Robotics Salary Guide 2025

Executive Summary

Data snapshot: December 2025 | 907 jobs with salary data

Key Numbers:

• Median Salary: $156,563
• Typical Range: $101,920 - $201,400 (25th-75th percentile)
• Career Growth Potential: 139% (entry to leadership)
• Software Premium: 53% over hardware roles
• Highest-Paying Industry: Transportation & Autonomous Vehicles ($200,000)
• Highest-Paying Location: California ($193,000)

What This Report Covers:

We analyzed 907 robotics jobs with disclosed salary data from our database of 3,300+ positions across various role types, industries, locations, and high-signal skills to answer the questions that actually matter:

• Which career tracks pay 2x others (and why)
• Whether graduate degrees are worth the cost (spoiler: not always)
• Which industries to target and which to avoid
• How much location really matters (spoiler: 81% salary premium exists)
• Which skills signal high-paying tracks

Bottom Line:

Role selection drives your earning potential more than experience or education. Software-focused roles average $194k while hardware-focused roles average $127k—a 53% premium. Industry choice matters almost as much: the same “Robotics Engineer” title pays $185k in tech but $113k in manufacturing.

The data shows clear patterns. Your first career decisions (software vs. hardware track, which industry to target) matter far more than years of experience or advanced degrees.

Table of Contents

1. Salary by Role

High-Level Insight:

Your career track determines your earning ceiling. We identified 5 distinct tracks with different salary ranges and growth potential.

Career Tracks:

Detailed Role Breakdown:

Motion Planning Engineers command the highest individual contributor salaries at $205k median, followed closely by Machine Learning Engineers at $200k. The software-focused track consistently outperforms others, with all three software roles (ML, Motion Planning, Software Engineering) clustered between $189k-$205k. Leadership and management roles top the overall rankings at $209k, but the premium over senior IC roles is modest—only $16k above Motion Planning Engineers.

The gap between tracks is substantial. Software-focused roles average $194k while hardware-focused roles average $127k—a 53% premium that compounds significantly over a 30-year career. Entry points vary dramatically as well: Automation Technicians start at $69k while Robotics Software Engineers enter at $189k, a 174% difference at career start. The hardware track does offer the widest progression range, spanning from $69k for technicians to $183k for senior hardware engineers, representing 167% growth potential for those who advance.

Systems-focused roles (Controls, Systems, Robotics Engineer) occupy a middle tier at $109k-$164k. These hybrid positions require both software and hardware knowledge but don’t command the premium of pure software specialization. Controls Engineers specifically sit at the lower end of this range ($109k), closer to hardware compensation than software, despite requiring programming skills.

Actionable Takeaway:

If you’re early in your career, choose your track now—the pay gap between software and hardware tracks makes early track selection financially significant. The $67k annual income difference between software and hardware tracks ($194k vs $127k) compounds to over $2 million across a 30-year career, even before accounting for the investment and equity opportunities higher salaries enable.

2. Salary by Education

690 jobs with specified education requirements

High-Level Insight:

There’s a binary split in robotics compensation: the technical track (bachelor’s and above) versus the technician track (associate degree or high school). The bachelor’s degree threshold represents a 147% salary premium ($169k vs $68k)—the single largest discrete jump in the data.

The Education ROI Story:

Master’s degrees pay more than PhDs in robotics ($186k vs $180k), a counterintuitive finding that reveals how this industry values applied skills over research credentials. Unlike academia-driven fields, robotics is an industry-driven market where companies prioritize engineers who can ship products over researchers who publish papers.

The bachelor’s to master’s premium is surprisingly modest—only 10% or roughly $17k annually. Consider the ROI calculation: two years of lost income ($340k for a bachelor’s-level engineer) plus tuition costs ($100k for a decent program) versus a $17k annual salary bump. You’d need 26 years just to break even, and that’s before accounting for opportunity cost and compound returns on that $440k. The data shows 67% of robotics jobs (465 out of 690) require only a bachelor’s degree and still offer a $169k median salary.

