What are the responsibilities and job description for the Senior ML/AI Engineer_Hybrid (NYC) position at PulseRise Technologies?
Dear applicants, please note that applications without salary expectations and an active LinkedIn profile will not be considered.
We are looking for a Senior ML/AI Engineer to build and deploy the intelligent systems at the core of an applied AI and data analytics platform. This is not research for the sake of papers — it's production systems that reason, forecast, and act autonomously across complex enterprise data landscapes. You will develop the models and agentic architectures that power demand forecasting, consumer intelligence, competitive analysis, and autonomous decision-making. The same bar as every role on this team: senior enough to think deeply, but still energized by hands-on implementation. High agency, low ego, great communicator.
Details
Schedule: Full-time
Location: Hybrid (NYC)
Type of collaboration: Full-time employment
The platform connects an organization's entire data landscape — internal systems, social media trends, industry reports, consumer behavior signals — into a single coherent intelligence layer that surfaces insights and automates workflows that used to take analysts weeks. At its core is a production graph RAG system connecting temporal and sentiment data at enterprise scale — a key technical differentiator. You will work at the intersection of applied ML, agentic AI, and graph-based reasoning. The company runs experiments at the fringes of modern technology — ML, graph databases, agentic AI — and wants engineers who share the drive to stay at the frontier and turn innovation into real product value. This role spans prototype to production, and everything in between.
You have
5 years of experience in applied machine learning and AI, with models deployed and running in production
M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field — or equivalent practical experience (what you've built matters more than the degree)
Deep proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, scikit-learn)
Strong background in statistical analysis, predictive modeling, and time series forecasting
Experience with applied agentic AI/ML systems and multi-agent orchestration
Experience with NLP, LLMs, and RAG architectures
Comfort working with large-scale datasets and distributed computing environments
Nice to have
Graph database or graph RAG experience (a major plus — core to the stack)
Background in retail, supply chain, or demand forecasting domains
Experience with graph neural networks or knowledge graphs
Familiarity with MLOps platforms and model serving infrastructure
Contributions to open-source ML/AI projects or published research
What To Do
Design, build, and deploy ML models for demand forecasting, time series prediction, consumer sentiment analysis, and anomaly detection at enterprise scale
Develop and iterate on the agentic AI architecture — building systems that reason across heterogeneous data sources and take autonomous action
Build and maintain robust ML pipelines: data preprocessing, feature engineering, model training, evaluation, and production deployment
Architect and improve the production graph RAG system
Build RAG systems and LLM integrations that power natural language interfaces and autonomous workflows
Collaborate with backend engineers to ensure models are production-grade — optimized for latency, reliability, and scale
Own model performance end-to-end: monitoring, retraining, and continuous improvement in production
Stay at the frontier of AI research and bring relevant innovations into the platform
Interview process
Recruiter screen
Intro call with engineering leadership
Technical screen with a senior engineer
On-site: ML coding, system design, product sense, AI sense, and a meeting with co-founders
We are looking for a Senior ML/AI Engineer to build and deploy the intelligent systems at the core of an applied AI and data analytics platform. This is not research for the sake of papers — it's production systems that reason, forecast, and act autonomously across complex enterprise data landscapes. You will develop the models and agentic architectures that power demand forecasting, consumer intelligence, competitive analysis, and autonomous decision-making. The same bar as every role on this team: senior enough to think deeply, but still energized by hands-on implementation. High agency, low ego, great communicator.
Details
Schedule: Full-time
Location: Hybrid (NYC)
Type of collaboration: Full-time employment
The platform connects an organization's entire data landscape — internal systems, social media trends, industry reports, consumer behavior signals — into a single coherent intelligence layer that surfaces insights and automates workflows that used to take analysts weeks. At its core is a production graph RAG system connecting temporal and sentiment data at enterprise scale — a key technical differentiator. You will work at the intersection of applied ML, agentic AI, and graph-based reasoning. The company runs experiments at the fringes of modern technology — ML, graph databases, agentic AI — and wants engineers who share the drive to stay at the frontier and turn innovation into real product value. This role spans prototype to production, and everything in between.
You have
5 years of experience in applied machine learning and AI, with models deployed and running in production
M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field — or equivalent practical experience (what you've built matters more than the degree)
Deep proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, scikit-learn)
Strong background in statistical analysis, predictive modeling, and time series forecasting
Experience with applied agentic AI/ML systems and multi-agent orchestration
Experience with NLP, LLMs, and RAG architectures
Comfort working with large-scale datasets and distributed computing environments
Nice to have
Graph database or graph RAG experience (a major plus — core to the stack)
Background in retail, supply chain, or demand forecasting domains
Experience with graph neural networks or knowledge graphs
Familiarity with MLOps platforms and model serving infrastructure
Contributions to open-source ML/AI projects or published research
What To Do
Design, build, and deploy ML models for demand forecasting, time series prediction, consumer sentiment analysis, and anomaly detection at enterprise scale
Develop and iterate on the agentic AI architecture — building systems that reason across heterogeneous data sources and take autonomous action
Build and maintain robust ML pipelines: data preprocessing, feature engineering, model training, evaluation, and production deployment
Architect and improve the production graph RAG system
Build RAG systems and LLM integrations that power natural language interfaces and autonomous workflows
Collaborate with backend engineers to ensure models are production-grade — optimized for latency, reliability, and scale
Own model performance end-to-end: monitoring, retraining, and continuous improvement in production
Stay at the frontier of AI research and bring relevant innovations into the platform
Interview process
Recruiter screen
Intro call with engineering leadership
Technical screen with a senior engineer
On-site: ML coding, system design, product sense, AI sense, and a meeting with co-founders