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MLOps vs Applied ML Career Path Comparison

Compare MLOps vs Applied ML career paths: ESTIMATED salaries, job growth, and skills. Data-driven insights for AI/ML professionals.

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Showing — rows ★ Estimates only — see methodology below
Role ⇅Career Level ⇅Average Salary (ESTIMATE) ⇅Salary Range (ESTIMATE) ⇅Job Growth (ESTIMATE) ⇅Common Industries ⇅Top Skills ⇅Job Postings (ESTIMATE) ⇅

Choosing between an MLOps vs applied ML career path can feel overwhelming, especially when weighing factors like salary expectations, job growth, required skills, and industry demand. This comparison tool breaks down key distinctions between these two high-demand roles using ESTIMATED data from public sources like Glassdoor, LinkedIn Talent Insights, Levels.fyi, and the Bureau of Labor Statistics. Whether you're a new graduate, mid-career professional, or considering a pivot, understanding these nuances can help you make an informed decision.

MLOps (Machine Learning Operations) focuses on the deployment, monitoring, and scaling of ML models in production environments. Professionals in this field work closely with software engineers, DevOps teams, and data scientists to ensure models are reliable, efficient, and integrable into real-world applications. Applied ML roles, on the other hand, prioritize model development, experimentation, and research, often involving deep learning, NLP, computer vision, or statistical analysis. While there is overlap—many applied ML scientists also work with pipelines and deployment—the core focus and day-to-day responsibilities differ significantly.

Salary is a critical factor in this comparison. According to ESTIMATED data, entry-level MLOps engineers earn roughly $80K–$110K, while applied ML scientists at the same level average $85K–$115K. At senior levels, MLOps engineers may outearn their applied ML counterparts ($170K vs. $180K on average), though compensation varies widely based on industry, location, and company size. Tech hubs like the SF Bay Area or NYC tend to offer higher salaries but come with a higher cost of living. Remote roles are increasingly competitive, with companies offering salaries comparable to in-person positions, especially for specialized roles.

Job growth is another key consideration. LinkedIn Talent Insights and the U.S. Bureau of Labor Statistics project faster growth for MLOps roles (estimated 12–15% annually) due to increasing demand for scalable, production-ready AI systems. Applied ML roles are also growing but at a slightly slower rate (8–12% estimated), as companies refine their models and shift toward implementation over pure research. The data suggests that while applied ML remains a robust career path, MLOps roles may offer greater stability and more numerous opportunities in industries like finance, healthcare, and e-commerce.

Finally, skill requirements diverge between the two paths. MLOps professionals need expertise in CI/CD pipelines, Kubernetes, cloud platforms (AWS/GCP/Azure), and monitoring tools, along with foundational knowledge of ML. Applied ML scientists require deep knowledge of Python, deep learning frameworks (TensorFlow, PyTorch), statistics, and algorithm design. Hybrid roles are emerging, but most professionals still specialize in one area.

Use this tool to explore the MLOps vs applied ML career path comparison in depth, filtering by career level, industry, or skill set to align your goals with market realities. For additional insights, explore our career resources linked below.

How It Works

This table compares career trajectories between MLOps and applied ML roles using ESTIMATED salary ranges, job growth rates, industry demand, and skill requirements. Filter by career level or industry to tailor the comparison to your situation. All numeric data is labeled as ESTIMATE—see the methodology note for details on sources and limitations.

Methodology Note

All numeric data in this tool is ESTIMATED and derived from aggregated public sources, including:

  • Glassdoor and Levels.fyi (salary ranges)
  • LinkedIn Talent Insights (job postings, growth rates)
  • U.S. Bureau of Labor Statistics (employment projections)
  • Industry reports from O'Reilly, McKinsey, and Gartner (trends, skills demand)

Salary figures are U.S.-centric (or adjusted for purchasing power in globally remote roles) and represent base compensation. Bonuses, equity, and benefits are not included. Job growth percentages are annualized estimates based on 5-year projections. Industry and skill tags are derived from common job descriptions and may not reflect every organization's specific requirements.

Data was last refreshed in Q2 2024. For precise comparisons, consult company-specific postings or salary surveys.

Frequently Asked Questions

Which career path pays more: MLOps or applied ML?
Salaries vary by experience, location, and industry, but ESTIMATED data suggests that senior MLOps engineers may earn slightly more than applied ML scientists ($170K vs. $180K average). However, applied ML roles at FAANG companies or in research can exceed $200K. Bonuses, equity, and cost-of-living adjustments also impact total compensation.
Is MLOps easier to break into than applied ML?
MLOps roles may have a lower barrier to entry for professionals with software engineering or DevOps experience, as some companies prioritize pipeline experience over advanced ML research. Applied ML roles often require a stronger background in statistics, linear algebra, and model development, especially for research-heavy positions.
What industries hire the most MLOps vs applied ML professionals?
MLOps roles are in high demand in tech, finance, healthcare, and e-commerce, where scalable ML systems are critical. Applied ML scientists are more prevalent in tech, research, advertising, and consulting, where model development and experimentation are prioritized.
Can I switch from applied ML to MLOps (or vice versa)?
Yes! Many skills overlap, such as Python, cloud platforms, and basic ML knowledge. To switch to MLOps, focus on CI/CD, Kubernetes, and monitoring tools. To move into applied ML, deepen your knowledge of deep learning frameworks, statistics, and algorithm design. Hybrid roles are increasingly common.
How does remote work impact salaries in MLOps vs applied ML?
Remote salaries are often 5–10% lower than in-person roles in high-cost areas (e.g., SF or NYC), but many companies offer competitive packages regardless of location. MLOps roles, which are more tied to infrastructure, may have slightly more remote opportunities than research-focused applied ML positions.
What are the biggest challenges in each career path?
MLOps professionals often face operational challenges, such as debugging production models, managing latency, and ensuring compliance. Applied ML scientists deal with experimental bottlenecks, such as model drift, interpretability, and reproducing research results. Both roles require collaboration but prioritize different skill sets.
Are there more job openings for MLOps or applied ML?
ESTIMATED data from LinkedIn Talent Insights suggests there are slightly more openings for MLOps roles (e.g., 5,000+ vs. 4,500+ for mid-level positions), likely due to the growing need for scalable, production-ready AI systems. However, applied ML remains highly competitive, especially in research and big tech.
Which path is better for career growth in 5–10 years?
Both paths offer strong career growth, but MLOps may provide more managerial opportunities due to its overlap with software engineering and DevOps. Applied ML can lead to specialized research or executive roles (e.g., Chief AI Scientist) but may require advanced degrees for top positions. Long-term success depends on your interests and willingness to adapt.
Career Resources

Navigate Your AI/ML Career with Confidence

Explore our curated collection of e-books, templates, and interview guides designed to help you land top roles in MLOps, applied ML, and beyond. From salary negotiation to technical preparation, we’ve got you covered.

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