{"id":4148590,"url":"https://alion.io/job/cariad-staff-engineer-autonomous-driving-data-platform-curation","title":"Staff Engineer, Autonomous Driving Data Platform & Curation","company":{"id":135,"name":"CARIAD","domain":"cariad.technology","url":"https://alion.io/company/cariad","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Robotics","role_family":"Robotics","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Dagster","optional":false},{"name":"Dask","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"Computer Vision","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-09-11T15:42:39Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-12T08:17:38Z","board_verified":true,"closed_at":null,"days_open":61,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":61},"description":"We are CARIAD, an automotive software development team with the Volkswagen Group. Our mission is to make the automotive experience safer, more sustainable, more comfortable, more digital, and more fun. To achieve that we are building the leading tech stack for the automotive industry and creating a unified software platform for over 10 million new vehicles per year. We’re looking for talented, digital minds like you to help us create code that moves the world. Together with you, we’ll build outstanding digital experiences and products for all Volkswagen Group brands that will transform mobility. Join us as we shape the future of the car and everyone around it.\nRole Summary:\nThe Staff Engineer, Autonomous Driving Data Platform & Curation is a hands-on staff-level individual contributor who owns the path from raw multimodal vehicle data to reliable, versioned, and model-ready datasets. The ideal candidate combines production data or ML systems experience with practical knowledge of autonomous-driving data and can lead ingestion, schema, storage, query, curation, quality, and delivery. This engineer designs and operates datasets containing more than 10 million records or samples, evaluates formats such as Parquet and Lance, and enables efficient filtering, slicing, random access, and sequential retrieval. The role also advances data-quality monitoring, statistical and out-of-distribution detection, rule-based and model-based tagging, model-in-the-loop and human-in-the-loop labeling, and future data preparation for imitation learning and reinforcement learning. \nRole Responsibilities:\nAutonomous Data Architecture, Storage & Query \nDesign canonical representations for drives, scenarios, clips, frames, trajectories, sensor references, vehicle state, map context, labels, predictions, and dataset manifests. \nBuild and maintain validated, versioned datasets containing more than 10 million records or samples on local or cloud object storage. \nEvaluate Parquet, Apache Arrow, Lance, and related technologies; own partitioning, indexing, file sizing, compaction, schema evolution, lineage, and reproducibility. \nOptimize filtering, projection, joins, scenario slicing, random sampling, shuffling, sequential retrieval, and model data-loading performance. \nMeasure and improve ingestion throughput, query latency, training throughput, storage utilization, reliability, and cost per usable sample. \nAutonomous Dataset Lifecycle, Curation & Training Readiness \nWork effectively with synchronized camera and other sensor data, ego state, localization, calibration, coordinate frames, map context, control actions, clips, and trajectories. \nTranslate perception, planning, VLA, and evaluation needs into schemas, searchable attributes, scenario definitions, sampling strategies, and reproducible dataset splits. \nBuild reliable batch or distributed pipelines for ingestion, transformation, enrichment, validation, cataloging, and publication, including retries, backfills, idempotency, and observability. \nEnable self-service discovery and composition for lane keeping, lane changes, long-tail scenarios, hard examples, balanced datasets, and leakage-resistant train, validation, and test splits. \nData Quality, Statistics & Distribution Monitoring \nDefine automated quality gates, dashboards, and alerts for completeness, validity, freshness, duplication, synchronization, calibration, corruption, label integrity, coverage, balance, and cost. \nApply statistics, sampling, and distribution comparisons to detect drift, anomalies, underrepresented conditions, and out-of distribution data. \nUse deterministic checks, heuristic rules, geometry and metadata queries, embeddings, VLMs, and learned models to assess quality and create searchable scenario tags. \nQuarantine suspicious data and lead root-cause analysis across collection, synchronization, schema, transformation, storage, annotation, sampling, and model-consumer failures. \nAuto-Labeling, Closed-Loop Data Automation & RL Readiness \nDesign model-in-the-loop and human-in-the-loop labeling workflows