The real dividing line is that bachelor’s degree. It’s the gate to the technical track, opening access to engineering roles with six-figure salaries. Below that threshold, you’re largely limited to technician positions averaging $68k-$83k. Above it, even entry-level engineers start near $120k with clear paths to $200k+.

Actionable Takeaway:

Get the bachelor’s degree—it’s non-negotiable for the technical track. Consider a master’s only if you’re specifically targeting AI/ML specialization where the degree matters for role access, or if your employer pays for it. Skip the PhD unless you’re committed to staying in research permanently, and understand you’ll likely earn less than your master’s-holding industry peers.

3. Salary by Experience Level

High-Level Insight:

The first 7-10 years of your career (Entry to Senior) represent 116% salary growth and capture 88% of your total career earnings potential. Leadership roles add a modest $20k premium but show sharply diminishing returns compared to the rapid growth of early career progression.

Career Progression Analysis:

The steepest climb happens in your first two years. Entry to Junior level delivers 29% growth, the fastest year-over-year increase in the entire career progression. Years 2-5 (Junior to Mid) represent the peak acceleration period with 35% growth—this is when focused skill development and project delivery compound most effectively. The Mid to Senior jump (years 5-10) still delivers strong 25% growth as you reach the individual contributor ceiling.

After Senior, the curve flattens dramatically. The Senior to Lead+ transition adds only 11% or roughly $20k—a fraction of earlier jumps. This means the Entry to Senior climb ($89k to $193k) represents a $103k increase, while Senior to Lead+ adds just $20k more. The money is in the first decade, not in chasing leadership titles.

This pattern reveals something critical about robotics careers: experience and delivery beat credentials at the top. A Senior Engineer with only a bachelor’s degree earns $193k, outpacing PhD researchers at $180k. The market rewards proven execution over academic pedigree once you’ve reached senior levels. The first five years are where salary acceleration happens (29-35% jumps), making rapid skill development during this window far more valuable than any credential earned later.

Actionable Takeaway:

Optimize aggressively for skill development and high-impact projects in your first 5 years—that’s when salary jumps are largest (29-35% year-over-year). After reaching Senior level, shift your optimization criteria from base salary bumps to total compensation including equity, work-life balance, and learning opportunities. The $20k leadership premium rarely justifies the increased responsibility and reduced technical work.

4. Salary by Industry

High-Level Insight:

Industry choice can double your salary for the same job title. Transportation & Autonomous Vehicles and Robotics Software & AI command $200k and $198k medians respectively—exactly 2x what Research & Academia ($102k) and Industrial Manufacturing ($103k) pay. This isn’t a 10-20% adjustment—it’s career transformation.

Industry Tiers:

Detailed Industry Rankings:

Transportation & Autonomous Vehicles tops the rankings at $200k median, driven by intense VC-funded competition for talent in self-driving technology. Robotics Software & AI follows at $198k, representing pure-play robotics companies and AI-first startups. These premium tier industries don’t just pay more—they offer equity packages on top of base salary, creating total compensation packages that can reach $300k-$400k for senior engineers. The companies in this tier (Waymo, Cruise, Aurora, Figure, 1X) compete aggressively for the same talent pool, driving sustained upward pressure on compensation.

The high tier—Aerospace & Defense ($170k) and Healthcare & Life Sciences ($148k)—represents established industries with sophisticated robotics needs and stable budgets. Defense contractors and medical device companies have procurement processes and government contracts that support consistent, above-market compensation without the volatility of startups. The mid-tier industries (Logistics $130k, Energy $128k, System Integration $113k) represent industrial applications where robotics improves operational efficiency. These are mature markets with clear ROI calculations but traditional compensation structures.

The bottom tier reveals a harsh reality: Research & Academia ($102k) and Industrial Manufacturing ($103k) consistently underpay relative to market. If you’re currently in manufacturing or academia earning ~$100k, the same skillset could command $200k in Transportation/AV or Software/AI—not through a promotion or skill upgrade, but purely through industry switching. Traditional industries operate on legacy pay scales that haven’t adjusted to the modern robotics talent market, while VC-funded companies set compensation based on competitive dynamics, not historical precedent.

