Machine Learning Engineer (Scenes)

As a Machine Learning Engineer in DRV, you will bridge the gap between cutting-edge machine learning and real-world vehicle performance, engineering the validation and onboard systems that ensure the autonomous vehicle reliably executes intelligent and predictable maneuversOwn ML features for Scene Understanding (traffic controls, signs, special regions such as RRX and flood zones, lane states) end to end: data → model → eval → onboard launchMine and curate large-scale driving data (real and simulated) to find long-tail failures, build training and eval sets, and design labeling and supervisionDesign, train, and iterate on model tasks and heads in large multi-task models. Improve calibration, robustness, and generalization to new geographiesBuild rigorous evaluation: PRT metrics, slicing taxonomies, side-by-sides, and staging launches, and turn the results into launch decisionsIntegrate models into the onboard stack (C++), combining learned outputs with safe heuristic fallbacks, Mendel-guarded rollouts, and map-contradiction handlingTriage real driving events, find the root cause anywhere in the stack, and propose surgical or systemic fixes. Work closely with Perception, Reasoning, Mapping, and ML Infrastructure teamsBenefitsMedical, dental, and vision insurance for employees and dependentsEmployee assistance programs focused on mental healthPersonalized workplace adjustments for diverse needs and abilities, including physical, mental, and neurodivergent considerationsAccess to mental health appsOnsite wellness centersSecond medical opinion for you and your loved onesCounseling servicesSupport programs including menopause benefitMedical advocacy program for transgender employeesCompetitive compensationRegular bonus and equity performance grant opportunitiesGenerous 401(k) and regional retirement plansAnnual cross-company compensation review and pay equity analysis1-on-1 financial coachingFertility and growing family assistanceParental leave and baby bonding leaveElder care and supportSurvivor income benefitBackup childcareCaregiver leavePaid time off, including vacation, bereavement, sick leave, parental leave, disability, and holidaysJury duty leaveMilitary leaveHybrid work model with remote work opportunities also availableEducational reimbursementPeer learning and coaching platformDonation matching programsVolunteer hoursEmployee resource groupsInternal community groups and local culture clubsInspiring spaces to work, recharge, and collaborate with fellow WaymonautsOn-site meals and snacksFitness centers, massage programs, and ergonomic supportOn-demand fitness, wellbeing, and cooking classesCommuter benefitsHands-on experience shipping ML models to production, including training, offline evaluation, and deployment (L4: 2+ yrs; L5: 4+ yrs, or a PhD plus 2+ yrs) Demonstrated cross-stack generalist work: has owned problems that crossed data, model, and systems/serving boundariesBS/MS/PhD in CS, EE, Robotics, ML, or a related field, or equivalent practical experienceStrong data mining / data engineering: building large-scale batch pipelines (Beam/Flume/Spark or similar) and analytical SQL to find, slice, and curate dataRigorous evaluation instincts: metric design, error analysis and slicing, and A/B or staged rolloutProficiency in Python with a modern DL framework (JAX, TensorFlow, or PyTorch) and production C++Experience with transformer or temporal models (GRU/RNN), multi-task learning, and model calibration/uncertaintyAV/Robotics experience, especially perception (detection, classification, tracking)TPU/accelerator performance work; exporting models for onboard/edge inferenceLabeling-pipeline and supervision design; autolabeling or synthetic dataBuilds internal tools (visualization, triage, experiment tracking) that accelerate the team