AI Biologist - Metagenomics

As molecular data generation and frontier model intelligence grows, new approaches to data analysis are needed across the biotech industry. Latch is building intelligent, high-performance agents for biological data analysis, empowering over 5,000 scientists across 150+ R&D labs to handle data from instrument-to-insights.

We're seeking an AI Biologist to join our Metagenomics team, working at the frontier of what artificial intelligence can achieve in biology.

About the Role

You will contribute to our technical approach for evaluating how AI agents reason through complex microbiome and metagenomics datasets. Working with software engineers and computational biologists, you'll build ground-truth benchmark datasets drawn from real published microbiome studies that rigorously test whether agents can perform the critical analytical and interpretive steps that expert microbiome researchers execute.

Your deep expertise in microbiome biology, metagenomics analysis, and the ability to identify confounders and technical artifacts will define how we measure agent capability in computational microbiology.

Requirements

• Master's or PhD in microbiology, microbial ecology, bioinformatics, biology, or related field
• 3+ years of hands-on research experience with microbiome or metagenomics data, demonstrated through publications or industry work
• Deep expertise in at least one of:
  • Human microbiome and disease (e.g. IBD, CRC, obesity)
  • Longitudinal microbiome or microbiome interventions (e.g. FMT, diet, antibiotics)
  • Microbial function and metabolism
  • Plant, soil, or environmental microbial ecology
• Experience interpreting microbiome data in biological context and identifying confounders, technical artifacts, and limitations of the underlying experiments
• Proficiency in Python and/or R for biological data analysis and visualization
• Familiarity with 16S and/or shotgun metagenomics, including taxonomic, functional, and community-level analysis

Nice-to-Have

• Hands-on experience designing or troubleshooting microbiome experiments, including sampling, extraction, controls, or library preparation
• Experience integrating metagenomics with metabolomics, metatranscriptomics, host omics, or clinical phenotypes
• Experience with strain-level analysis, metagenomic assembly, MAGs, or long-read sequencing

Culture @ Latch

How we work. Genuinely flexible schedules - we just ask that you communicate when you're coming in later than usual. We care most about hard work and output. We're respectfully opinionated, it will always be us against problems, not each other. Optimize for each other's time and bring solutions, not just problems. You'll join a bench suited to your expertise, but we value cross-domain learning and interoperability across teams.

The office & perks. Waterfront office near Oracle Park, 2x free daily meals, unlimited snacks, company outings most months, and team offsites. Plus a vibrant community: cycling, soccer, figure skating, boxing, run clubs, reading clubs, martial arts, music, game nights.

Compensation & Logistics

• 1099 (or W8-BEN) contract, 40 hrs/week, no end date
• Fully performance-based pay: $120K–$180K, uncapped upside. 2x quota = 2x pay.
• 2-4 week paid ramp—full OTE from day one
• Remote (globally), hybrid, or onsite in SF (onsite preferred)
• Work authorization: OPT visa holders only (not STEM Extension)
• Onsite perks: 2x free meals/day, waterfront office (China Basin), monthly parking (based on availability)
• Candidates with all of the above qualifications + proven management skills will be eligible for more senior positions

Interview Process

Timeline: We move fast: 8–12 days from submission to offer.

1. Intro Screen - Technical Recruiter
2. Take-Home Project - HackerRank
3. Technical Interview - Member of Technical Staff
4. Culture Interview - C-Suite
5. Offer

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About Astera

Astera is a private foundation on a mission to steer science and technology toward an abundant future. We believe the coming years will bring an era of unprecedented scientific and technological advancement as exponential progress in AI converges with central advances in other fields to dramatically accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive scientific progress.  

Unlike traditional non-profit research organizations, projects supported by Astera operate like high-velocity startups, allowing us to focus on ambitious goals, match structure to problem, and attract strong technical talent and leadership. You can read more about our mission, vision, and programming at astera.org/vision.  

Position Summary

Today's frontier biological AI models are trained almost entirely on static, pre-existing data. They are powerful pattern matchers, but lack feedback from real biology.

Polytope Bio is a residency project at Astera that is building the missing piece: a post-training engine that closes the loop between frontier AI models and high-throughput biology. Our work will power new applications in generative biology by aligning frontier AI models directly to experimental measurements of what actually folds, binds, and functions.

We are looking for a Principal AI Scientist to help launch our AI research program. You will be joining at the point of maximum leverage: early enough to shape the scientific direction, the modeling approaches, and the training strategy. The project is resourced with significant compute, financial runway, and the ability to generate large-scale prospective biological datasets. The researcher in this role will work hands-on to develop and publish new reinforcement learning methods and bio AI models leveraging datasets created by our unique high-throughput biology platform.

Longer-term, we are seeking a candidate who is driven by the prospect of growing their leadership of the AI research program. While our immediate focus is on collaborative research strategy and execution, the candidate will have the potential to develop into a co-founding leadership position in the event of a successful spinout.

Responsibilities:

• Co-direct the research program: Partner directly with the founder to shape the scientific vision, research direction, and technical execution.
• Develop RL feedback loop: Design, implement, and improve model post-training methods that translate high-throughput biological measurements into direct reward signals for biological language models.
• Hands-on engineering: You will work directly with the technical founder to architect model training infrastructure and build, run, and debug models, training loops, and evaluation metrics.
• Bridge wet/dry lab: Partner with the experimental team to ensure that what we measure in the lab and what the models learn are designed as a single, cohesive system.