Actionable Takeaway:

If you’re mid-career in a low-tier industry, industry switching is your biggest salary lever—same skills, different industry can mean a 50-100% income change. Don’t optimize your career within manufacturing or academia; optimize your exit strategy toward premium-tier industries. The $100k annual difference compounds to $3-4 million over a 30-year career.

5. Geographic Analysis: US Market

High-Level Insight:

California dominates US robotics compensation with a $193k median—81% above the non-California average of $106k. The West Coast commands a structural premium driven by concentration of autonomous vehicle and robotics AI companies.

State Rankings:

States with 20+ job postings shown. Additional states excluded due to insufficient sample size.

Geographic Analysis:

California’s $193k median reflects the concentration of premium-tier employers in the Bay Area: autonomous vehicle companies (Waymo, Cruise, Zoox), robotics AI startups (Figure, Covariant), and tech giants with robotics divisions (Google, Tesla, Apple). These companies compete for the same talent pool, creating sustained upward salary pressure. The premium isn’t just cost-of-living—even after adjusting for housing costs, California’s $193k translates to roughly $130k equivalent purchasing power, still 23% above non-CA markets.

The West Coast premium extends beyond California. Washington’s $179k median, driven primarily by Seattle’s robotics ecosystem (Amazon Robotics, autonomous vehicle presence), demonstrates consistent 15-30% premiums over other regions. Massachusetts at $166k benefits from its deep robotics research infrastructure—MIT, Boston Dynamics, and numerous academic spinouts—though it trails West Coast compensation.

Other markets show distinct profiles. Michigan’s $143k reflects its automotive robotics heritage—solid compensation for the Midwest, though traditional manufacturing focus limits upside compared to software-first companies. Texas at $124k and Maryland at $132k represent growing robotics hubs with defense contractors and industrial applications. New York at $114k serves niche markets but lacks the critical mass of robotics employers to drive premium compensation.

The purchasing power calculation matters: Massachusetts ($166k) and Washington ($179k) deliver 85-93% of California salaries while housing costs run 40-60% lower. For many engineers, these markets optimize the income-to-cost-of-living ratio better than California.

Actionable Takeaway:

West Coast markets (California, Washington) offer maximum earning potential if you’re optimizing for absolute income and wealth building. East Coast hubs (Massachusetts, Maryland) provide strong compensation with better purchasing power and quality of life. Regional markets (Michigan, Texas, Illinois) serve as excellent entry points to build experience before targeting premium markets, or as stable career bases if optimizing for lower cost of living over maximum salary.

6. Top Hiring Companies

High-Level Insight:

Company choice creates significant compensation variance beyond industry effects. Top-tier companies (NVIDIA, Waymo, Shield AI) pay 46-73% above market median, while high-volume employers show divergent strategies—Amazon and NVIDIA both post ~70 jobs, but NVIDIA pays 58% more ($270k vs $171k).

Company Compensation Strategies:

The innovator tier—NVIDIA($270k), Waymo ($232k), and Shield AI ($228k)—represents companies pushing the boundary of what’s technically possible in AI, autonomous systems, and robotics. NVIDIA’s 73% premium reflects its position as the infrastructure provider for AI/robotics, where marginal improvements in their chips directly impact every customer. Autonomous vehicle companies (Waymo, Shield AI, GM’s Cruise division at $213k) consistently pay 40-50% premiums as they tackle unsolved engineering problems in perception, planning, and real-world deployment.

High-volume employers reveal two distinct approaches. NVIDIA pairs scale with premium pay (70 jobs at $270k median), competing for specialists who can advance state-of-the-art. Amazon takes a different path (70 jobs at $171k), paying closer to market rates while offering equity, career progression, and the scale to deploy robotics systems across hundreds of warehouses. Both strategies work—NVIDIA attracts boundary-pushing researchers, Amazon builds large teams solving operational challenges. Tesla ($165k) and Apple ($162k) similarly leverage mission and product scope to compete on total package beyond base salary.

