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About This Role
About Lucid
At Lucid, we are creating exceptional mobility experiences through innovation to drive the world forward. Built on Lucid's proprietary technology and software-defined vehicle architecture, our award-winning vehicles bring our "Compromise Nothing™" approach to the global automotive market. That means refusing to choose between performance and sustainability, design and engineering, ambition and integrity. In Lucid Air and Lucid Gravity, we have designed and built vehicles that have redefined their segments, combining exceptional range, performance, design, and expansive space in a single experience.
We achieve this through deep vertical integration, with design, engineering, and production happening in-house across our global offices and manufacturing facilities. Our teams come from industries around the world, united by a shared commitment to excellence. By refusing to settle, you can help redefine what's possible and shape the future of mobility.
Position Overview
We are seeking an experienced Senior Engineering Manager, Data Platform and AIto lead the strategy, architecture, delivery, and operation of an enterprise data platform that ingests high-volume vehicle telemetry and enterprise systems data, integrates and curates it into trusted data products, and provides scalable, governed, AI-ready data foundations. This role will oversee teams building business-critical capabilities for engineering, manufacturing, supply chain, finance, sales, service, and corporate functions.
The ideal candidate combines strong engineering leadership with deep technical expertise in data engineering, data quality, AWS, and Amazon Redshift. You will build and lead a high-performing team responsible for reliable batch and streaming ingestion, cross-domain data integration, identity resolution, data curation, governance, and platform operations at scale.
What You'll Do
Lead Data & AI Platform Engineering
- Define and execute the strategy and roadmap for enterprise data, telemetry, and AI platforms.
- Lead, mentor, and grow a high-performing team of data and platform engineers.
- Drive engineering excellence through automation, operational rigor, and modern development practices.
- Partner with business and technology leaders to deliver high-impact data products and capabilities.
Build and Scale Data Platforms
- Own the architecture, reliability, performance, security, and cost efficiency of the enterprise data platform on AWS.
- Lead the design and operation of Amazon Redshift, including data modeling, distribution and sort strategies, workload management, scaling, performance tuning, access controls, and cost optimization.
- Develop and operate scalable data lakes, warehouses, streaming platforms, orchestration frameworks, and self-service data capabilities.
- Establish reusable platform patterns, data contracts, metadata standards, and automation that improve delivery speed and operational consistency.
Lead Enterprise & Telemetry Data Engineering
- Build and operate high-volume, resilient batch and real-time pipelines that ingest vehicle telemetry and data from enterprise applications.
- Integrate and stitch telemetry with manufacturing, supply chain, sales, service, finance, customer, and master data to create consistent cross-domain views.
- Address schema evolution, late-arriving events, time-series processing, event ordering, data contracts, and vehicle-to-enterprise identity resolution.
- Curate governed, reusable data products and standardized business entities that support analytics, engineering diagnostics, machine learning, and Generative AI.
- Provide secure, reliable self-service access to trusted telemetry and enterprise data for engineering, data science, and business teams.
Drive Data Quality, Governance, and Operational Excellence
- Define and enforce data quality standards, ownership, controls, and measurable SLAs across critical data products.
- Implement data profiling, validation, reconciliation, lineage, observability, anomaly detection, and root-cause remediation capabilities.
- Establish monitoring, incident management, disaster recovery, and service-level objectives for business-critical data services.
- Lead platform modernization, performance optimization, security, compliance, and cloud cost optimization initiatives.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
- 12+ years of experience in data engineering, data platform engineering, distributed systems, or large-scale data technologies.
- 6+ years of experience leading engineering teams and delivering enterprise-scale, production data solutions.
- Deep hands-on expertise in Amazon Redshift, including dimensional modeling, distribution and sort strategies, workload management, query and storage optimization, security, scaling, and cost management.
- Proven experience integrating high-volume vehicle, IoT, connected-product, or telemetry data with enterprise transactional and master data.
- Strong knowledge of data quality management, including profiling, validation, reconciliation, lineage, observability, quality metrics, and remediation processes.
- Experience creating curated, governed, reusable data products and AI-ready datasets for analytics, machine learning, and Generative AI.
- Deep understanding of batch and streaming architectures, schema evolution, event processing, time-series data, data contracts, and distributed data systems.
- Strong AWS experience and hands-on knowledge of technologies such as S3, Glue, Lambda, Kinesis or Kafka, Airflow, Spark, IAM, Terraform, CI/CD, and observability platforms.
- Strong programming skills in Python and SQL, with experience in Java and/or Scala preferred.
- Strong software engineering foundation and experience operating secure, reliable, petabyte-scale data platforms in production.
- Excellent communication and stakeholder management skills, with the ability to translate business and AI use cases into scalable technical solutions.
Why Join Us
You will lead a strategic engineering organization responsible for connecting vehicle telemetry and enterprise data to create trusted, governed, AI-ready foundations. This is an opportunity to shape the AWS and Amazon Redshift data platform, improve data quality and accessibility at enterprise scale, enable advanced analytics and AI, and develop a world-class engineering team.
Compensation & Benefits:Lucid offers a comprehensive and competitive benefits package including medical, dental, and vision insurance; life and disability coverage; paid time off; paid holidays, paid sick leave; and a 401(k) retirement plan. Hourly/non-exempt employees accrue up to 120 hours paid time off, and salaried/exempt employee accrue up to 160 hours paid time off. Eligible employees may also participate in Lucid's equity program and/or a discretionary annual cash incentive program. Incentive and equity awards, if applicable, are determined based on individual performance, role scope, market considerations, and overall company results, in accordance with the terms of the applicable plans.
Equal Opportunity: At Lucid, we believe diversity strengthens everything we build. Lucid Motors is proud to be an equal opportunity employer and is committed to providing an inclusive workplace for all. We consider all qualified applicants without regard to race, color, national or ethnic origin, age, religion, disability, sexual orientation, gender, gender identity or expression, marital status, or any other characteristic protected by applicable state or federal laws and regulations.
Accessibility: Lucid Motors is committed to providing reasonable accommodations for qualified individuals with disabilities. If you need any accommodation to participate in the application process, please contact us at TA-Operations <@> lucidmotors.com. This email address is designated solely for accommodation requests and is not monitored for job applications or resume submissions. To be considered for employment, all applications must be submitted through the Lucid Motors Careers website.
Candidate Data Privacy: By submitting your application, you understand and agree that your personal data will be processed in accordance with our Candidate Privacy Notice.
To all recruitment agencies:Lucid Motors does not accept agency resumes. Please do not forward resumes to Lucid Motors. Lucid Motors is not responsible for any fees related to unsolicited resumes.
Salary Context
This $211K-$310K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At Lucid Motors, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($260K) sits 23% above the category median. Disclosed range: $211K to $310K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Lucid Motors AI Hiring
Lucid Motors has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Newark, CA, Phoenix, AZ, US. Compensation range: $310K - $310K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (122) are outnumbered by mid-level (5,256) and senior (3,922) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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