Flagship Ventures
(Senior) Machine Learning Scientist,Materials Composition/Structure
Flagship Ventures, Cambridge, Massachusetts, us, 02140
Company Summary
Lila Sciences is a privately held, early-stage technology company pioneering the application of artificial intelligence to transform every aspect of the scientific method. Lila is backed by Flagship Pioneering, which brings the courage, long-term vision, and resources needed to realize unreasonable results. Join our mission-driven team and contribute to the future of science.
Our Physical Sciences effort is developing a novel AI and data-driven approach to materials discovery and development to accelerate the transition to a sustainable economy.
At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.
If this sounds like an environment you'd love to work in, even if you only have some of the experience listed below, please apply.
Responsibilities: Train, fine-tune and deploy deep learning models
connecting materials composition, structure and performance. Develop and train deep learning architectures for representation learning and generative AI over materials composition and structure. Develop physics-informed learning architectures and loss functions that capture conservation laws, and other invariances. Connect information retrieval with LLM tools and quantitative mathematical and physical symbolic reasoning. Develop and implement strategies to optimize machine learning models for
materials synthesis
and
performance
prediction. Utilize AI-backed methods for
lab orchestration , experimental assay design, and optimization of process parameters in
materials synthesis
and
testing . Contribute to a
digital platform
that continually
fine-tunes models
as more data becomes available, driving constant improvement. Work closely with experimental teams to drive
material discovery
and
development . Communicate findings to stakeholders through
written reports, slide decks
and
verbal presentations .
Must-Have Qualifications: Strong proficiency with PyTorch , including training and deploying models, preferably with experience in multi-GPU parallelization Demonstrated expertise in training supervised or unsupervised deep learning models, preferably on
structure
or
composition
of materials or chemicals-such as in
crystals ,
polymers , or
biomolecules . Expertise in including
physics-based inductive bias
in deep learning model architecture or loss function: conservation laws, symmetry (equivariance), functional form, PINNs, neural ODEs Proven track record of publishing
scientific papers
in scientific journals or ML conferences, or contributing to/creating
public code bases
related to machine learning and materials science. Proficiency in
Python
and the
data science ecosystem
(NumPy, SciPy, Pandas) and data visualization (matplotlib, plotly, etc). PhD
in Computer Science, Applied Mathematics, or a quantitative discipline, with a strong focus on machine learning. Excellent communication skills for conveying technical findings to diverse audiences. Preferred Qualifications:
Experience with
cloud computing services
(e.g., AWS) to optimize training and evaluation processes. Familiarity with integrating
machine learning
in
experimental workflows
within materials science or chemistry.
About Flagship
Flagship Pioneering is a platform innovation company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity's most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture. Flagship has been recognized twice on FORTUNE's "Change the World" list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company's annual list of the World's Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.
Flagship Pioneering and our ecosystem companies are
committed to equal employment opportunity
regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Recruitment & Staffing Agencies : Flagship Pioneering and its affiliated Flagship Lab companies (collectively, "FSP") do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.
Lila Sciences is a privately held, early-stage technology company pioneering the application of artificial intelligence to transform every aspect of the scientific method. Lila is backed by Flagship Pioneering, which brings the courage, long-term vision, and resources needed to realize unreasonable results. Join our mission-driven team and contribute to the future of science.
Our Physical Sciences effort is developing a novel AI and data-driven approach to materials discovery and development to accelerate the transition to a sustainable economy.
At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.
If this sounds like an environment you'd love to work in, even if you only have some of the experience listed below, please apply.
Responsibilities: Train, fine-tune and deploy deep learning models
connecting materials composition, structure and performance. Develop and train deep learning architectures for representation learning and generative AI over materials composition and structure. Develop physics-informed learning architectures and loss functions that capture conservation laws, and other invariances. Connect information retrieval with LLM tools and quantitative mathematical and physical symbolic reasoning. Develop and implement strategies to optimize machine learning models for
materials synthesis
and
performance
prediction. Utilize AI-backed methods for
lab orchestration , experimental assay design, and optimization of process parameters in
materials synthesis
and
testing . Contribute to a
digital platform
that continually
fine-tunes models
as more data becomes available, driving constant improvement. Work closely with experimental teams to drive
material discovery
and
development . Communicate findings to stakeholders through
written reports, slide decks
and
verbal presentations .
Must-Have Qualifications: Strong proficiency with PyTorch , including training and deploying models, preferably with experience in multi-GPU parallelization Demonstrated expertise in training supervised or unsupervised deep learning models, preferably on
structure
or
composition
of materials or chemicals-such as in
crystals ,
polymers , or
biomolecules . Expertise in including
physics-based inductive bias
in deep learning model architecture or loss function: conservation laws, symmetry (equivariance), functional form, PINNs, neural ODEs Proven track record of publishing
scientific papers
in scientific journals or ML conferences, or contributing to/creating
public code bases
related to machine learning and materials science. Proficiency in
Python
and the
data science ecosystem
(NumPy, SciPy, Pandas) and data visualization (matplotlib, plotly, etc). PhD
in Computer Science, Applied Mathematics, or a quantitative discipline, with a strong focus on machine learning. Excellent communication skills for conveying technical findings to diverse audiences. Preferred Qualifications:
Experience with
cloud computing services
(e.g., AWS) to optimize training and evaluation processes. Familiarity with integrating
machine learning
in
experimental workflows
within materials science or chemistry.
About Flagship
Flagship Pioneering is a platform innovation company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity's most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture. Flagship has been recognized twice on FORTUNE's "Change the World" list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company's annual list of the World's Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.
Flagship Pioneering and our ecosystem companies are
committed to equal employment opportunity
regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Recruitment & Staffing Agencies : Flagship Pioneering and its affiliated Flagship Lab companies (collectively, "FSP") do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.