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Kepler

AI agent platform for bioinformatics workflows

Founding Talent

Full-Time
$170k - $230k/yr
Senior
New York, NY, USA
In Person
People & HRGeneralist Recruiting, Technical Recruiting

About the job

Requirements
  • At least 5 years of recruiting experience, primarily technical, including in-house experience at a fast-growing startup with a high hiring bar.
  • Personally sourced and closed senior engineers competing with offers from AI labs and big tech.
  • Experience running an extended campaign to win an individual candidate through relationship-building, stakeholder conversations, and an offer tailored to the candidate's priorities.
  • Ability to build long-term candidate relationships.
  • Ability to evaluate engineering résumés, distinguish engineers who owned systems from those who worked near them, and assess the quality of training at employers.
  • Experience building recruiting from scratch or joining early enough to help build the pipeline, interview loop, scorecards, and reporting.
  • Ability to use funnel data, including candidate drop-off, source conversion, and stage duration, to improve recruiting.
  • Fluency with AI tools and ability to build agents and automations using Claude and Ashby.
  • Ability to communicate candidly when a recruiting pitch or offer is wrong before the candidate exits.
  • Ability to communicate proactively and respond reliably to teammate and candidate needs.
  • Ability to adapt when plans change quickly while work continues.
Responsibilities
  • Own talent acquisition at Kepler, including deciding whom to pursue, how to win candidates, how engineers perceive the company as a workplace, and maintaining hiring standards as the company grows.
  • Develop a live map of the engineers the team wants to recruit, the rationale, and the team members best positioned to reach them.
  • Identify five high-impact candidates and design an approach to recruit each one.
  • Take over active senior searches, beginning with the candidate at greatest risk of being lost.
  • Host events for engineers in New York, such as technical dinners, talks, or events with investors.
  • Improve candidate closing strategy, including selecting the conversations, participants, and proof points that help win candidates and determining when compensation discussions occur.
  • Identify and fix recruiting-process issues that cause strong candidates to drop out.
  • Determine how outbound recruiting and agency partners complement the network, and retain only effective approaches.
  • Build the talent function around effective practices, including the network, process, employer brand, data, and, when volume warrants, a team.
  • Help determine which roles to open and when, including whether to create a role for an exceptional candidate.
  • Prioritize engineering hiring, including forward deployed engineers, followed by go-to-market and design roles as those openings arise.
  • Participate in every candidate debrief and provide critical assessments of candidates, recruiting processes, and offers.
  • Handle recruiting operations, including coordinating onsite interviews across calendars, conducting reference calls, and preparing offer letters.
  • Build recruiting agents and automations using Claude and Ashby.

About the company

Kepler AI builds an AI agent platform for bioinformatics research and workflow automation. It lets research teams, biotech firms, and pharmaceutical companies ask complex questions in plain English without writing code, and it connects to large datasets to speed up drug discovery. The platform supports data analysis, experiment planning, and literature reviews by running AI agent-based queries across integrated data sources, unifying scattered databases to help identify drug candidates more efficiently. A notable collaboration with TileDB and Tahoe Therapeutics produced TahoeDive, which adds Tahoe-100M single-cell perturbation data into Kepler AI’s platform. The company’s goal is to increase scientific productivity and accuracy in life sciences R&D and to accelerate the path from data to actionable insights in drug discovery.

Company Size

1-10

Company Stage

N/A

Total Funding

N/A

Headquarters

New York City, New York

Founded

2025

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Simplify Jobs

Simplify's Take

What believers are saying

  • September 2025 Tahoe partnership gives Kepler a credible single-cell data wedge.
  • 2026 job postings show active hiring across agentic systems, ML, and product roles.
  • If TahoeDive gains adoption, Kepler becomes infrastructure for verified life-sciences analytics.

What critics are saying

  • No disclosed funding, revenue, or customer count signals fragile bioinformatics commercialization.
  • The company is hiring a Head of AI Research while listing only 1-10 employees.
  • Bioinformatics is crowded; Novo Nordisk, Recursion, and Benchling already own workflows.

What makes Kepler unique

  • Kepler AI pairs deterministic provenance with natural-language bioinformatics queries, launched 2025.
  • TahoeDive, built with TileDB and Tahoe Therapeutics, queries Tahoe-100M single-cell data.
  • Its platform unifies scattered datasets for reproducible analysis, experiment planning, and literature reviews.

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Benefits

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • Life Insurance
  • Disability Insurance
  • Health Savings Account/Flexible Spending Account
  • Unlimited Paid Time Off
  • Meal Benefits
  • Conference Attendance Budget
  • Professional Development Budget

Company News

Business Wire
May 21st, 2026
Anthropic highlights Kepler's verifiable AI platform for financial services built with Claude

Kepler, a verifiable AI platform for financial research, has been featured in an Anthropic customer profile detailing how it uses Claude to deliver defensible financial analysis. The platform combines language models for interpretation with deterministic code for calculations, mediated by a proprietary financial ontology that maps analyst terminology to exact line items in filings. Kepler's specialised models identify correct line items in 10-K filings 94% of the time, compared to 38-46% for frontier models alone. The platform indexes 26 million SEC filings, 50 million public documents and one million private documents across 14,000 companies and 27 global markets. Every figure links directly to its source filing. Built in under three months, Kepler serves analysts at private equity firms, hedge funds and investment banks, with expansion into private credit underway.

