Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
United States - New York
Live, first seen by us 85 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
Design and deploy large-scale ML systems for ad ranking and bid optimization
Build and operate production ML pipelines that serve predictions at low latency
Optimize training and inference for compute efficiency, latency, and cost
Partner with Data Science, Data Engineering, and product teams to ship models
Run online experiments to validate model impact
Design, build, and deploy large-scale ML systems for ad ranking, click-through and conversion prediction, bid optimization, and next-best-action recommendation.
Build and operate production ML pipelines — feature engineering, distributed training, deployment, retraining, and monitoring — that process billions of events and serve predictions at low latency.
Optimize training and inference for compute efficiency (CPU/GPU utilization), latency, and cost across distributed systems.
Partner closely with Data Science, Data Engineering, and product teams to ship models that move key business metrics
run rigorous online experiments to validate impact
- A record of shipping models that demonstrably improved product or business metrics (e.g., CTR, CVR, ROAS). - Master's or Ph.D. in CS, ML, or a related field, or equivalent practical experience. Nice to have
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
Day 85
Up longer than most. Similar jobs usually come down between day 7 and day 72.
From 4,777 similar postings that came down. Coming down is not always a hire.
6 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 8+ years building and deploying ML systems in production at scale, ideally in AdTech, recommender systems, search/ranking, or real-time bidding. - Strong software-engineering fundamentals — data structures, algorithms, system design — and proficiency in Python (plus Java, Scala, or C++ a strong plus). - Hands-on experience with modern ML frameworks (PyTorch, TensorFlow, or JAX) and distributed data/compute systems (Spark, Beam/Dataflow, Flink, or similar).
- Experience with real-time, low-latency model serving and online learning. - Familiarity with Databricks / Unity Catalog, semantic layers, or feature platforms. - Experience with LLMs and agentic systems applied to audience insight or optimization.
- Strong software-engineering fundamentals — data structures, algorithms, system design — and proficiency in Python (plus Java, Scala, or C++ a strong plus). - Hands-on experience with modern ML frameworks (PyTorch, TensorFlow, or JAX) and distributed data/compute systems (Spark, Beam/Dataflow, Flink, or similar). - Experience building robust ML pipelines: feature stores, data versioning, model monitoring, and automated retraining.
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New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
DT (NASDAQ: APPS) is unifying the full mobile experience. Our growth platform powers the mobile ecosystem by bringing together advertisers, app developers, and device partners, to create a unique and premium value exchange, that results in a personalized and seamless mobile device experience.
At DT, we move fast, think big, and build collaboratively. We believe great ideas can come from anywhere, and we trust our teams to take ownership, challenge convention, and make an impact from day one.
Here, you’ll have the opportunity to grow your career while contributing to products and experiences used by millions around the world.
With us, you will enjoy competitive compensation, equity awards, and a comprehensive benefits package designed to invest in your total well-being, all within a hybrid and flexible work environment.
DT is transforming from a distribution company into a data-intelligence company. At the center of that shift is our ML platform and the "Next Best Action" framework — the capability that lets every DT product activate user-level models in real time against our unified, first-party device-level data (the DT User Card).
As a Senior Machine Learning Engineer, you'll design and scale the ML systems behind ad ranking, bid optimization, recommendation, and user-level prediction across our demand and supply businesses — the DSP, offer wall, content media, notifications, and Ignite. You'll build the pipelines and serving infrastructure that turn our data advantage into measurable lifts in CTR, CVR, and ROI, and help DT show up as an AI-first company.
Design, build, and deploy large-scale ML systems for ad ranking, click-through and conversion prediction, bid optimization, and next-best-action recommendation.
Build and operate production ML pipelines — feature engineering, distributed training, deployment, retraining, and monitoring — that process billions of events and serve predictions at low latency.
Leverage the data assets from across the DT product portfolio to enable ML solutions that create decisioning in real time using the entire breadth of available features.
Optimize training and inference for compute efficiency (CPU/GPU utilization), latency, and cost across distributed systems.
Partner closely with Data Science, Data Engineering, and product teams to ship models that move key business metrics, and run rigorous online experiments to validate impact.
Stay current with applied ML research and bring innovative techniques to advertising, ranking, and audience modeling.
8+ years building and deploying ML systems in production at scale, ideally in AdTech, recommender systems, search/ranking, or real-time bidding.
Strong software-engineering fundamentals — data structures, algorithms, system design — and proficiency in Python (plus Java, Scala, or C++ a strong plus).
Hands-on experience with modern ML frameworks (PyTorch, TensorFlow, or JAX) and distributed data/compute systems (Spark, Beam/Dataflow, Flink, or similar).
Experience building robust ML pipelines: feature stores, data versioning, model monitoring, and automated retraining.
A record of shipping models that demonstrably improved product or business metrics (e.g., CTR, CVR, ROAS).
Master's or Ph.D. in CS, ML, or a related field, or equivalent practical experience.
Experience with real-time, low-latency model serving and online learning.
Familiarity with Databricks / Unity Catalog, semantic layers, or feature platforms.
Experience with LLMs and agentic systems applied to audience insight or optimization.
DT (NASDAQ: APPS) is a mobile growth platform that powers superior experiences and outcomes for advertisers, app developers, carriers, OEMs, and publishers worldwide.
Our technology connects brands and apps with consumers across devices, apps, and mobile environments — helping partners drive awareness, acquisition, engagement, and monetization through intelligent, integrated experiences.
Headquartered in Austin, Texas, DT operates globally across North America, EMEA, APAC, and LATAM.
DT is an equal opportunity employer committed to fostering an inclusive and diverse workplace. We welcome people of different backgrounds, experiences, abilities, and perspectives, and believe teams with different points of view build better products, ideas, and experiences.
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United States - New York
Here, you’ll have the opportunity to grow your career while contributing to products and experiences used by millions around the world. With us, you will enjoy competitive compensation, equity awards, and a comprehensive benefits package designed to invest in your total well-being, all within a hybrid and flexible work environment. The Role
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