How Much Do Machine Learning Engineers Make? (2025 Guide for HR & Compensation Teams)

Introduction

This guide provides U.S. compensation benchmarks, pay drivers, and salary structuring strategies for machine learning engineers in 2025. It is designed for HR professionals, talent acquisition teams, and compensation leaders who need up-to-date market data to attract and retain top AI talent. As machine learning becomes central to business strategy, understanding current pay trends is critical for competitive hiring.

Machine learning engineers in the U.S. typically earn $130,000-$190,000 in base salary as of 2025, with total compensation ranging from $160,000-$260,000 depending on experience level, location, and industry. This guide will help you understand what drives these numbers, how to structure competitive offers, and how to benchmark roles using real-time data.

U.S. Machine Learning Engineer Salary Ranges by Level (2025)

Level	Base Salary Range	Total Compensation Range
Entry/Junior	$110,000–$145,000	$120,000–$170,000
Mid-Level	$145,000–$190,000	$160,000–$230,000
Senior	$185,000–$230,000 (up to $260,000 in top hubs)	$220,000–$320,000 (up to $400,000+ with equity/bonuses)
Staff/Principal	$220,000–$280,000+ (up to $300,000+ in top firms)	$350,000–$500,000+ (with equity/bonuses)
Note: These figures reflect national U.S. data and may vary by geography, industry, and company size.

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Key Takeaways

• Machine learning engineers in the U.S. typically earn $130,000-$190,000 in base salary as of 2025, with total compensation ranging from $160,000-$260,000 depending on experience level, location, and industry.

• Geographic location significantly impacts pay, with tech hubs like San Francisco, Seattle, and NYC offering 20-40% premiums over national averages.

• Senior and staff-level ML engineers at major tech companies can earn $300,000-$500,000+ in total compensation when including equity and bonuses.

• Traditional annual salary surveys often lag 6-18 months behind the rapidly evolving ML engineer market, making real-time compensation data essential for competitive offers.

• Skill specializations in areas like large language models (LLMs: Large Language Models, advanced AI models trained on vast text data), deep learning, and MLOps (Machine Learning Operations: the practice of streamlining and automating the deployment and maintenance of machine learning models in production) command premium pay in today’s AI-driven market.

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What Does a Machine Learning Engineer Do—and Why Are They So Highly Paid?

The Role of a Machine Learning Engineer

Machine learning engineers represent a unique intersection of software engineering, data engineering, and applied machine learning expertise. From an employer’s perspective, these professionals don’t just build models—they create production systems that directly impact revenue, risk management, and operational efficiency.

ML engineers take prototype models or research code developed by data scientists and transform them into scalable, secure, monitored services that run reliably in production environments.

How ML Engineers Differ from Related Roles

• Data Scientists focus on model development and experimentation.

• Machine Learning Engineers bridge the gap by designing infrastructure, pipelines, and deployment systems to make machine learning capabilities accessible to end users and business processes.

• Applied Scientists, Research Engineers, and Research Scientists often emphasize developing new algorithms, publishing research, and advancing state-of-the-art capabilities. These roles may command similar compensation but focus more on research, while ML engineers focus on productionization, reliability, and scale.

Core Responsibilities of Machine Learning Engineers

• Data pipeline design and maintenance: Building reliable data ingestion and feature engineering pipelines, often collaborating with data engineering teams to ensure model training and inference have consistent, high-quality inputs.

• Production model deployment: Implementing MLOps best practices (Machine Learning Operations: streamlining and automating deployment/maintenance of ML models), including continuous integration/continuous deployment (CI/CD) for machine learning, automated retraining pipelines, model performance monitoring, and rollback strategies.

• Feature engineering at scale: Developing and optimizing feature generation systems using distributed computing platforms and cloud infrastructure.

• Model training and optimization: Managing the full model lifecycle from training and hyperparameter tuning to evaluation and versioning using modern frameworks.

