Research Engineer, AI/ML

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. No Job ID etc. Remove boilerplate. Must preserve all substantive info. Let's parse the posting. First paragraph: "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." That's about the company; could be "About the role"? Actually it's about the company. Could be "About the role" or maybe not needed. The instruction: Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have an "About the role" section describing the job. The first paragraph is about the company, not the role. But we could still put it under About the role? Might be okay. However we need to preserve all substantive info. The role description: "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" That's responsibilities. Then "What You Bring" bullet list: Strong statistical and machine learning fundamentals... etc. That's qualifications/skills. Then "Experience and education" bullet list: We welcome a range... etc. That's qualifications. Then "How We Work": description of culture. Then "Perks And Benefits (Full-time Employees)" list: Competitive compensation and equity, Unlimited PTO, Up to 100% employer covered monthly healthcare premium..., Lunch provided via Sharebite..., Parental leave..., Tax free commuter..., Voluntary insurances..., Employee Assistance Program..., Free One Medical Membership, 401k. Then "Compensation Range: $170K - $235K". That's Pay. No schedule info. Thus sections: About the role (maybe combine company description + role overview?), Responsibilities, Requirements (maybe combine What You Bring and Experience and education?), Qualifications (maybe separate?), Skills, Benefits, Pay. Schedule omitted. We must not create sections with filler. So we include only those with content. We need to decide which headings to use. The instruction says use those headings as examples; we can include any of them if we have content. We need to avoid duplication. Could combine Requirements and Qualifications? But they are separate headings; we could include both if we have distinct content. Let's see: "What You Bring" includes skills and fundamentals; "Experience and education" includes education/experience. Could map "What You Bring" to Skills, and "Experience and education" to Qualifications. Or Requirements could be combined with Qualifications. But we need to avoid thin sections. Let's think: We have substantive content for Skills (strong statistical and ML fundamentals, experience shipping ML/AI systems, comfort with noisy data, experience across several areas, strong Python skills, TypeScript plus). That's Skills. Qualifications: education/experience (bachelor's + 3 years, master's +1 year, PhD, equivalent practical experience). That's Qualifications. Responsibilities: the bullet list under "The Job". About the role: could be the initial company description plus maybe short summary of role? But we already have responsibilities. Could put company description under About the role. That's fine. Benefits: the list under Perks And Benefits. Pay: compensation range. Schedule: none, so omit. We must not include boilerplate like equal opportunity, etc. Remove that. We must not include application instructions. We must preserve all substantive info: ensure we include each bullet point. Let's extract details. About the role (company description): "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." Responsibilities (bullet points): - 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. We need to keep the exact phrasing? Preserve all substantive information; do not invent or summarize away detail. So we should keep the sentences as given, maybe split into list items. What You Bring (skills): - 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 (qualifications): - 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 (culture) maybe could be included under About the role? But we have no heading for culture; we could omit or put under About the role as extra paragraph. The instruction says we can include only those headings if we have real specific content. We have content for How We Work; but there's no heading for that in the list. We could incorporate into About the role or maybe omit because not in list. The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but says use those for section headings (e.g...). Likely they expect only those headings. So we should not create a custom heading like How We Work. We could incorporate that content into About the role or maybe under Responsibilities? But it's about culture, not responsibilities. Could add a paragraph under About the role after company description. That would preserve the info. Thus About the role will contain: company description + How We Work description. Let's extract How We Work text: "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." We need to preserve line breaks? We'll put as

with sentences. Now Benefits list: - 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 Pay: Compensation Range: $170K - $235K Now we need to output HTML fragment only, no markdown fences. We'll produce:

About the 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.

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.

Responsibilities

• 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.

Skills

• 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.

Qualifications

• 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;