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Altos Labs

Director / Senior Director, Machine Learning for Biology

San Diego, CA; San Francisco Bay Area, CA; Bay Area, California; San Diego, CaliforniaUnited States
Director+ · Institute of Computation
POSTED 20 AUG
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Role
81 days opentrue age
Posted 20 Aug
Pay
$135k–$168kno pay posted
Market p25–p75
$135k–$168k
San Diego · n=10 · 365d to 6 Nov
CompanyAltos Labs →
17open roles
Opened · 30d
+0
Closed · 30d
−1
6d of snapshots · 30d window
Market
65open · Director+ ML / AI
Opened · 7d
+35% of open
All ml / ai
6,677open
All tracked boards · live 9 Nov · n=65
LLMs

Our Mission

Our mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life.

For more information, see our website at altoslabs.com.

Our Value

Our Single Altos Value: Everyone Owns Achieving Our Inspiring Mission.

Diversity at Altos

Altos Labs has been named one of the Top 3 Biotech Companies and ranked for the second year on the Forbes 2026 Best Startups in America list. At Altos, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining a diverse and inclusive environment.

What You Will Contribute To Altos

Altos is asking questions biology has no method for yet; the Institute of Computing (IoC) builds the machine learning to answer them, and it is looking for a Director or Senior Director-level Scientist to lead that capability with a proven record of taking large-scale ML methods to real biological problems. Someone who is still deep enough in the work to be hands-on in the technical trenches and shape method choices, and senior enough to set strategic direction, at critical decision points, across the institute's most ambitious programs.

Responsibilities

  • Partner with the rest of the IoC Leadership Team to co-define and steer Altos’ core technical agenda, strategically identifying and prioritizing the most critical biological questions where machine learning can drive breakthroughs in cell rejuvenation and resilience, ensuring ML investments are deployed against highest-impact scientific frontiers rather than pure technical advancement.
  • Build and maintain strong partnerships with key stakeholders and leaders across different functions / Altos.
  • Lead a team of ML Scientists and Engineers, while staying hands-on enough to provide direct technical guidance across IoC's highest-impact ML programs.
  • Provide technical and strategic guidance and advice on ML project design and de-risking across multiple teams, drawing on deep intuition for what drives success or failure in ML-for-biology projects, backed by established experience.
  • Provide sustained technical mentorship and strategic guidance, supporting the professional development and growth of the team while proactively growing IoC's ML bench through training, coaching, and active recruitment of outstanding talent.
  • Lead effectively in a matrixed environment, influencing technical direction on projects and teams outside your direct reporting line, and aligning priorities across IoC's ML programs even where you don't hold reporting authority.

Who You Are

  • Deep knowledge and understanding with common and state of the art AI/ML methods and innovations and large-scale training procedures, applied to biological data modalities (e.g., single-cell, perturbation-response, spatial, or sequence data).
  • We are looking for a broad expertise and mastery across various AI/ML domains, specifically: reasoning and open-ended learning framework design for scientific hypothesis generation and evaluation; large-scale pretraining, fine-tuning, and post-training of foundation models, including RL, distillations, finetuning, and strategies to reduce hallucination and align model objectives with scientific discovery; model architectures that span multimodal generative and predictive modeling (normalizing flows, diffusion models, LLMs) for out-of-distribution generalization in biological systems; and hybrid modeling approaches — model identifiability, robustness, and regularized/interpretable methods such as anchor or energy-based models — for biological tasks, including target identification; semantic-based dynamic inference, virtual cell and world model paradigms.
  • A strong track record of leading innovative biological ML projects to success, while foreseeing and mitigating ML project failure modes at scale.
  • Ability to think strategically, and can optimize for the long term while acting and delivering with pace.
Source · Altos LabsFirst seen 4 NovOriginal ↗