• Experimentation and validation: Designing and implementing A/B tests and multi-armed bandit experiments to measure business impact and validate model improvements.

• Cross-functional collaboration: Working closely with product management, software engineering, analytics, and compliance teams to integrate ML capabilities into user-facing products and internal tools.

Why Are ML Engineers Highly Paid?

• Business Impact: ML engineers build systems that become core revenue infrastructure or mission-critical risk controls.

• Scarcity of Talent: The combination of advanced quantitative skills, production-grade software engineering, business acumen, and accountability for high-stakes outcomes is rare.

• Strategic Value: In industries like finance, healthcare, and e-commerce, ML engineers directly influence revenue, risk, and operational efficiency.

With this context, let's examine current compensation trends for machine learning engineers in the U.S. in 2025.

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How Much Do Machine Learning Engineers Make in the U.S. in 2025?

National Compensation Overview

All compensation figures in this section reflect U.S.-only data drawn from 2024-2025 market sources including industry salary surveys, compensation platforms, and verified offer data. However, given how rapidly machine learning engineer compensation evolves—particularly in the post-2023 generative AI acceleration period—these numbers should be validated against real-time market data using tools like Bigfoot Live before making final compensation decisions.

National Averages

• Base Salary (Mid-Level ML Engineer): $145,000–$165,000

• Total Cash Compensation (Mid-Level): $160,000–$210,000 (includes bonuses and cash incentives, excludes equity)

• Overall Compensation Band (Junior to Principal): $110,000 (entry-level, moderate-cost markets) to $350,000+ (senior/staff, large tech firms/high-growth startups)

• Elite Principal/Staff Roles: $400,000–$500,000+ (with equity and bonuses)

Compensation by Employer Type

• Product-centric technology companies: Pay at or above the upper end of national ranges. Mid-level ML engineers: $150,000–$190,000 base, $220,000–$300,000+ total compensation in major tech hubs.

• Traditional enterprises: Often pay 10–25% below tech-forward benchmarks for cash compensation, especially for mid-level roles.

• Smaller startups/early-stage companies: Lower base salaries (near national midpoints), but higher equity percentages. Mid-level base: $130,000–$170,000.

Recent Benchmark Data

• Average base salaries: $157,000–$162,000

• Additional cash compensation: $44,000–$50,000

• Median total compensation (with equity): $250,000–$295,000 (higher-paying market segments)

Transition

Next, let's break down machine learning engineer pay by seniority level to help you structure competitive offers and internal bands.

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Machine Learning Engineer Pay by Seniority Level

Level-driven compensation structures are particularly critical for machine learning engineer roles because technical capabilities and business impact compound rapidly with experience. The total compensation gap between a fresh graduate ML engineer and a staff-level machine learning engineer commonly exceeds $150,000–$250,000 per year, especially in top technology hubs.

Entry/Junior Machine Learning Engineer (0–1 years experience)

• Base salary range: $110,000–$145,000

• Total compensation range: $120,000–$170,000

Typical responsibilities:

• Implementing well-defined model features

• Writing data preprocessing code

• Contributing to model training pipelines

• Building smaller services within established architectures

Mid-Level Machine Learning Engineer (2–5 years experience)

• Base salary range: $145,000–$190,000

• Total compensation range: $160,000–$230,000

Typical responsibilities:

• Owning end-to-end ML features or services

• Translating business requirements into model solutions

• Designing data pipelines, training/validating models, implementing APIs

• Integrating with product or operational systems

Senior Machine Learning Engineer (5–9 years experience)

• Base salary range: $185,000–$230,000 (nationally), $210,000–$260,000 (top-tier hubs)

• Total compensation range: $220,000–$320,000, up to $400,000+ (with equity/bonuses)

Typical responsibilities:

• Owning critical ML systems/platforms end-to-end

• Designing architecture for ML services

• Leading experimentation strategies

• Mentoring junior team members

• Influencing product roadmaps

Staff/Principal/Lead Machine Learning Engineer (8–12+ years experience)

