Salary guide
| Measure | Annual pay (USD) |
|---|---|
| 25th percentile | $206,000 |
| Median | $242,000 |
| 75th percentile | $278,000 |
| Lowest listing | $100,000 |
| Highest listing | $560,000 |
| Listings counted | 307 |
| Level | Listings | Median pay |
|---|---|---|
| Mid-level | 19 | $260,000 |
| Senior | 101 | $232,000 |
| Country | Currency | Listings | Median pay | Middle half |
|---|---|---|---|---|
| United States | USD | 283 | $244,000 | $207,000 to $280,000 |
| Canada | USD | 12 | $232,000 | $182,000 to $255,000 |
Own the design and delivery of production ML systems, applying LLMs, deep learning, graph neural networks, and classical ML techniques to solve complex customer experience problems.
Own the full IDV ML stack, including document authenticity models, 1:1 and 1:N face-match, liveness detection, presentation-attack detection, and deepfake/injection detection from feature pipeline through threshold tuning and production enforcement.
At Anduril, our Battlespace Awareness Command and Control Software team specializes in solving complex, real-world problems through cutting-edge algorithms and intelligent software integrations.
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
Midjourney · 1mo ago
Own the tissue-class segmentation and labeling models for the ultrasound CT clinical analysis layer, and the pipelines that make them trainable and verifiable.
Job Description This role is a hands-on builder for CELSTARTAB’s portfolio companies, focused on developing AI/ML systems and data platforms that unlock new value from data.
ClickUp is seeking an experienced Machine Learning Engineer to join our Search team. You'll own the ML systems that power search relevance for millions of users and ground our AI in the right context.
We are looking for an AI/ML Engineer with strong machine learning fundamentals and hands-on experience in model development, training, and evaluation.
The AI Insights team builds customer-facing AI capabilities across CoreWeave’s Mission Control portfolio.
As a team, our Machine Learning Engineers broadly work across two areas: ML Research Engineering , focused on the systems and frameworks researchers use to train, evaluate and iterate on models at scale; and ML Performance Engineering , focused on making ML workloads execute as…
Own the machine learning function at an early-stage AI company, shaping post-training and agent systems from research through production. You will help set technical priorities and build the foundations for the ML team as it grows.
Capital OneNew · 1d ago
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
Our customers increasingly need those same models on hardware we don't control: non-NVIDIA accelerators, edge servers, and embedded platforms with their own inference runtimes, operator sets, and constraints.
We’re hiring an ML Engineer (P3) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data & ML organization within Engineering.
Roblox · 6d ago
A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.
You have trained LLMs to be SOTA on specific tasks. You have opinions on whether sequence-level or token-level importance ratios are more effective. You probably shared the ScaleRL paper in your group chats, and kicked off a few ablations after you read it.
Broadcom · 29d ago
The Central Engineering Group is seeking an energetic, visionary, and passionate Infrastructure AI/ ML Engineer to lead the Agentic AI Infrastructure development workflow for memory design, characterization, and yield optimization.
The Payroll AI and Data team is helping transform how payroll is processed for Workday customers around the world. Payroll touches nearly everyone who works, and the data behind it is complex, time-sensitive, and deeply important.
You will have access to the best sensor data in the world and an incredible infrastructure for testing and validation to bring new algorithms into our robotics stack.
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
The AI/ML Engineering team builds and operates ClickHouse's AI and machine learning products end-to-end.
The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform.
Airbnb · 4mo ago
The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform.
Join the Inference Engine team to enhance AI inference performance. Work with state-of-the-art technologies in a dynamic environment.
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
Rocket Lab · 7d ago
Develop computer vision algorithms and deep neural networks for image and video processing chains. Collaborate with other researchers/engineers on artificial intelligence, machine learning, and computer vision.
Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems. Collect, analyze, and synthesize findings from data and build intelligent data-driven model. Write clean, efficient, and sustainable code.
