Research Scientist (AI/ML)
The Research Scientist in AI/ML focuses on advancing core algorithmic frontiers: designing novel neural architectures, multimodal systems, reinforcement learning methods, and post-training alignment techniques.
The Research Scientist in AI/ML focuses on advancing core algorithmic frontiers: designing novel neural architectures, multimodal systems, reinforcement learning methods, and post-training alignment techniques.
The profile typically requires a Ph.D. in Computer Science or Mathematics, publications at top-tier conferences (NeurIPS, ICML, ICLR, CVPR), and the ability to turn theoretical breakthroughs into scalable code using PyTorch or JAX across distributed GPU clusters.
Methodologies & technical foundations (% penetration)
Infrastructure & tooling demanded in job specs (% penetration)
Seniority Level Breakdown
Corporate hierarchy distribution
Top Hiring Enterprises
Leading corporations posting this role
- Alphabet Inc. (Class A)44 positions
- Alphabet Inc. (Class C)44 positions
- Google (Alphabet)44 positions
- Anthropic43 positions
- Amazon17 positions
- OpenAI17 positions
S&P 500 Sector Distribution
Industry sectors generating the highest hiring demand
- Communication Services52.9%
- Information Technology18.4%
- Consumer Discretionary8.6%
- Industrials6.3%
- Health Care6.3%
Live Enterprise Job Postings
Sample of active postings directly from corporate ATS boards
Staff Quantitative Researcher
Research Scientist II
Senior Research Scientist / Engineer - Electronics R&D
Research Scientist, Energy Innovation
Data Scientist, Global Quantitative Research
Intern - Quantitative Research, Principal Asset Allocation (Undergrad or Graduate - Summer 2027)
Welding Research Engineer
AI Applied Scientist
Recommended Skill Tracks & Certifications
Curated programs on Coursera, DataCamp and cloud providers aligned with these role requirements
Google Cloud Professional Data Engineer Specialization
Official curriculum by Google Cloud covering large-scale data processing with BigQuery, streaming pipelines with Dataflow, and modern lakehouse architecture.
Data Engineer with Python Career Track
Interactive, browser-based coding track covering robust data pipelines, PostgreSQL, unit testing, software design patterns for data, and PySpark fundamentals.
Generative AI with Large Language Models
Created by Andrew Ng and AWS: practical engineering foundations of transformer architectures, pre-training, instruction fine-tuning, and alignment evaluation in production.
Google Cloud Professional Data Engineer
Gold standard certification assessing design, security, scalability, and operational reliability of BigQuery and GCP analytical pipelines.
Databricks Certified Data Engineer Professional
Validates mastery of streaming, batch ETL, Unity Catalog governance, and production Lakehouse architectures with Delta Lake and Spark.