• Base salary range: $220,000–$280,000+, $250,000–$300,000+ (top-tier firms/hubs)

• Total compensation range: $350,000–$500,000+ (with equity/bonuses)

Typical responsibilities:

• Acting as architects/technical leaders for ML across product areas/business units

• Designing organization-wide ML platforms

• Setting technical standards

• Leading multi-team initiatives

• Influencing product strategy and long-term technical roadmaps

Related Roles

• Applied Scientist, Research Engineer, Research Scientist: These roles often command similar compensation to ML engineers at equivalent seniority, but focus more on research and algorithm development rather than productionization and scale.

Transition: Now that you understand pay by level, let's explore how location, industry, and specialized skills further influence machine learning engineer compensation.

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Location, Industry, and Skill Premiums for ML Engineers

Machine learning engineer compensation varies dramatically based on geography, industry sector, and specialized technical skills—creating 30–70% pay spreads for roles with similar titles and responsibilities.

Geographic Pay Differentials

• High-cost tech hubs: San Francisco Bay Area, New York City, Seattle, Boston (20–40% higher base salaries than national medians)

• Secondary tech hubs: Austin, Denver, Atlanta, Raleigh ($180,000–$220,000 for senior ML engineers)

• Lower-cost regions: $160,000–$200,000 for senior ML engineers

Remote roles: Some employers index remote salaries to employee location (using tiered structures), while others anchor to a reference market rate (e.g., “U.S. national” or “San Francisco-adjusted”).

Industry Compensation Clustering

• Consumer/Enterprise Tech & AI-Native Startups: Highest cash compensation and equity packages.

• Finance/Fintech: Strong cash compensation, large performance bonuses for risk-sensitive/latency-critical roles.

• Healthcare/Life Sciences: Competitive, but often slightly below top tech tiers.

• Traditional Enterprise/Industrial: Historically lower, but rising post-2023 as AI becomes strategic.

Technical Skill Premiums

• Deep Learning & Modern Architectures: Experience with transformers, LLMs (Large Language Models: advanced AI models trained on vast text data), diffusion models, and neural networks.

• Large-Scale Recommendation Systems: Specialized knowledge in algorithms and distributed infrastructure.

• Natural Language Processing & LLMs: Building/fine-tuning LLM-based systems (e.g., chatbots, summarization).

• MLOps & ML Platform Engineering: Designing/operating internal ML platforms, feature stores, real-time inference services, and automated retraining pipelines.

• Computer Vision in Regulated Contexts: Automotive, medical imaging, industrial inspection.

Skill premiums: ML engineers with production LLM or large-scale recommender system experience regularly command 10–25% higher cash compensation.

Transition: Understanding these variables, let's look at the key factors that drive machine learning engineer compensation.

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Key Factors That Drive Machine Learning Engineer Compensation

Machine learning engineer salaries reflect more than coding proficiency or years of experience. Compensation decisions depend on a mix of educational background, demonstrable business impact, technical depth, domain expertise, leadership capability, and responsible AI practices.

Education and Credentials

The educational baseline for most ML engineer roles includes a bachelor’s degree in computer science, electrical engineering, mathematics, statistics, or a related field. Many professionals also hold master’s or Ph.D. degrees, especially in research-heavy organizations.

• Impact on pay: Advanced degrees may yield higher entry-level salaries, but compensation differences compress at mid/senior levels. Performance and business impact become dominant factors.

Experience and Demonstrable Impact

Employers focus on:

• Production ML system ownership: Number/complexity of systems deployed and maintained in production.

• Documented business impact: Quantified results (e.g., revenue uplift, cost savings, risk reduction).

• Full lifecycle expertise: Experience across the ML workflow (discovery, modeling, A/B testing, deployment, monitoring, improvement).