Pacific Northwest National LaboratoryNew · 2d ago
At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.
The Trust team is responsible for developing the technology that helps protect our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards.
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
Own end-to-end machine learning initiatives from problem formulation and offline evaluation to production deployment and online A/B testing, driving measurable lifts in user engagement, retention, and trading activity.
Architect and deploy the multi-agent framework for agent assist, internal SOP execution, and secure LLM-powered actions, building scalable systems that handle millions of customer interactions.
Own the design and reliability of ML inference infrastructure serving both predictive models and LLMs, maintaining high availability and low latency at scale. Build and optimize low-latency streaming pipelines that deliver fresh, high-quality feature data to production ML models.
the role and team · 5d ago
Operating across key business areas like grocery and food, the Consumer Incentives team drives Uber's growth and long-term profitability.
Safety Benchmarking: Design and maintain safety benchmarks for large language and multimodal models, translating risk scenarios into test cases, datasets, and scoring rubrics.
Build and deliver innovative Metropolis AI solutions powered by Cosmos world foundation models, addressing real-world requirements in intelligent video analytics, perception, and physical AI.
Designing end2end solutions for Perception and AV stack to enable traffic signal detections across various driving environments from complex intersections to rural roads to highways.
Adobe Firefly’s Generative AI Services team is seeking a Senior Staff Machine Learning Engineer for our GenAI Services area.
The Payments org at Airbnb is responsible for everything related to settling money in Airbnb’s global marketplace and makes the Payment experience as delightful, magical, intuitive, and easy as possible.
Work closely with AI & ML application teams to design seamless workflows for continuous model training, inference, and monitoring. Lead technical execution, conduct code and design reviews, and author high-impact code for critical system paths.
As a Machine Learning Engineer at Sardine, you'll own the systems that make real-time fraud detection possible. Our data science team builds custom models for our clients, you build and run the platform they deploy onto, and the low-latency serving path those models score on.
Getting a model from a research notebook to a production API serving millions of requests is one of the hardest problems in AI.
Build Threat Detection Models: Design, train, and deploy ML models for ransomware detection, suspicious session identification, and user behavior analytics.
At Anduril, our Battlespace Awareness Command and Control Software team specializes in solving complex, real-world problems through cutting-edge algorithms and intelligent software integrations.
Our teams apply machine learning, optimization, and software engineering to solve complex problems for customers, shoppers, retailers, brands, and internal partners.
We are looking for a Senior Machine Learning Engineer with a strong Operations Research background to join the Service Availability & Routing team within Instacart's Logistics organization.
The median Machine Learning Engineer salary in our $100k+ listings is $242,000 a year, based on 307 live USD listings. The middle half of listings pays $206,000 to $278,000. These figures cover only listings that publish a salary of $100k+ or more, so they sit above pay for Machine Learning Engineer roles overall.
Individual $100k+ Machine Learning Engineer listings range from $100,000 to $560,000 a year. The 25th percentile of listing midpoints is $206,000 and the 75th percentile is $278,000.
Median pay by level in our $100k+ listings: mid-level $260,000 (19 listings), senior $232,000 (101 listings). Levels with fewer than 5 listings are left out.
The companies with the most open $100k+ Machine Learning Engineer listings are Waymo (39 listings, median $238,000), Reddit (19 listings, median $276,000), Capital One (19 listings, median $206,000), Zoox (13 listings, median $295,000) and NVIDIA (13 listings, median $204,000).
The figures come from the salary ranges employers publish in their own job listings, collected from company career pages and applicant-tracking systems. Each listing counts once, at the midpoint of its range. Only USD listings go into the headline figures, and listings that are closed or stale are removed. The data was last refreshed on November 12, 2026.
Yes. Each listing is tagged remote, hybrid or on-site, and remote roles carry a region (US only, EMEA, APAC or global), so you can filter the live job list to the arrangement you want.
United States
Canada
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