Established robotics and industrial companies cluster in the $114k-$155k range. Boston Dynamics ($155k), ABB ($140k), FANUC ($125k), and Toyota Research Institute ($114k) represent companies deploying proven robotics technology at production scale, often with decades of domain expertise. These companies offer different value propositions: work-life balance, deep engineering knowledge over rapid iteration, physical products you can touch, and opportunities to work on systems operating in factories and warehouses worldwide.

Actionable Takeaway:

Target companies based on your career stage and what you want to learn. Early career: innovator-tier companies maximize learning velocity and wealth building if you can handle the intensity. Mid-career: established companies offer depth in production systems, work-life balance, and paths to subject matter expertise that startups can’t provide. Many senior engineers at innovator-tier companies came from traditional robotics backgrounds—the experience compounds in both directions.

7. Highest-Paying Skills

Top 10 Skills:

High-Level Insight:

Skills analysis primarily reflects role selection, not individual skill value. Software and AI skills cluster at +35% to +45% premium because they indicate Software Engineer roles, not because adding Python to your resume increases your salary. You’re choosing a career track through your skill clusters, not just learning individual technologies.

Understanding Skill Premiums:

The top skills are exclusively software and AI focused: CUDA, machine learning, C++, Python, PyTorch, TensorFlow, reinforcement learning. This reflects the $194k software track versus the $127k hardware track we identified in Section 1. Python doesn’t magically pay +37% more—rather, roles requiring Python (Robotics Software Engineers, ML Engineers) pay +37% versus roles that don’t (Automation Technicians, Field Service Engineers). The skill is a proxy for the role, and the role determines compensation.

Similarly, “Technical Leadership” at +43% isn’t something you add to your skillset—it’s a descriptor indicating senior or management positions that inherently pay more. Industrial and controls skills (PLC programming, HMI/SCADA, Allen-Bradley) show the inverse pattern, clustering in roles that average $109k-$127k. These aren’t “worse” skills—they’re indicators of the hardware/technician track, which has different compensation norms but strong demand and clear career paths.

What makes certain skills valuable is their market prevalence combined with track signaling. Python appears in 43% of jobs, C++ in 38%, and Machine Learning in 31%—these are the common language of high-paying software-focused roles. If you want to switch from hardware to software track, you need the full stack (Python + C++ + ML frameworks + domain specialization like computer vision or motion planning), not just one course. Employers hiring for software roles expect the complete toolkit.

Actionable Takeaway:

Focus on skill clusters that define career tracks, not individual skills in isolation. Switching tracks requires systematic development of a complete skillset—one Python course won’t reposition you from Automation Technician to ML Engineer. If you’re building a software career, prioritize the high-prevalence skills (Python, C++, ML) that appear across the most job opportunities. If you’re in the hardware/controls track, recognize that your skills have strong market demand—optimize for roles and companies within that track rather than chasing software premiums.

8. Salary by Role × Industry

Top 15 Combinations:

High-Level Insight:

Industry choice can matter more than role choice for generic titles. A “Research Scientist” varies by 79% based on industry alone: $112k in academia versus $200k in autonomous vehicles. That’s an $88k annual difference for the same job title. Specialized roles show more consistent compensation across industries.

Role × Industry Deep Dive:

Generic titles show massive industry-driven variance. “Robotics Engineer” spans from $113k in industrial manufacturing to $185k in robotics software/AI—a 64% spread for identical job titles. “Research Scientist” demonstrates the widest swing at 79%: academia pays $112k while Transportation/AV pays $200k for the same role descriptor. Even “Systems Engineer” varies by 22%, which is the smallest variance we observed but still represents a $44k annual difference.

Specialized titles maintain more consistent compensation regardless of industry. Motion Planning Engineers, Machine Learning Engineers, and Product Managers command similar salaries whether they work in Transportation/AV, Robotics Software/AI, or Aerospace—the specialization itself signals a premium track. A Motion Planning Engineer earns $203k-$205k across industries, while a generic “Robotics Engineer” swings wildly from $113k to $185k.

