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About This Role
Description
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Salesforce AI Research is looking for outstanding AI Research Scientists / Research Engineers.
Do you want to be at the forefront of a team at Salesforce that is making history? Do you want to develop new technology and science that millions of CRM customers will use? Are you excited about working on large\-scale Generative AI, Agentic Systems, Speech Intelligence, and Multimodal Deep Learning?
Our team discovers new research problems, develops novel models, and bridges the gap between groundbreaking theory and customer\-facing reality. We are dedicated to a high\-impact research model that provides the best of both worlds: the ability to advance the state\-of\-the\-art in AI and the opportunity to see that work operate at massive, real\-world scale within the world’s \#1 AI CRM. We believe that making substantive progress on hard, enterprise\-level applications drives and sharpens the research questions we study.
Check out our website to learn more about the groundbreaking work of the Salesforce AI Research team: https://www.salesforceairesearch.com
Candidates have a strong background in one or more of the following areas:
- Agentic AI \& Reasoning: LLM\-powered agents, reinforcement learning, and autonomous workflows.
- Multimodal \& Computer Vision: Vision\-language models (VLM), video understanding, and visual grounding for GUI agents.
- Speech Intelligence: Voice intelligence, TTS/ASR, human\-like turn\-taking, and low\-latency audio processing.
- Efficient Systems: Scalable deep learning infrastructure, high\-performance audio/video pipelines, and low\-latency deployment.
- Core Modeling: Machine learning methodology, pre\-training/post\-training (RLHF, DPO), and model distillation.
Responsibilities:
- Own and pursue ambitious research and engineering goals aligned with strategic initiatives.
- Develop novel models and technical solutions for real\-world, large\-scale problems within the enterprise ecosystem.
- Design, build, and maintain production\-ready services, including scalable APIs and agentic evaluators.
- Collaborate across a strike\-team of researchers and engineers to deliver complex projects that directly impact our core AI products and customer experiences.
Minimum Qualifications:
- Ph.D., Master’s, or Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
- A strong publication record in top AI venues (e.g., NeurIPS, ICLR, ICML, CVPR, ICCV, ACL, EMNLP) OR a proven track record of shipping AI products to real users.
- Demonstrated expertise in one or more core focus areas: LLMs, Agentic Systems, Computer Vision/Multimodal, Speech Intelligence, or Research Engineering.
- Experience with Python and deep learning libraries/platforms (e.g., PyTorch, Jax, or DeepSpeed).
- Proven ability to implement, operate, and deliver results via innovation at a large scale.
Preferred Qualifications:
- Full\-time industry experience in deep learning research and/or product development.
- Experience training and evaluating large\-scale multimodal models or speech models, including techniques for alignment, safety, and latency optimization.
- Experience with production cloud deployments (AWS/GCP), container orchestration (Kubernetes), or high\-performance systems (MLOps).
- Strong programming skills, with a competitive background (e.g., ACM\-ICPC or significant open\-source contributions) or experience in UI/graphic design.
- Thoughtful about AI impacts, trust, and ethics.
- Outstanding communication, collaboration, and problem\-solving skills.
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 26,159 AI roles we're tracking, Research Scientist positions make up 0% of the market. At Informatica, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $257,000 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Informatica AI Hiring
Informatica has 39 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Architect, AI Software Engineer. Positions span IN, US, CA, US, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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