Tip: Use structured leveling guides that define career levels by scope and business outcomes, not just tenure.

Technical Depth and Breadth

Key technical competencies:

• Modern Deep Learning Proficiency: PyTorch, TensorFlow, JAX, transformers, sequence models, graph neural networks, generative models.

• Cloud-Native ML Engineering: Training/inference pipelines on AWS, GCP, Azure; Kubernetes, serverless inference, distributed computing.

• MLOps and DevOps Integration: CI/CD for model code, automated data validation, model registry management, drift detection, canary deployments, automated rollback.

• End-to-End Pipeline Design: Architecting workflows from raw data capture to real-time inference.

Professional Certifications

• Examples: AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure AI Engineer Associate, Google Cloud Professional Machine Learning Engineer.

• Impact: May provide modest pay differentiation at entry/early-career levels, but rarely substitute for production experience at senior levels.

Leadership and Strategic Influence

Upper tiers of compensation require:

• Technical leadership: Leading ML teams, cross-functional groups, or platform initiatives.

• Mentorship: Developing junior/mid-level ML engineers and data scientists.

• Product/strategic influence: Shaping product strategy, defining ML roadmaps, prioritizing AI investments.

• Responsible AI advocacy: Promoting model reliability, fairness, explainability, bias mitigation, and regulatory compliance.

Transition: With these factors in mind, let's discuss how HR and compensation teams should structure ML engineer pay ranges.

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How HR and Compensation Teams Should Structure ML Engineer Pay Ranges

Translating market data into effective compensation architecture requires systematic approaches that account for market volatility, internal equity, and strategic talent objectives.

Range Construction and Market Positioning

• Target market positioning: Decide whether to pay at the 50th percentile (median) or higher (65th–75th percentile) based on talent strategy.

• Salary range structure: Build three-part salary ranges (minimum, midpoint, maximum) for each level (e.g., ML Engineer I, II, Senior, Staff). ML roles may justify wider ranges (30–50% width) due to rapid skill/market evolution.

Example: If the Senior ML Engineer median base in San Francisco is $230,000 and the 75th percentile is $250,000, set a midpoint at $240,000 and a 40% width range ($204,000–$276,000).

Level Mapping and Internal Alignment

• Integrate ML engineers into existing technical frameworks: Map ML levels to standard engineering tracks (e.g., Junior ≈ L3, Mid ≈ L4, Senior ≈ L5, Staff ≈ L6, Principal ≈ L7+).

• Align titles: Calibrate “Applied Scientist,” “ML Engineer,” and “Data Scientist” using clear job descriptions and market benchmarks.

Tool tip: Job Description Studio helps standardize role definitions and connect them to market data.

Geographic Differentiation Strategy

• Establish formal geographic tiers:

  • Tier 1: SF Bay Area, NYC, Seattle, Boston (100% reference)

  • Tier 2: Austin, Denver, Chicago, LA, DC, Atlanta (90–95%)

  • Tier 3: Other U.S. markets/remote (80–90%)

• Set differentials based on market pricing, not just cost-of-living.

Hybrid and Blended Role Pricing

• Hybrid role pricing: For roles combining ML engineering with other functions (e.g., data platform, analytics, MLOps, research), use blended benchmarks. Weight each component by time allocation to derive composite market values.

Definition: Hybrid role pricing is the practice of combining compensation benchmarks from multiple job families to accurately price roles that span more than one discipline.

Tool tip: SalaryCube’s DataDive Pro supports hybrid role pricing workflows.

Operational Pay Structure Guidelines

• Compa-ratio targets: (Compa-ratio is the ratio of an employee’s current pay to the market midpoint for their role.) Define where performers should fall within ranges (e.g., solid performers at 0.95–1.05, strong performers at 1.1–1.2).

• Merit increase frameworks: (Merit increase refers to pay raises based on performance.) Set annual raise guidelines based on performance and current range position.