Transportation & Autonomous Vehicles pays a universal premium across all role types. ML Engineers earn $201k in Transportation/AV, Motion Planning Engineers $203k, Research Scientists $200k, even Hardware Engineers $183k and Mechanical Engineers $175k. This isn’t just “software roles in tech”—it’s Transportation/AV specifically commanding 15-25% premiums across the board due to intense competition for talent in the autonomous vehicle race.

The pattern reveals an optimization strategy: if your title is generic, industry selection becomes critical. If your title is specialized, you’ve already positioned yourself in a premium track where industry matters less. Controls Engineers, for example, range from $109k to $184k depending on industry—that $75k spread represents the difference between industrial manufacturing versus aerospace & defense applications of the same core skillset.

Actionable Takeaway:

If you hold a generic title (“Robotics Engineer,” “Research Scientist,” “Systems Engineer”), treat industry switching as your primary salary optimization lever—the same title can mean a 50-80% income difference. If you’re early in your career, invest in specializing your role (Motion Planning, ML Engineering, specific domain expertise) to reduce industry dependence and increase negotiating power. The most valuable combination is specialized role + premium industry, but specialized role alone provides strong baseline compensation.

Methodology

Data Source:

• 907 robotics and automation jobs with disclosed salary data analyzed (of total 3,300+ positions)
• Collection period: November-December 2025
• Source: Aggregated from company career pages and applicant tracking systems
• Geographic coverage: Global, with US state-level breakdown

Filtering & Quality:

• Salary range: $12,000 - $1,200,000 (outlier removal at both ends)
• Currently open and recently closed jobs included to capture seasonal variations
• Deleted jobs excluded from analysis
• Jobs posted in multiple locations or categories counted in each relevant segment

Statistical Thresholds:

We applied minimum sample size requirements to ensure statistical significance and protect against small-sample bias:

• Roles: Minimum 28 jobs per category
• Industries: Minimum 30 jobs per category
• US States: Minimum 20 jobs per state
• Companies: Minimum 10 jobs per company
• Skills: Minimum 30 jobs per skill
• Role × Industry: Minimum 15 jobs per combination
• Seniority Levels: Minimum 25 jobs per level

Categories below these thresholds were excluded from analysis to prevent misleading conclusions from insufficient data.

Salary Metrics:

• Median used throughout (not mean) to reduce outlier impact
• IQR (25th-75th percentile) shown for salary ranges
• Salary values in tables and charts rounded to nearest $1,000 for readability
• “Market median” refers to $156,563 overall median across all jobs
• Premium/discount percentages calculated against market median

Important Limitations:

This data represents posted salary ranges, not actual total compensation. Most technology companies offer equity, bonuses, and other benefits on top of base salary, particularly at senior levels. The analysis also reflects job posting requirements rather than credentials actually held by employed professionals—a job requiring a bachelor’s degree may be filled by someone with a master’s.

Tech industry and software-focused roles are over-represented in our dataset relative to traditional manufacturing, reflecting both market hiring activity and our platform’s current reach. Seniority levels were inferred from years of experience requirements when not explicitly stated; approximately 30% of jobs lacked sufficient data for confident seniority classification and were excluded from that specific analysis.

Geographic analysis is limited to locations explicitly mentioned in job postings. Remote positions may be counted in multiple locations if companies specified multiple acceptable work locations.

What This Means for Your Career

If You’re Just Starting:

Your foundational decisions matter more than you think. The data shows that choosing the software track over hardware track creates a 53% salary premium ($194k vs $127k average) that compounds over 30 years into a $2+ million difference. Get your bachelor’s degree—it’s the non-negotiable gate to the technical track and represents a 147% premium over technician roles. Skip the master’s unless you’re specifically targeting AI/ML specialization where the degree unlocks role access, or your employer pays for it.