Qualifications and Experience

• Research Experience: PhD in machine learning, computational biology, or a related field with a minimum of 2 to 5 years of industry research experience post-PhD (accomplished researchers without a PhD are also encouraged to apply).
• Model Training: You have trained models from scratch, not just fine-tuned or called APIs. You have owned real training runs, know where they break, and know how to debug them.
• Modern Algorithms: Hands-on experience with generative diffusion models and/or transformer architectures.
• Reinforcement Learning: Deep familiarity with modern reinforcement learning and preference-optimization methods for deep learning.
• Builder mindset: A track record of strong research via publications, open-source work, shipped models, or equivalent evidence that you drive results. You are highly self-directed but thrive in a tight-knit, collaborative founding partnership.
• Entrepreneurial spirit: Comfort operating with ambiguity and research ownership. You are excited to build the infrastructure and grow the team.

Strong Pluses

• Familiarity with biological research (protein modeling, sequence models, structural biology, or adjacent areas).
• Experience building and scaling training infrastructure on large GPU clusters.
• Past team management and technical leadership experience.

Location

Preference for candidates able to co-locate in NYC or SF Bay Area. Remote work is possible for the right candidate.

What we offer

• Compensation: Base salary of $250,000 to $350,000 during the residency.
• Upside: The potential for a co-founding leadership role in a future spinout, contingent on project success and mutual fit.
• Scientific Impact: Authorship of high-impact open-source datasets, methods, and models.
• Resources: Significant secured runway and dedicated GPU resources.
• Unique Environment: A rare combination of frontier ML work directly coupled to a purpose-built, high-throughput experimental engine.
• Comprehensive Benefits: Full benefits package including health insurance, a company sponsored retirement plan, vision, dental, and more.

‍

About Astera

Astera is a private foundation on a mission to steer science and technology toward an abundant future. We believe the coming years will bring an era of unprecedented scientific and technological advancement as exponential progress in AI converges with central advances in other fields to dramatically accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive scientific progress.  

Unlike traditional non-profit research organizations, projects supported by Astera operate like high-velocity startups, allowing us to focus on ambitious goals, match structure to problem, and attract strong technical talent and leadership. You can read more about our mission, vision, and programming at astera.org/vision.  

Position Summary

Today's frontier biological AI models are trained almost entirely on static, pre-existing data. They are powerful pattern matchers, but lack feedback from real biology.

Polytope Bio is a residency project at Astera that is building the missing piece: a post-training engine that closes the loop between frontier AI models and high-throughput biology. Our work will power new applications in generative biology by aligning frontier AI models directly to experimental measurements of what actually folds, binds, and functions.

We are looking for a Senior/Staff AI Research Scientist to join our foundational team. You will be joining at the point of maximum leverage: early enough to influence the modeling approaches, experimental design, and training strategy. The project is resourced with significant compute, financial runway, and the ability to generate large-scale prospective biological datasets. The researcher in this role will work hands-on to develop and publish new reinforcement learning methods and generative AI models leveraging datasets created by our unique high-throughput biology platform.

While our immediate focus is on hands-on research and rapid execution, there is potential for the right candidate to evolve into a co-founding technical or leadership role in the event of a future spinout.

Responsibilities:

• Drive core research: Work closely with the technical founder and team to develop and execute the scientific vision, develop cutting-edge modeling approaches, and iterate rapidly on new ideas.
• Develop RL feedback loop: Design, implement, and improve model post-training methods that translate high-throughput biological measurements into direct reward signals for biological language models.
• Hands-on engineering: You will work directly with the technical team to architect model training infrastructure and build, run, and debug models, training loops, and evaluation metrics.
• Bridge wet/dry lab: Partner with the experimental team to ensure that what we measure in the lab and what the models learn are designed as a single, cohesive system.

Qualifications and Experience

• Research Experience: PhD in machine learning, computational biology, or a related field, with a minimum of 1-2 years of post-PhD research or industry experience (accomplished researchers without a PhD are also encouraged to apply).
• Model Training: You have trained models from scratch, not just fine-tuned or called APIs. You have owned real training runs, know where they break, and know how to debug them.
• Modern Algorithms: Hands-on experience with generative diffusion models and/or transformer architectures.
• Reinforcement Learning: Familiarity with modern reinforcement learning and preference-optimization methods for deep learning.
• Builder mindset: A track record of strong research via publications, open-source work, shipped models, or equivalent evidence that you drive results. You are highly self-directed but thrive in a tight-knit, collaborative early-stage environment.
• Entrepreneurial spirit: Comfort operating with ambiguity and a desire to build something new. You are excited to tackle hard problems and potentially transition into a technical co-founder in the future.

Strong Pluses

• Familiarity with biological research (protein modeling, sequence models, structural biology, or adjacent areas).
• Experience building and scaling training infrastructure on large GPU clusters.

Location

Preference for candidates able to co-locate in NYC or SF Bay Area. Remote work is possible for the right candidate.

What we offer

• Compensation: Base salary of $200,000 to $300,000 during the residency.
• Upside: The potential to evolve into a co-founding technical role in a future spinout, contingent on project success and mutual fit.
• Scientific Impact: Authorship of high-impact open-source datasets, methods, and models.
• Resources: Significant secured runway and dedicated GPU resources.
• Unique Environment: A rare combination of frontier ML work directly coupled to a purpose-built, high-throughput experimental engine.
• Comprehensive Benefits: Full benefits package including health insurance, a company sponsored retirement plan, vision, dental, and more.

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