• Promotion adjustments: Promotions between ML levels often warrant 10–20% base increases plus equity refreshers.

Tool tip: SalaryCube’s free compensation tools include compa-ratio calculators and merit increase planners.

Transition: To keep up with the fast-moving market, let's look at how to use real-time data to benchmark ML engineer salaries and avoid survey lag.

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Using Real-Time Data to Benchmark ML Engineer Salaries (and Avoid Survey Lag)

Traditional salary surveys often lag behind the fast-evolving ML engineer market, sometimes by 6–18 months. This can result in under-budgeting, failed offers, and internal equity issues.

SalaryCube’s Real-Time Approach

• U.S.-focused, daily-updated data: Bigfoot Live provides daily updates on ML engineer salary benchmarks.

• No survey participation required: Access current benchmarks without submitting data or waiting for annual cycles.

• Self-service workflows: Generate current market benchmarks in minutes for time-sensitive offers and planning.

Five-Step ML Engineer Benchmarking Workflow

1. Role Standardization with Job Description Studio

   • Input your ML engineer job description into Job Description Studio (a tool that helps clarify role responsibilities and map positions to benchmark families).

   • Identify critical skill tags (e.g., NLP, LLMs, recommendation systems, MLOps, cloud platforms).

2. Market Benchmarking with DataDive Pro

   • Use Salary Benchmarking (DataDive Pro) (a platform for filtering and analyzing compensation data) to filter by level, skills, location, and industry.

   • Generate current market snapshots for base salary, bonus, and total compensation.

3. Multi-Dimensional Analysis and Reporting

   • Create market cuts for geographic, industry, and level comparisons.

   • Export results as CSV, Excel, or PDF for integration with internal models.

4. Strategic Positioning and Range Development

   • Set target percentile positioning (e.g., 65th percentile for Tier 1 markets).

   • Construct salary ranges with appropriate minimum, midpoint, and maximum levels.

5. Documentation and Methodology Transparency

   • Document assumptions and methodology using SalaryCube’s resources.

   • Create audit-ready compensation packets with role definitions, market data, rationale, and approval workflows.

Transition: With these benchmarking strategies, you can ensure your compensation decisions are current and defensible. Next, let's address common employer questions about machine learning engineer compensation.

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FAQ: Machine Learning Engineer Compensation for Employers

How often should we refresh our machine learning engineer salary ranges?

• Recommendation: Annual reviews are the minimum. Many teams conduct mid-year or quarterly spot checks for high-demand roles.

• Tip: Real-time platforms like SalaryCube enable continuous monitoring and targeted adjustments when market medians move ±5–10%.

Should we pay machine learning engineers more than software engineers at the same level?

• Typical practice: ML engineer cash compensation often aligns with or slightly exceeds software engineer pay, especially where ML drives core business value.

• Approach: Align career frameworks first, then use current market data to determine differentials.

How do we handle pay equity for ML engineers hired in different years at different market rates?

• Solution: Conduct periodic internal equity reviews using current benchmarks, identify underpaid employees, implement structured market adjustments, and communicate clearly about range updates.

What’s the best way to benchmark hybrid ML roles like “ML Engineer + Data Scientist”?

• Best practice: Use blended benchmark methodologies—break down responsibilities by time percentage, benchmark each component, and weight accordingly.

• Tool tip: SalaryCube’s hybrid role pricing workflows are designed for this use case.

How can we justify high ML engineer salaries to finance and leadership?

• Strategies:

  • Connect ML compensation to measurable business impact (revenue, cost, risk).

  • Provide defensible external benchmarks from SalaryCube.

  • Emphasize the cost of underpaying (hiring delays, turnover).

  • Document methodology and rationale for specialized skill premiums.

For HR and compensation leaders recalibrating ML engineer ranges or developing AI talent strategies, booking a SalaryCube demo or watching interactive demonstrations is the most efficient way to see real-time compensation intelligence workflows in action.