Target Transportation/AV or Robotics Software/AI industries from day one if maximizing income. These sectors pay $198k-$200k medians versus $102k-$113k in manufacturing or academia—nearly double for the same skills. If you’re not in a tech hub, use your first role to build foundational skills, then relocate to California ($193k median) or Washington ($179k) within 2-3 years. The first 5 years of your career deliver 29-35% year-over-year salary growth—optimize ruthlessly for learning velocity during this window.

Build complete skill clusters, not individual skills. The market wants Python / C++ / ML frameworks + domain specialization (computer vision, motion planning, manipulation), not one Coursera certificate. Focus on high-prevalence skills that appear across the most opportunities: Python (43% of jobs), C++ (38%), Machine Learning (31%).

If You’re Mid-Career:

Your biggest salary lever is industry switching, not promotion. The same “Robotics Engineer” title pays $185k in software/AI versus $113k in manufacturing—a $72k annual difference requiring no new skills, just a different employer. Similarly, “Research Scientist” varies 79% by industry alone ($112k academia vs $200k autonomous vehicles). If you’re in a lower-paying industry earning $100k-$120k, the same skillset commands $180k-$200k in Transportation/AV or Robotics Software/AI.

Consider geographic relocation if you’ve plateaued locally. California’s $193k median represents 81% premium over non-CA markets, even after cost-of-living adjustments. If California feels too expensive, Massachusetts ($166k) or Washington ($179k) offer 85-93% of California salaries with 40-60% lower housing costs.

After reaching Senior level ($193k median), optimize for total compensation beyond base salary. The Senior to Lead+ jump adds only $20k (+11%), so evaluate leadership moves based on equity, work-life balance, and learning opportunities rather than base salary alone. If you hold a generic title, invest in specialization—Motion Planning Engineers and ML Engineers command consistent $200k+ across industries, while generic titles swing wildly from $113k to $185k depending on context.

If switching from hardware to software track mid-career, commit to systematic skill development. You need the full stack (Python, C++, ML frameworks, domain expertise) and portfolio projects demonstrating capability, not just courses. This is a 12-18 month repositioning effort, not a weekend bootcamp. Alternatively, optimize within the hardware track by targeting companies and industries that pay well for those skills—Aerospace & Defense pays $170k for Controls Engineers versus $109k in industrial manufacturing.

If You’re Hiring:

Market rate varies 2x based on role selection. Software-focused roles (ML Engineer, Motion Planning Engineer) command $189k-$205k while hardware-focused roles (Technician, Field Service) run $69k-$127k. Don’t underprice specialized roles—Motion Planning Engineers expect $205k, and lowballing at $150k will eliminate your candidate pool before interviews start.

Industry compensation expectations vary dramatically. Transportation/AV candidates expect $200k+ base salaries because that’s market rate in their sector. If you’re in manufacturing or academia offering $110k-$120k, you’re competing for a different talent pool—either adjust compensation or target candidates from similar industries who understand your constraints.

California candidates have 46% of the US market to choose from locally—you’re competing on compensation, mission, and growth opportunity, not just job availability. If you can’t match California salaries ($193k), emphasize remote flexibility, equity upside, work-life balance, or technical depth that large companies can’t provide. Premium companies (NVIDIA $270k, Waymo $232k) set the compensation ceiling that filters down through the market.

Skill requirements should reflect actual role needs. Don’t list Python, C++, ML, and computer vision for a Controls Engineer role—that’s a Software Engineer job description. Similarly, don’t expect $120k to attract candidates with the full software stack when market rate for those skills is $180k-$200k. Match compensation to realistic skill requirements, or adjust requirements to match budget.

Related Resources

Compare hiring data: See our Robotics Hiring Report for analysis of 2,700+ active positions. Discover top companies hiring, in-demand skills, and geographic distribution. While this Salary Guide reveals what roles pay, the Hiring Report shows where the opportunities are.

Explore current opportunities: Browse 2,700+ active robotics positions across all roles, industries, and locations. Filter by your target role, required skills, and location preferences.

Currently 2,700+ active positions • 907 jobs with salary data analyzed for this report