with confidence thresholds, review routing, audit sampling, disagreement handling, and label provenance. \nClose the loop from model failures and edge cases through selection, annotation, quality review, dataset publication, training, and evaluation. \nEvaluate auto-labeled and synthetic data using label quality, coverage, distributional impact, and downstream model performance. \nPrepare replayable trajectory data for imitation learning and offline RL, including observations, actions, timestamps, policy versions, interventions, rewards, termination conditions, and alignment checks. \nCross-Functional Technical Leadership, Reliability & Cost \nServe as the staff-level owner and escalation point for dataset architecture, storage and query performance, data quality, reliability, reproducibility, and cost. \nAlign vehicle collection, autonomy metadata, model interfaces, annotation, storage, governance, and training requirements across partner teams. \nCommunicate risks, trade-offs, decisions, and recovery plans with clear evidence; maintain traceable schemas, data contracts, lineage, operating procedures, and incident records. \nMentor engineers and strengthen design reviews, testing, observability, reproducibility, cost awareness, and data engineering standards.\n\nGeneral Skills:\nDeep production experience with Parquet and/or Lance, including dataset migration, indexing, compaction, schema evolution, version maintenance, cloud storage, random access, or training-loader optimization. \nExperience with large-scale multimodal autonomous-driving data from cameras, LiDAR, radar, maps, localization, vehicle state, simulation, or other sensor-rich robotic systems. \nExperience building auto-labeling, active-learning, model-in-the-loop, or human-in-the-loop systems, including confidence calibration, annotation routing, review workflows, audit sampling, label agreement, and annotation QA. \nStrong statistical knowledge for quality monitoring, distribution comparison, drift measurement, anomaly detection, and out-of-distribution detection. \nExperience using rule-based logic, geometry and metadata heuristics, embeddings, semantic search, VLMs, classifiers, or other learned methods to tag data, assess quality, mine edge cases, detect hard examples, or prioritize review. \nExperience connecting dataset metrics, composition, label quality, drift, cost, and downstream model performance through dashboards and evaluation. \nExperience optimizing distributed ML data loading, caching, sampling, preprocessing, and the data-to-GPU path. \nExperience preparing sequential or trajectory datasets for imitation learning, offline reinforcement learning, preference or ranking data, reward modeling, or policy evaluation, including observations, actions, rewards, policy provenance, and replay semantics. \nFamiliarity with data governance, privacy, retention, redaction, access control, auditability, and security requirements for fleet-collected sensor data. \n\nRequired Specialized Skills:\nDemonstrated ownership of production data pipelines and large datasets, including systems with more than 10 million records or samples or comparable multi-terabyte scale. \nStrong dataset and storage foundation, including schema design, columnar storage, partitioning, file and fragment sizing, compression, predicate pushdown, projection, indexing, compaction, schema evolution, versioning, lineage, and reproducibility. \nHands-on experience with Parquet, Lance, Apache Arrow, or a comparable analytical dataset technology, with the ability to reason about format selection and benchmark storage, query, update, and model-loading trade-offs. \nStrong Python and SQL skills with production software-engineering practices, including testing, code review, version control, profiling, debugging, maintainable APIs, and operational documentation. \nExperience building distributed or parallel data pipelines and orchestration using technologies such as Ray, Spark, Beam, Dask, Airflow, Dagster, or comparable systems. \nExperience with local or cloud object storage, such as S3 or GCS, and practical understanding of throughput, request patterns, caching, data movement, reliability, access control, and cost. \nBasic autonomous-driving or robotics data knowledge, including temporal sensor data, camera and other sensor modalities, timestamps and synchronization, calibration, ego and vehicle state, coordinate frames, clips, trajectories, scenarios, and annotations. \nAbility to design automated data validation and observability and translate ML training, sampling, evaluation, and failure-analysis needs into reliable data products. \n\nDesired Skills:\nDeep production experience with Parquet and/or Lance, including dataset migration, indexing, compaction, schema evolution, version maintenance, cloud storage, random access, or training-loader optimization. \nExperience