Careers at Manifold Bio: Teams, Pay and How to Get Hired

Who Gets Hired: And Where They Sit

Manifold Bio's careers board listed eight open roles as of October 2026, six in Boston, two split between Boston and San Francisco, and the company's description, "the first high-throughput in vivo discovery engine combining massively multiplexed in vivo screening and AI-powered design to create tissue-targeted biologics," acts as the filter for every applicant. This guide maps what those roles pay, how the interview loop screens for the rare hybrid skill set the platform demands, where the work actually happens, and which traits predict survival inside a company that fuses wet-lab throughput, AI-guided protein design, and molecular barcoding into a single engine.

The open roles trace the org chart. Wet-lab openings: Scientist, cDNA Display, Director, Oligo Team Lead, Associate Scientist/Senior Associate Scientist, In Vivo Pharmacology – Study Coordinator, and a contract Research Technician, Pre-clinical Pharmacology. These roles support the mCodes platform: inert peptide barcodes that quantify hundreds of candidates across tissues in a single animal. Computational roles: Computational Scientist, Assay Development and AI/ML Research Engineer sit at the protein-design–in-vivo-data interface. The Product Manager, AI Platform & Partnerships owns the roadmap across protein design, foundation models, reasoning models, agents, and infrastructure. The lone commercial role, Sr. Director / VP, Business Development, reflects a dual-path strategy Jeff Huber described in 2022: the platform-as-a-service business stands alone even if the therapeutic pipeline stalls.

Leadership reinforces the cross-functional mandate. Co-founders Gleb Kuznetsov (CEO) and Pierce Ogden (CTO) trained in George Church's lab at Harvard Medical School; Shane Lofgren leads portfolio development and strategy. Kimberly Scearce-Levie is CSO, Paula Cobb COO, Steve Holtzman executive chairman. Doug Williams chairs the strategic advisory board. George Church is a co-founder and advisor. Investors include Triatomic Capital, Playground Global, Section 32, and Horizons Ventures, firms with platform-track records. Hiring accelerated: open roles rose 14 percent in the 30 days before October 19, 2026. Candidates who pass show depth in one domain (bench or computational) and fluency in the other. The next section maps what that depth pays.

What the Roles Pay

Manifold Bio pays above the biotech median. Zero G Talent's job board shows eight salaried openings spanning $93K–$298K with a $182K median as of October 2026. Third-party data vary: alion.io reports a $138K median across seven recent postings; salary.com's May 2026 survey of current employees averages $110K, lower, likely reflecting hires before the latest funding round.

Zero G Talent's figures put the Sr. Director / VP, Business Development base at $250K–$350K/yr. Zero G Talent's data shows the Product Manager band at $165K–$275K/yr. Zero G Talent found the Director, Oligo Team Lead band at $195K–$239K/yr. The spread is wide. The Sr. Director / VP, Business Development carries the only base crossing $300K. The Product Manager band spans senior IC to early lead. Director, Oligo Team Lead and AI/ML Research Engineer sit in the $140K–$239K corridor. Mid-tier research roles cluster near $120K. The contract technician runs $30–$35/hr (roughly $62K–$73K annualized, no equity).

Equity follows the standard venture-backed biotech template: new-hire grants scaled to role and level, four-year vesting with a one-year cliff, annual refresh grants. Benefits match the Boston biotech baseline: employer-paid medical, dental, vision, 401(k) match, generous PTO, and lab-specific perks such as conference budgets and compute credits for ML roles.

Hiring velocity supports the bands. RecruitingFromScratch counted 13 open roles with disclosed pay as of mid-October, four posted in the prior 30 days. Candidates who bring cross-functional proof, wet-lab throughput data, ML model iteration cycles, partnership deal sheets, should expect offers at the midpoint to upper quartile of each band.

Inside the Interview Loop

Manifold Bio runs a five- to six-stage funnel spanning three to five weeks from application to offer. Interview Query's March 2026 guide maps it: resume review, recruiter screen, technical or case-study round, behavioral interview, final panel with multi-team interviews and a project presentation, then offer negotiation. The acceptance rate for qualified candidates is estimated at 3 to 5 percent — a reflection of both selectivity and the rare hybrid skill set required.

The recruiter screen filters for platform alignment, massively parallel in vivo screening and protein barcoding, and for the applicant's ability to map their background to the role. Data engineering tracks probe ETL pipeline design, experimental data modeling, and life-sciences integration. Benchling familiarity and AWS cloud storage experience differentiate. The careers page adds: deep expertise in machine learning, hybrid computational/wet-lab science, or library-guided design warrants an email to [email protected] even without a matching requisition.

Technical assessment typically follows as a take-home. Candidates design and implement a data pipeline, clean and analyze a sample dataset, or present a past project. The bar is production-grade: scalable ETL for high-throughput biological data, systematic cleaning and quality assurance, schema design for complex experimental metadata. Python and SQL fluency is assumed; evaluators weigh reproducibility and scientific rigor, not just functional correctness.

The final panel blends technical depth with cross-functional communication. Scenario-based questions cover troubleshooting and optimizing pipelines; behavioral prompts probe adaptability, collaboration, and presenting complex insights to computational biologists, molecular biologists, and biochemists. Manifold's diversity commitment signals that interviewers value varied experiences and ways of thinking. A candidate who describes translating a biologist's assay requirements into a versioned, monitored data flow gains an edge.

Data engineering roles offer remote flexibility, though periodic onsite visits in Boston or San Francisco may be required. Wet-lab positions, research associate, protein biochemist, in vivo pharmacology, are bench-bound in Boston, shaping both candidate pool and interview logistics. Manifold hires for the intersection. A pure software engineer without biological context stalls at the technical round; a bench scientist who cannot version-control an analysis pipeline stalls at the take-home. The candidates who advance have already built the bridge.

