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About This Role
We are WisdomAI.
*We exist to help people unlock clarity from complexity.*
WisdomAI builds AI\-powered analytics that put answers directly in the hands of the people closest to the business. Our platform doesn’t just surface insights — it explains the why behind them, helping teams move from data to decisions with speed, confidence, and context.
We’re trusted by companies like Cisco, Patreon, and Rubrik — and we’re just getting started.The problems here are hard, and the expectations are high. Our team is energized by both. If you’re compelled by ambitious problems, high standards, and meaningful ownership, you’ll thrive here.
We're looking for exceptional Research Engineers who enjoy turning cutting\-edge AI research into production systems used by enterprise customers. This role sits at the intersection of research and engineering \- you'll design experiments, invent new approaches, and ship systems that directly impact customer experience.
This role is based out of our San Mateo office.
What You'll Do
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- Design and run rigorous experiments, including ablations, benchmarking, and error analysis, to improve NL\-to\-SQL generation, retrieval, ranking, and agentic reasoning.
- Advance multi\-turn conversational understanding through improved context retention, entity resolution, conversational memory, and intelligent clarification strategies.
- Build robust evaluation frameworks, including automated benchmarks, regression suites, and LLM\-as\-a\-judge methodologies to measure and improve model quality.
- Prototype, validate, and productionize ML techniques, including fine\-tuning, distillation, retrieval optimization, and agent architectures.
- Evaluate emerging foundation models and AI research, rapidly translating promising advances into production experiments.
- Contribute to the broader AI community through technical reports, or conference talks.
What We're Looking For
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- 5\+ years of experience in applied machine learning, NLP, large language models, or AI research, with a proven track record of shipping production AI systems.
- Master's degree in Computer Science, Machine Learning, Artificial Intelligence, NLP, or a related field required; PhD is a strong plus.
- Deep experience with modern LLMs and agent architectures.
- Strong software engineering skills with production\-quality Python and experience using modern AI frameworks.
- Demonstrated ability to design rigorous evaluation frameworks, benchmark models, and make data\-driven decisions balancing quality, latency, cost, and reliability.
- Demonstrated ability to improve model cost/quality through experimentation—including retrieval optimization, fine\-tuning, distillation, inference optimization, and agent design.
Nice to Have
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- Publications at leading AI or NLP conferences (ACL, EMNLP, NeurIPS, ICML, ICLR, NAACL, CVPR) or meaningful contributions to open\-source AI projects.
- Experience adapting foundation models and deploying production\-scale LLM systems.
You'll Thrive Here If You...
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- Think scientifically and validate ideas through rigorous experimentation.
- Care equally about elegant research and measurable production impact.
- Enjoy digging into production failure cases to uncover new research directions.
- Balance curiosity with pragmatism, you know when to explore and when to ship.
- Want your work to directly influence how enterprises interact with AI every day.
Why WisdomAI
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We're building foundational technology at the intersection of large language models, reasoning, and enterprise analytics. Our platform empowers people closest to the business with trustworthy, conversational access to insights hidden across their organization's data.
You'll join a team of engineers and researchers tackling some of the hardest problems in applied AI—from structured reasoning and conversational memory to retrieval, planning, and trustworthy analytical agents.
You'll have the freedom to explore bold ideas, the infrastructure to validate them quickly, and the opportunity to see your work deployed in production for leading enterprises.
If solving unsolved AI problems while shipping real\-world impact excites you, we'd love to build with you.
Additional Details:
Compensation: The expected starting base salary for this role is $280,000\+, in addition to equity and benefits. Final compensation will be determined based on factors including experience, technical depth, interview performance, and overall alignment with the role. We believe in paying competitively for exceptional talent and leveling compensation based on demonstrated impact.
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Our CRAFT
CRAFT isn’t a list of aspirational values. It’s how we hire, how we promote, how we resolve hard calls, and how we stay honest with ourselves.
C — Customer trust. Everything starts here. We listen carefully, act with integrity, and respond with urgency. Delight is downstream of trust — never the other way around.
R — Relentless on outcomes. We move with urgency, take initiative, and see things through. Effort isn’t the measure — results are. If something isn’t working, we change the approach now.
A — Authenticity. We say the hard things, directly and with respect. We also show up as full humans. That combination is how trust is earned and how we grow together.
F — First principles, grounded in reality. We don’t accept “that’s how it’s done.” We strip problems to what’s actually true and build up from there — then validate quickly and let real\-world results decide.
T — Transparency. We share the why, not just the what. When something can’t be shared, we say so. We’re one team — information flows so we can do our best work and challenge each other when we’re wrong.
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Equal opportunity employer
WisdomAI is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by applicable law. We are committed to building a diverse, inclusive team where everyone can do their best work.
Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,708 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Wisdom AI, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $280,000 based on 147 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Wisdom AI AI Hiring
Wisdom AI has 1 open AI role right now. They're hiring across Research Engineer. Based in San Mateo, CA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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