with large-scale multimodal autonomous-driving data from cameras, LiDAR, radar, maps, localization, vehicle state, simulation, or other sensor-rich robotic systems. \nExperience building auto-labeling, active-learning, model-in-the-loop, or human-in-the-loop systems, including confidence calibration, annotation routing, review workflows, audit sampling, label agreement, and annotation QA. \nStrong statistical knowledge for quality monitoring, distribution comparison, drift measurement, anomaly detection, and out-of-distribution detection. \nExperience using rule-based logic, geometry and metadata heuristics, embeddings, semantic search, VLMs, classifiers, or other learned methods to tag data, assess quality, mine edge cases, detect hard examples, or prioritize review. \nExperience connecting dataset metrics, composition, label quality, drift, cost, and downstream model performance through dashboards and evaluation. \nExperience optimizing distributed ML data loading, caching, sampling, preprocessing, and the data-to-GPU path. \nExperience preparing sequential or trajectory datasets for imitation learning, offline reinforcement learning, preference or ranking data, reward modeling, or policy evaluation, including observations, actions, rewards, policy provenance, and replay semantics. \nFamiliarity with data governance, privacy, retention, redaction, access control, auditability, and security requirements for fleet-collected sensor data. \nYears of Relevant Experience:\n8+ years of experience in applied machine learning or deep learning \n4+ years of experience reinforcement learning, computer vision, or AD/ADAS systems. \nStrong candidates with equivalent industry experience will be considered \nRequired Education:\n Master’s in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, or a related field\nDesired Education: \n PhD in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, or a related field \nCompensation\nSalary range is dependent on factors such as geographical differentials, credentials or certifications, industry-based experience, qualification and training. In the city of Mountain View, CA, the salary range for this position is $196,267 - $269,203.\nBy submitting your application, you acknowledge and agree to participate in the export control and sanctions compliance screening process. Your cooperation in this matter is essential to our shared success and the integrity of our operations. Thank you for your understanding and commitment to upholding these important standards.\nCARIAD, Inc. provides performance-based merits and annual bonus along with a competitive benefits package. Benefits include medical, dental, vision, 401k with employer match and defined contribution plan, short- and long-term disability, basic life and AD&D insurance, employee assistance program, tuition reimbursement and student loan repayment plans, maternity and non-primary caregiver leave, adoption assistance, employee referral program and vacation and paid holidays. We also offer a unique vehicle le...","description_format":"text","description_chars":13100,"description_truncated":true,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["401k plan"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Autonomous Driving","Automotive Software"],"lifecycle":[{"event":"open","at":"2026-11-12T08:17:38Z"}],"visa":[],"liveness":{"score":24,"band":"cold","label":"Long shot","p_open":1,"p_active":0.662,"p_room":0.36,"age_days":61,"expected_fill_days":39,"reasons":["conf:1","win:tail","crowd:"],"computed_at":"2026-11-12T09:46:41Z"},"pay":null,"html_url":"https://alion.io/job/cariad-staff-engineer-autonomous-driving-data-platform-curation","json_url":"https://alion.io/job/cariad-staff-engineer-autonomous-driving-data-platform-curation.json","meta":{"generated_at":"2026-11-12T09:46:41Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":19861,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-13T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":135},"rest":"https://alion.io/mcp/rest/get_company?id=135"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fcariad-staff-engineer-autonomous-driving-data-platform-curation"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fcariad-staff-engineer-autonomous-driving-data-platform-curation"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fcariad-staff-engineer-autonomous-driving-data-platform-curation"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/cariad-staff-engineer-autonomous-driving-data-platform-curation\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fcariad-staff-engineer-autonomous-driving-data-platform-curation"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4148590}},{"tool":"search_jobs","arguments":{"company":"cariad.technology","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4148590},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}