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About This Role
What to Expect
The AI Platform Integration team is seeking a Functional Safety Engineer to design and develop cutting\-edge safety\-critical systems for seamless integration of AI\-driven technologies into AI platforms. We are looking for experienced candidates passionate about ensuring safety and reliability in innovative AI applications.
The AI Platform Integration team focuses on embedding AI solutions into safety\-critical vehicle functions, such as autonomous driving, predictive control systems, and real\-time decision\-making. You will help design and build software and integration frameworks that meet stringent safety and reliability standards. Your role will involve determining optimal system design paradigms, communication protocols, and monitoring strategies while collaborating with a team of functional safety engineers, software developers and hardware engineers to establish robust processes, tools, and frameworks that set or exceed industry benchmarks.
This is a high\-visibility role with significant responsibility. You will work cross\-functionally with AI development, hardware, and systems engineering teams to achieve integration goals and meet safety requirements you help define. Additionally, you will contribute directly to software solutions across the full vertical stack to address these requirements. Join a diverse team of experts across multiple domains to pioneer industry\-first integrations of AI into vehicle safety systems.
What You'll Do* Partner with cross\-functional engineering teams to define integration strategies, evaluate architectural trade\-offs, and ensure AI platform designs meet functional safety objectives across compute, sensing, and actuation layers
- Lead functional safety activities such as Hazard Analysis and Risk Assessments (HARAs), Design Failure Mode and Effects Analysis (DFMEAs), and Safety Requirements definition using internal and industry\-standard tools to ensure high system reliability and performance
- Leverage simulation, SIL/HIL testing, and fleet data to validate safety assumptions and monitor real\-world performance of AI\-driven systems
- Implement safety\-critical software as needed to meet requirements in system architecture, with a focus on monitoring, fault detection for AI platforms
- Ensure follow\-through on design and integration aspects across various teams, establishing milestones from concept to deployment (e.g., simulation testing, engineering validation, and production readiness)
- Shape the safety concept validation plan to ensure high\-quality test cases for AI systems, both in\-vehicle and in simulation environments
What You'll Bring* Experience designing safety\-critical systems, preferably in AI or autonomous systems contexts
- First\-hand experience with automotive safety standards (such as ISO 26262\) and relevant tools, including Hazard and Risk Assessment (HARA) and/or Failure Mode and Effects Analysis (FMEA) and/or Fault Tree Analysis
- Deep technical expertise in any of the following: AI system integration, embedded systems, real\-time software development, or vehicle control systems
- Experience with testing and validating safety\-critical software, particularly in AI or machine learning applications to support robust Safety Cases
- Experience of designing systems with stringent availability and safety requirements
- Proficiency in software development leveraging agentic workflows is preferred (e.g., machine learning frameworks or real\-time processing)
- Experience in C\+\+, Python, or similar programming is preferred
Compensation and Benefits
Benefits
Along with competitive pay, as a full\-time Tesla employee, you are eligible for the following benefits at day 1 of hire:
- Medical plans \> plan options with $0 payroll deduction
- Family\-building, fertility, adoption and surrogacy benefits
- Dental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contribution
- Company Paid (Health Savings Accounts) HSA Contribution when enrolled in the High\-Deductible medical plan with HSA
- Healthcare and Dependent Care Flexible Spending Accounts (FSA)
- 401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits
- Company paid Basic Life, AD\&D
- Short\-term and long\-term disability insurance (90 day waiting period)
- Employee Assistance Program
- Sick and Vacation time (Flex time for salary positions, Accrued hours for Hourly positions), and Paid Holidays
- Back\-up childcare and parenting support resources
- Voluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft \& legal services, and pet insurance
- Weight Loss and Tobacco Cessation Programs
- Tesla Babies program
- Commuter benefits
- Employee discounts and perks program
Expected Compensation
$140,000 \- $390,000/annual salary \+ cash and stock awards \+ benefits
Pay offered may vary depending on multiple individualized factors, including market location, job\-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
Tesla is an Equal Opportunity / Affirmative Action employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws.
Tesla is also committed to working with and providing reasonable accommodations to individuals with disabilities. Please let your recruiter know if you need an accommodation at any point during the interview process.
Salary Context
This $140K-$390K range is above the 75th percentile for AI Safety roles in our dataset (median: $243K across 6 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Safety positions make up 0% of the market. At Tesla, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Safety roles pay a median of $300,000 based on 21 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($265K) sits 12% below the category median. Disclosed range: $140K to $390K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include Research Engineer ($280,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Tesla AI Hiring
Tesla has 8 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Software Engineer, AI Safety. Positions span Brookshire, TX, US, Palo Alto, CA, US. Compensation range: $258K - $390K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Safety roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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