigh-throughputpipelinesfortraining,evaluation,dataprocessing,andlarge-scaleofflineinference.
• DataWorkflowsandTooling:Developreliablepipelinesfordatasetcuration,annotation,preprocessing,visualization,diagnostics,benchmarking,andcontinuousfeedbackfromfielddata.
• Cross-FunctionalIntegration:Partnerwithautonomy,systems,hardware,andinfrastructureteamstoensureMLcomponentsintegratereliablyintothebroadervehicleplatform.

Whatwe'relookingfor

• Education:MSorPhDinComputerScience,MachineLearning,Robotics,ElectricalEngineering,Statistics,Optimization,orarelatedfield.
• Experience:Opentoallexperiencelevels.Levelingwillbedeterminedbasedonexperienceandtechnicaldepth.
• Programming&Frameworks:
• StrongPythonskillsandexperiencewithframeworkssuchasPyTorchorTensorFlow.
• CoreML&SystemsExpertise:
• Experiencedeployingandoptimizingneuralnetworksforreal-time,embedded,robotics,autonomousdriving,orotherperformance-constrainedsystems.
• Experiencewithmodeloptimizationtechniquessuchasquantization,pruning,compression,andefficientarchitectures.
• Experiencewithsoftwarearchitecture,profiling,latencyoptimization,system-leveldebugging,anddataflowanalysis.
• Infrastructure&ComputeTools:
• ExperiencewithCUDAandTensorRTishighlydesirable.
• Experiencewithcloud-basedMLtrainingandevaluationpipelines,preferablyAzure.

BonusQualifications:

• Experiencewithtransformers,multimodalmodels,diffusionmodels,worldmodels,orend-to-enddrivingmodelsisaplus.
• Experienceinautonomousdriving,robotics,orothersafety-criticalreal-timeMLsystemsisstronglypreferred.
• PublicationsordemonstratedtechnicalcontributionsinefficientML,autonomousdriving,robotics,orrelatedareasareaplus.
• Priorcontributionstolarge-scaleMLsystemsdeployedinproduction.