Kepler
Mar 17th, 2026
How Parallel helps Kepler build AI that finance professionals can actually trust.

How Parallel helps Kepler build AI that finance professionals can actually trust. This post was originally published on Parallel's blog. Kepler is building AI for work where the answer can't just be plausible - it has to be right. Its first product, Kepler for Finance, helps investment professionals analyze public companies with institutional depth and AI speed. Analysts ask complex questions in natural language and receive answers backed by financial filings, computed metrics, and citations down to the page and line item. Achieving that level of reliability requires a different architecture from most AI systems. Kepler integrates Parallel's Search API as the discovery layer at the top of its analytical pipeline, combining AI's ability to understand open-ended questions with deterministic infrastructure that guarantees accuracy and auditability on the back end. Key impact. * Landscape discovery in minutes: Preliminary research that once took analysts days now runs as an automated pipeline step. * Expanded product capability: Kepler now supports competitive landscape and sector-level analysis, not just single-company research. * Global coverage: Discovery across private companies, international players, and niche sectors beyond curated databases. * Fully automated workflows: Parallel search results feed directly into Kepler's deterministic pipeline without manual extraction. In finance, reliability is architectural. Language models are excellent at interpreting questions, understanding context, and structuring answers. They're far less reliable at retrieving precise data or producing the same answer twice. In many applications, that tradeoff is acceptable. In finance, it isn't. A wrong number can mean a blown deal, a compliance issue, or reputational damage that lasts years. Kepler built the platform around that constraint. The architecture enforces a strict separation between two layers: an AI layer that interprets questions and structures answers, and a deterministic layer that retrieves financial data, computes metrics, and generates citations tracing every number to its source. The two communicate through structured interfaces, but they never blend. Today, Kepler for Finance covers 950K+ SEC filings, 14K+ companies, and 27 global markets. But even this architecture has a gap at the very top of the funnel. Every analysis starts with discovery, and that requires the open web. Most financial analysis doesn't start with a known list of companies. An analyst working on a deal typically starts with a question: * What does the competitive landscape look like around this company? * Which international players matter that a US coverage list might miss? * Which upstream suppliers could influence risk? There's no filing to retrieve yet - the analyst first has to determine what the analysis should even cover. Traditionally, that means scanning industry reports, asking colleagues, and searching the web before the real work begins. It's slow, inconsistent, and impossible to scale. Discovery is the only place in Kepler's architecture where the system permits probabilistic outputs. That makes the quality bar unusually high: if discovery surfaces the wrong companies, the entire downstream pipeline operates on incomplete context. Parallel's Search API met the bar: relevant, broad, and built for machines. Kepler evaluated discovery solutions across three requirements: * Relevance: Discovery has to be accurate enough that the downstream pipeline is always pointed at the right material. * Breadth of index: Its customers increasingly work across areas curated datasets struggle to cover - private companies, international competitors, niche industry segments. Parallel's coverage across the open web lets Kepler discover entities well beyond traditional financial databases. * Programmatic design: Most search products are designed for humans. Kepler needed structured outputs that route directly into its pipeline without manual extraction. "We built Kepler to be audit-ready at every step. That standard applies to discovery too. Parallel's accuracy and coverage are what made it the only search API we trusted in that role." - Vinoo Ganesh, CEO, Kepler When an analyst asks a landscape-level question, Parallel performs entity discovery using real-time information from the open web. Those results flow directly into Kepler's deterministic pipeline. From that handoff forward, no probabilistic system touches the data. Parallel identifies who matters. Kepler determines what the data says about them. What this unlocked. Before Parallel, Kepler was a research tool that needed to be provided an explicit list of companies - powerful, but limited to situations where analysts already knew which companies to analyze. * Competitive landscape analysis. Analysts can now start with broad questions like "What does the competitive landscape look like in this sector?" Parallel identifies the relevant companies; Kepler analyzes them. * Supply chain and sector mapping. For an analyst researching EV battery manufacturers, the most relevant upstream suppliers - cathode material producers across South Korea or China - often don't appear clearly in US filings. Parallel surfaces these entities so Kepler's pipeline can incorporate them. * Comp set construction in unfamiliar sectors. Investment professionals are frequently staffed on deals outside their primary coverage. Parallel enables Kepler to generate structured company sets dynamically, helping analysts understand the ecosystem around a target much faster. The architecture scales beyond finance. Kepler started in finance, but the architecture is domain-agnostic. Legal research, regulatory compliance, healthcare analysis, procurement intelligence - anywhere professionals need defensible decisions from verified information, the same problems exist. Discovery at the top of the funnel. Verifiability at every step downstream. Kepler is partnering with Parallel to bring this architecture to every domain where auditability is essential.