Senior Level Artificial Intelligence Scientist Jobs
Senior level artificial intelligence scientist jobs call for professionals who set the technical direction, own research outcomes, and lead the teams or projects that bring complex AI systems to life. Openings are concentrated across Technology & Software, Banking & Financial Services, and Investment & Asset Management, with a mix of on-site, remote, and hybrid settings, and employers like Intuit, Apple, and NVIDIA hiring at this level now.
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Join the NVIDIA GeForce NOW cloud team that allows users to play high-quality PC games on various devices, without the need for a dedicated gaming PC or console. NVIDIA's GeForce NOW service is built on top of our GPU technology, including our proprietary GPU architectures and software optimizations allowing efficient and high-quality experience even at high resolutions and fps and at industry leading low latencies.
Our team is building the Diagnostic, Prescriptive and AI-augmented Analytics solutions that encompass processing, visualization, anomaly detection, root cause and predictive modeling for the benefit of millions of our end users. Our active projects include real-time forecasting of demand, constraint-optimized capacity allocation, dynamic prescriptions per session, Customer Onboarding and Voice of Customer Analytics, targeted Customer Outreach campaigns based on Customer Retention modeling, effective personalized diagnostic recommendations, LLM Chatbot. You will wield the power of Data and AI to help globally deliver a best-in-class cloud computing/streaming performance and experience. Our technology stack relies on industry standard components and tools (Python, R, Pandas, JupyterLab, Spark, SQL, Databricks, MLFlow, Delta Lake, Grafana, Kibana, Kubeflow, Elyra, Kubernetes, Gitlab, CI/CD, MLOps, Kafka, SQS, Kubernetes)
What you'll be doing:
Provide Technical Leadership to the team of Data Scientists and Engineers working on global deployment at scale of GPU Compute services.
Work with Leadership and Stakeholders to understand top level requirements, build a tech roadmap, design solutions and guide the team to deliver results.
Acquire and apply domain knowledge of the product and platform to lead the design, implementation, and deployment of AI/ML based solutions for generating actionable insights and real-time prescriptive analytic pipelines to drive optimal outcomes for production services.
Build and Deploy real time and scalable solutions for real time User Diagnostics, LLM Chatbot, dynamic Suspicious Activity Detection, User feedback based clustering and alerting and LLM based Actionable Insight Generation solutions for Engineering And Management.
Improve productivity of the org by wrangling petabytes of data using statistical/AI/ML/LLM models to provide actionable and real time insights.
Leverage pioneering Forecasting models and Constraint Optimization solvers to improve capacity management and deliver server efficiency and end-user latency.
Leverage innovative ML/AI predictive models with explainability for User Retention/Churn and designing outreach campaigns.
Build innovative multi-agent self-learning Harnesses for improving engineering productivity for analytics and deployments.
What we need to see:
Master’s/PhD or equivalent experience in Data Science, Statistics, Mathematics, Physics, Operations Research or related quantitative field
15+ years of software experience for large-scale and reliable production deployments and 8+ years of proven experience in Statistics/AI/ML
Hands-on expertise in programming languages like Python, SQL, Java and modeling frameworks like Scikit-learn, Pytorch, TensorFlow for large projects.
Experience with common tools for data storage and processing (e.g. Spark, Pandas, Delta Lake) including drilling into problems of running large scale software in a big network.
Excellent verbal and written communication skills to convey rich data insights to non-Technical and Technical Stakeholders.
An outstanding track record of successful past projects, as a lead, related to the research and application of data science at scale.
Experience of User Retention Modeling, LLMs, Time Series Forecasting or Operations Research is a plus.
With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and versatile people in the world working with us, and our engineering teams are growing fast in some of the most impactful fields of our generation: AI, Data Engineering, Data Science. If you're a creative engineer who enjoys autonomy and shares our passion for technology, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 248,000 USD - 379,500 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. #deeplearningSee All 97 Senior Level Artificial Intelligence Scientist Jobs
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Who's Hiring
- Intuit8
- Apple4
- NVIDIA4
- JPMorganChase4
- Goldman Sachs3
Top Industries Hiring
- Technology & Software39
- Banking & Financial Services12
- Investment & Asset Management12
- Fintech8
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Senior Level Artificial Intelligence Scientist Jobs: Frequently Asked Questions
How do I get a senior level artificial intelligence scientist job?
Employers hiring at the senior level look for candidates who have led end-to-end AI research or product initiatives, not just contributed to them. A strong publication record, open-source contributions, or deployed production systems carry significant weight. Demonstrating that you have mentored junior scientists, shaped research roadmaps, and made architectural decisions that held up at scale will set you apart from mid-level candidates.
Which companies hire senior level artificial intelligence scientists?
Companies hiring senior level artificial intelligence scientists right now include Intuit, Apple, and NVIDIA, based on current listings on Migrate Mate as of July 2026. Hiring at this level is driven by a mix of large technology firms, well-funded AI startups, and enterprise organizations building proprietary AI capabilities across healthcare, finance, and defense sectors.
Are there remote senior level artificial intelligence scientist jobs?
Yes, remote and hybrid options are widely available at the senior level. About 43% of senior level artificial intelligence scientist openings are remote or hybrid as of July 2026, reflecting the research-oriented nature of the role and employer competition for top talent regardless of location. On-site roles tend to cluster at labs and organizations where hands-on hardware or data infrastructure access is required.
What makes an artificial intelligence scientist role senior level?
Senior level roles are defined by scope and ownership. Where a mid-level scientist executes on a defined problem, a senior artificial intelligence scientist shapes how the problem is framed, selects the modeling approach, and is accountable for the outcome. These roles typically involve leading a sub-team or project workstream, mentoring junior researchers, contributing to hiring decisions, and publishing or presenting work that represents the organization externally.
Which industries hire the most senior level artificial intelligence scientists?
Senior level artificial intelligence scientist roles concentrate in Technology & Software, Banking & Financial Services, and Investment & Asset Management, based on current listings on Migrate Mate as of July 2026. These sectors drive demand because they have both the data infrastructure to support large-scale AI development and the competitive pressure to attract experienced scientists who can move quickly from research to production.