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About This Role
See yourself at Dataminr
Dataminr is searching for a visionary Senior Director of AI Engineering to lead and inspire the team of engineers building the AI and data engine powering Dataminr’s real\-time intelligence platform . This pivotal role demands a leader who can bridge the gap between cutting\-edge AI research and scalable, real\-world product deployment. You will drive technical strategies that translate complex AI, Deep Learning and Machine Learning innovations into tangible business impact. We are seeking a candidate with a strong technical foundation and a proven ability to deliver high\-quality, end\-to\-end AI solutions. You are passionate about tackling challenging AI problems, fostering collaboration across product and research teams, and empowering your team to build products that make a meaningful, real\-world difference. This US\-based role can be remote or based out of our New York City office.
AI Innovation at Dataminr
Working at Dataminr you’ll have the opportunity to tackle the most exciting trends in AI on a daily basis to power a revolutionary product that uncovers critical events around the world as they unfold.
Regenerative AI : our AI technology, ReGenAI, is a new form of generative AI that automatically regenerates real\-time Live Event Briefs as events unfold. Learn more here .
Agentic AI : we recently launched our Agentic AI capability, what we’re calling our Intel Agents, that autonomously generates critical context for our clients on real\-time events, threats, and risks allowing them to see the clearest, most accurate view of what’s happening on the ground. Learn more here
Multimodal AI: our platform detects events from many different types of data (images, video, sensor data, audio, and text in over 150 languages). Learn more here .
The opportunity
- Oversee all technical aspects of how different AI models’ outputs impact the quality of content produced by our AI platform. This includes working with PMs and different parts of the business to define proper performance requirements for different Deep Learning and Machine Learning models.
- Work closely with scientists, engineers, and product managers to drive and deliver state of the art solutions at scale in multiple areas relevant to real\-time detection of events in public data sources (NLP, CV, IR, Knowledge Graphs, etc).
- Define and track quality and cost metrics for models as well as for their integrated end\-to\-end performance in relation to key business goals.
- Hands\-on modification of workflows, evaluations, and outputs of models.
- Lead other engineers and scientists, directing changes to technical approaches.
- Excel in placing a human\-centered focus on the work (context, end\-user impact, etc), finding solutions that work in practice, and have significant impact.
What you bring
At Dataminr, we value you for who you are. We encourage you to apply for this role, even if you don't meet every qualification. Our candidates are reviewed on the basis of their skill and potential to succeed. 6 Bullets max
- Strong technical background required: graduate degree in Computer Science or Electrical Engineering.
- At least 7 years of industry experience as a software engineer in a cloud\-native environment (AWS, Kubernetes).
- At least 5 years experience leading software engineers in an AI team.
- Experience with deep learning frameworks, LLMs, agent harnesses, compound AI systems (PyTorch, Langfuse, LangGraph, Weaviate, A2A, etc)
- Hands\-on experience in developing and deploying Machine Learning/Deep Learning/LLMs at large scale in NLP, CV, IR, or a related field, preferably in a real\-time setting.
- Demonstrated track record of driving and delivering on multiple complex AI projects.
- Excellent communication skills.
- Deep understanding of research stages, and the ability to connect multiple complex technologies in end\-to\-end solutions.
\#LI\-BM
\#LI\-REMOTE
About Dataminr
At Dataminr, we are a mission driven team of talented builders, creators and visionaries who have real\-world impact on how organizations are able to respond to events. Dataminr’s groundbreaking, AI\-powered, intelligence platform provides organizations with the earliest signals of emerging risks, events, and threats before they unfold. Trusted by two\-thirds of the Fortune 50 and half of the Fortune 100, Dataminr’s platform analyzes billions of public data inputs spanning text, image, video, audio and sensor data across 150\+ languages, empowering our clients to stay one step ahead in an increasingly complex world where every second counts.
Founded in 2009, we have pioneered the world’s first real\-time event detection platform, long before the recent Gen AI ‘boom.’ Dataminr operates all around the world united by our passion to use AI for the greater good, be agents of positive change and put our technology into the hands of clients charged with the responsibility to keep organizations running and keep people safe.
As our employees focus on developing our revolutionary technology, we focus on our employees. Dataminr is proud to offer a variety of flexible work arrangements, offices all over the world to foster collaboration, generous PTO and sick leave, and more, as part of our competitive benefits package aimed at keeping all our employees happy and healthy. Explore all our benefits here .
We believe our differences give us strength. Our employees are empowered to be their best, authentic selves through various opportunities, such as our robust employee resource group (ERG) network, manager development programming, professional development funds, and more.
We serve a global community made up of many cultures and strive to reflect the world and clients we serve, with a workforce built on merit and equity. We actively condemn racism and discrimination in any form. We stand for social good, fostering a culture of allyship, and standing up for those who face systemic barriers to equality. We lead with empathy and strive to be agents of positive change in our company and in our communities.
The annual base salary range for this position is $229,600 \- $337,000\. You will also be eligible to receive a discretionary bonus and Company equity. Actual salary will be based on a number of factors including, but not limited to, geographic location, applicant skills, and prior relevant experience.
*Dataminr is an equal opportunity and affirmative action employer. Individuals seeking employment at Dataminr are considered without regards to race, sex, color, creed, religion, national origin, age, disability, genetics, marital status, pregnancy, unemployment status, sexual orientation, citizenship status or veteran status.*
*Dataminr will collect and process your personal data in accordance with Dataminr’s candidate privacy notice available* *here* *. By providing your details and applying via our careers website, you acknowledge that you have read our candidate privacy notice. If you have any queries, please contact* *[email protected]* *or* *[email protected]* *.*
*Notice Regarding Automated Employment Decision Tools in New York City*
Salary Context
This $229K-$337K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Dataminr, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($283K) sits 32% above the category median. Disclosed range: $229K to $337K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Dataminr AI Hiring
Dataminr has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $337K - $337K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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