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About This Role
Tabs is the AI Operating System for Revenue, built for modern finance and accounting teams. It combines deep revenue and accounting expertise with the agents and applications needed to run revenue work end to end. Tabs understands customer and contract context, applies accounting logic, and executes critical workflows with built in controls, auditability, and human oversight. With Tabs, finance teams can move from manually managing revenue workflows to directing outcomes while the system executes the work.
The Job
You’ll work on a fast-moving AI team, owning problems from initial exploration through production.
- Turn messy financial data and ambiguous problems into working AI products, starting with simple baselines and adding complexity only when it earns its keep
- Build evaluations that reflect real user outcomes, then use error analysis, ablations, and production feedback to make the system better
- Make practical tradeoffs across model quality, cost, latency, determinism, reliability, and maintainability
- Partner closely with product and engineering to build AI features that take real work off finance teams’ plates
What You Bring
- Strong statistical and machine learning fundamentals, with good judgment about when the answer is classical ML, an LLM, agents, or something in between
- Experience shipping ML or AI systems end-to-end, from data and evaluation through production
- Comfort making progress with noisy data, weak labels, incomplete specifications, and imperfect supervision
- Experience across several of: classical ML, embeddings, retrieval and reranking, similarity search, model evaluation, LLM applications, and agentic systems
- Strong Python skills and the ability to contribute to production software; TypeScript or modern web application experience is a plus
Experience and education
We welcome a range of backgrounds. Successful candidates will typically have one of the following:
- A bachelor’s degree in a relevant quantitative field plus 3+ years of relevant industry or applied research experience
- A relevant master’s degree plus 1+ year of relevant industry or applied research experience
- A relevant PhD; doctoral research counts as relevant experience, with 3 years of substantive doctoral research considered equivalent to the experience above
- Equivalent practical experience demonstrated through shipped systems, independent research, open-source work, or another nontraditional path
How We Work
We’re a small team, so everyone has a hand in deciding what to build and making it work in the real world.
- We ship, learn from real usage, and iterate
- We make assumptions explicit, follow the evidence, and communicate tradeoffs clearly
- We stick with hard problems and welcome better ideas, regardless of where they come from
- We help where the team needs us, even when it falls outside our immediate scope
- We collaborate closely in person five days a week
No one gets extra points for making the solution more complicated than the problem.
Even if you don’t meet every qualification, we encourage you to apply. We care most about curiosity, craft, judgment, and drive.
Perks and Benefits (Full-time Employees)
- Competitive compensation and equity
- Unlimited PTO
- Up to 100% employer covered monthly healthcare premium (medical, dental, vision)
- Lunch provided via Sharebite, plus dinner for any later in office days.
- Parental leave up to 12 weeks
- Tax free commuter and parking benefits
- Voluntary insurances (Life, Hospital, Critical Illness, Accident)
- Employee Assistance Program (Rightway)
- Free One Medical Membership
- 401k
Tabs is an equal opportunity employer. We welcome teammates of all identities and do not discriminate on the basis of race, ethnicity, religion, gender identity, sexual orientation, age, disability, veteran status, or any other protected characteristic. We’re committed to creating an environment where everyone can grow, contribute, and feel comfortable being themselves.
Compensation Range: $170K - $235K
Salary Context
This $170K-$235K range is above the median for Research Engineer roles in our dataset (median: $191K across 47 roles with salary data).
View full Research Engineer salary data →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 8,943 AI roles we're tracking, Research Engineer positions make up 3% of the market. At TABS, 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 $252,000 based on 419 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($202K) sits 20% below the category median. Disclosed range: $170K to $235K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
TABS AI Hiring
TABS has 1 open AI role right now. They're hiring across Research Engineer. Based in New York, NY, US. Compensation range: $235K - $235K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.
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