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Altos Labs

Machine Learning Engineer

Posted 9 Days Ago
9 Locations
Mid level
9 Locations
Mid level
The Machine Learning Engineer will develop and optimize machine learning models for biomedical applications, working within scientific teams to evaluate AI technologies, enhance model performance, and facilitate the integration of multi-Omics data. Key responsibilities include building scalable systems in cloud environments and managing analysis pipelines.
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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

We believe that diverse perspectives are foundational to scientific innovation and inquiry. 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 Labs is building high-performance, scalable, quantitative solutions for biomedical image analysis and integration with multi-Omics data. The team work at multiple scales including data from Electron/Light Microscopy, Digital Histology and Pathology up to functional analysis In Vivo. We will enable and accelerate the Altos mission by leveraging state of the art computer vision and machine learning, and collaborating with MLOps at Altos to make all our models easily trainable, findable, interpretable, and accessible across diverse research groups.

Responsibilities

  • Evaluate state of the art and Retrain AI models across the full spectrum of imaging including: de novo protein design, structure identification and dynamics in single particle CryoEM ; light microscopy  and multi-omics data integration and cross domain mapping of data collected in situ and in vivo.
  • Demonstrate software engineering skills to develop reliable, scalable, performant distributed systems in a cloud environment.
  • Develop efficient data loading strategy and performance tracking to train large models with distributed training across multiple nodes 
  • Build, deploy, and manage multi modal analysis pipelines for scientific analysis, and machine learning workflows in an integrated, usable framework.
  • Understand scientists' needs across a wide range of scientific disciplines by collaborating with both users and software engineers.
  • Bridge the communication gap between experimental scientists, algorithm developers and software deployers.

Who You AreMinimum Qualifications

  • BS/MS in Computer Science/Biomedical Engineering or related quantitative field.
  • Candidates should have 2-5 years of relevant industry and/or academic experience.
  • Experience with one or more programming languages commonly used for large-scale data management and machine learning, such as Python, C++, Pytorch/Tensorfllow, Pytorch Lightning etc.
  • Previous experience with Machine Learning at scale: Large Language Models and Self-Supervised/Contrastive/Representation Learning for Computer Vision applications and multi modal integration. 
  • Experience applying software engineering practices in a scientific environment, or another environment with similar characteristics.
  • Demonstrated track record of hands-on technical leadership and scientific contributions such as papers or conference communications.
  • Excited to design and implement technical and cultural standards across scientific and technical functions.

Preferred Qualifications

  • Bioinformatics data processing and analysis.
  • Multi source data Integration.
  • Experience with cloud computing and containerization.
  • Knowledge of genetics/human genetics

The salary range for Redwood City, CA:

  • Machine Learning Engineer I: $144,500 - $195,500
  • Machine Learning Engineer II: $170,000 - $230,000

The salary range for San Diego, CA:

  • Scientist I, Machine Learning: $141,950 - $192,050
  • Scientist II, Machine Learning: $160,650 - $217,350

Exact compensation may vary based on skills, experience, and location.


For UK applicants, before submitting your application:

- Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice)
- This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.

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What We Want You To Know

We are a culture of collaboration and scientific excellence, and we believe in the values of inclusion and belonging to inspire innovation.

Altos Labs provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. 

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. 

Altos currently requires all employees to be fully vaccinated against COVID-19, subject to legally required exemptions (e.g., due to a medical condition or sincerely-held religious belief).

Thank you for your interest in Altos Labs where we strive for a culture of scientific excellence, learning, and belonging.

Note: Altos Labs will not ask you to download a messaging app for an interview or outlay your own money to get started as an employee. If this sounds like your interaction with people claiming to be with Altos, it is not legitimate and has nothing to do with Altos. Learn more about a common job scam at https://www.linkedin.com/pulse/how-spot-avoid-online-job-scams-biron-clark/

Top Skills

C++
Python
PyTorch
Pytorch Lightning
TensorFlow

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