Where the Work Happens

Manifold Bio's headquarters occupies Suite 902 at 201 Brookline Ave in Boston's Longwood Medical Area, placing the company amid a dense concentration of academic hospitals, research institutes, and biotech ventures. The location is deliberate. The company's core thesis — in vivo testing at scale requires wet-lab infrastructure married to computational firepower — demands proximity to animal facilities, sequencing cores, and talent from Harvard Medical School, Dana-Farber, and the Broad Institute. CEO Gleb Kuznetsov made the operating model explicit in a May 2026 LinkedIn post: "Our HQ is in Boston and default 5 days on site."

The Boston site houses the full experimental stack. Zero G Talent postings list Boston for every wet-lab-touching role. A 2025 BioPharma Dive profile photographed co-founders Pierce Ogden and Gleb Kuznetsov in the labs, confirming dedicated bench space for the mCodes and mBER platforms that drive Manifold's throughput advantage.

Those platforms set the lab's capability ceiling. The mCodes system attaches inert peptide barcodes to therapeutic candidates, enabling pooled in vivo screens where hundreds of variants dose simultaneously in a single mouse and deconvolute via DNA sequencing at picomolar sensitivity. The company maintains a catalog of more than 1,000 mCodes and runs millions of protein-barcode combinations per campaign. In September 2026, the team reported measuring half a million designs in one experiment using mBER-2, the latest binder design engine. That scale, computational parallelism translated to mouse pharmacokinetics, is why the Boston lab exists. It also explains why the Director, Oligo Team Lead commands a specialized manufacturing salary: oligonucleotide synthesis and quality control at that throughput cannot be outsourced.

San Francisco appears only for two roles: Product Manager, AI Platform & Partnerships and AI/ML Research Engineer, both listed as "Boston or San Francisco." The pattern suggests a computational satellite near Bay Area AI talent, not a second wet-lab site. No postings list San Francisco for in vivo pharmacology, cDNA display, or oligo synthesis. The distinction matters: if your work requires the mCodes wetware, mouse colony, or DNA conversion pipeline, you are in Boston five days a week. If you are building the Virtual Organism — the predictive model that forecasts biodistribution from sequence, you may have geographic flexibility, but you still need tight iteration loops with Boston experimentalists generating the training data.

The Longwood address buys recruiting leverage. The 2026 LinkedIn Top Startups list for Boston named Manifold among the top 10, citing the convergence of "top AI, biology, and drug development talent." That convergence is geographic: a computational biologist leaving the Broad and a protein engineer spinning out of Harvard can walk to the same suite. For a company measuring progress in pooled in vivo experiments per quarter, that walk is a competitive advantage.

Who Thrives Here

The candidate who lasts at Manifold Bio operates at the intersection of wet-lab rigor and computational scale. The core technology — parallelized in vivo testing of up to a hundred barcoded molecules in a single mouse, demands fluency in both domains. Kuznetsov described the barcode design challenge as requiring each tag to be "stable, detectable, unique and manufacturable" without affecting the drug's behavior. That constraint forces molecular biologists to think like engineers and computational scientists to respect biological noise.

The vertically integrated model sharpens the filter. Kuznetsov said the company creates molecules from scratch, tests them in pooled in vivo screens, and advances hits toward clinical trials internally. A computational scientist cannot hand off a design and walk away — they own the candidate through synthesis, barcoding, animal dosing, and data interpretation. The Director, Oligo Team Lead carries similar accountability for the barcode library. Employees who thrive treat a failed in vivo readout as their debug target, not someone else's problem.

George Church's role as co-founder and advisor signals the intellectual lineage. Church pioneered multiplexed technologies from next-gen sequencing to genome writing. Manifold applies that logic to in vivo pharmacology. People from high-throughput settings, phage display, CRISPR screens, single-cell genomics, intuit the statistical discipline the platform requires. They know 95 of 100 molecules doing nothing is a feature, not a bug, and design follow-up experiments accordingly.

AI integration is operational. The Anthropic partnership on Claude Science and the Life Sciences Verification Program puts frontier models inside the design loop. Kuznetsov said Manifold looks forward to "putting frontier intelligence to work safely in our engine." The AI/ML Research Engineer and Product Manager, AI Platform & Partnerships roles exist because the barcoding platform generates training data at a scale most protein-language models never see. Candidates who have deployed models on proprietary biological data (not just public PDB splits) carry immediate credibility.

Commercial instincts matter. The Sr. Director / VP, Business Development role commands $250K–$350K because the platform's throughput creates partnership leverage: Manifold can de-risk a target for a pharma partner in weeks instead of years. Someone who has negotiated asset-centric deals, structured option agreements, or managed academic collaborations translates that speed into revenue.

The common thread: comfort with ambiguity that has a deadline. TechCrunch reported Series A capital — $40M led by Triatomic Capital with Section 32, FPV Ventures, Horizons Ventures, Tencent, Playground Global, Fifty Years, and FAST by GETTYLAB, funds the clinical transition. That transition imposes regulatory timelines on a research culture. People who have taken an IND-enabling package through GLP tox, or written CMC sections for a biologic, bring the operational maturity the next phase demands. They don't wait for process to be handed down — they build it.

Thriving here means treating a 100-plex in vivo experiment as a routine assay, not a moonshot. It means writing code that survives a reagent lot change. It means explaining a PK/PD disconnect to a computational colleague who has never held a pipette, then implementing their suggested fix in the next library design. The founders built the company on that friction. The people who stay are the ones who learn to work inside it, and the next time the careers board updates, the roles will still read like a map of the same unsolved problem.

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