Applied ML Engineer/Scientist
London
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingAs an Applied ML Engineer/Scientist, you will apply and adapt Boltz’s foundational machine learning models to real-world drug discovery problems, working directly on projects with external partners.
You’ll collaborate closely with ML researchers, software engineers, and domain experts in chemistry and biology to translate partner needs into concrete modeling strategies.
You’ll collaborate closely with ML researchers, software engineers, and domain experts in chemistry and biology to translate partner needs into concrete modeling strategies.
You’ll own the end-to-end applied modeling loop from problem formulation and experimentation to evaluation and delivery.
From the employer’s posting
As an Applied ML Engineer/Scientist, you will apply and adapt Boltz’s foundational machine learning models to real-world drug discovery problems, working directly on projects with external partners. Your focus will be on finding the most effective ways to fine-tune, condition, and deploy state-of-the-art models for specific scientific use cases in molecular modeling and design.
You’ll collaborate closely with ML researchers, software engineers, and domain experts in chemistry and biology to translate partner needs into concrete modeling strategies. This includes curating and adapting datasets, selecting and tuning model architectures and objectives, and iterating rapidly to maximize performance on applied tasks. You’ll own the end-to-end applied modeling loop from problem formulation and experimentation to evaluation and delivery.
As an Applied ML Engineer/Scientist, you will apply and adapt Boltz’s foundational machine learning models to real-world drug discovery problems, working directly on projects with external partners. Your focus will be on finding the most effective ways to fine-tune, condition, and deploy state-of-the-art models for specific scientific use cases in molecular modeling and design. You’ll collaborate closely with ML researchers, software engineers, and domain experts in chemistry and biology to translate partner needs into concrete modeling strategies. This includes curating and adapting datasets, selecting and tuning model architectures and objectives, and iterating rapidly to maximize performance on applied tasks. You’ll own the end-to-end applied modeling loop from problem formulation and experimentation to evaluation and delivery. You’ll also feed applied learnings back into core model development, identifying gaps and failure modes in practice and contributing concrete improvements that strengthen Boltz’s foundational models.
What you’ll bring
All qualificationsCore experience
- Familiarity with datasets, tools, data formats, and workflows commonly used in computational biology and chemistry.
- Experience collaborating with external partners or stakeholders, translating biological or chemical questions into concrete modeling and evaluation strategies.
- Experience contributing to and maintaining deep-learning codebases, with a high bar for engineering quality, reproducibility, and testing.
- Experience working in an interdisciplinary scientific environment, especially across ML, biology, chemistry, and physics.
Qualification wording
Familiarity with datasets, tools, data formats, and workflows commonly used in computational biology and chemistry.
Experience collaborating with external partners or stakeholders, translating biological or chemical questions into concrete modeling and evaluation strategies.
Experience contributing to and maintaining deep-learning codebases, with a high bar for engineering quality, reproducibility, and testing.
Experience working in an interdisciplinary scientific environment, especially across ML, biology, chemistry, and physics.
Tools in this posting
- Python
- NumPy
- pandas
- PyTorch
- Scipy
Source — Tool mentions in context
- Familiarity with datasets, tools, data formats, and workflows commonly used in computational biology and chemistry. - Strong hands-on experience with PyTorch and the scientific Python ecosystem (NumPy, SciPy, Pandas, etc.). - Experience contributing to and maintaining deep-learning codebases, with a high bar for engineering quality, reproducibility, and testing.
About Boltz
We provide the compute, the scalable infrastructure, and the collaboration layer, so scientists can iterate faster and stay focused.
In the employer’s words · Read in context
Job description
About Boltz
Boltz is a public benefit company building the next generation of AI-powered molecular modeling tools to make biology programmable and accelerate drug discovery, while keeping frontier capabilities broadly accessible.
Boltz-1, Boltz-2, and BoltzGen are open models trusted by 100,000+ scientists across biotech and academia, and used in programs at every Top 20 pharma as well as leading agrichemical and industrial research organizations.
We deliver these capabilities through Boltz Lab, our platform for running our latest models and design agents as reliable, production-grade tools. Boltz Lab is designed around real chemistry and biology workflows, so teams can start from a target and a hypothesis and quickly generate, evaluate, and rank candidate molecules. We provide the compute, the scalable infrastructure, and the collaboration layer, so scientists can iterate faster and stay focused.
You can read more about our mission, research and product vision on our manifesto.
About the role
As an Applied ML Engineer/Scientist, you will apply and adapt Boltz’s foundational machine learning models to real-world drug discovery problems, working directly on projects with external partners. Your focus will be on finding the most effective ways to fine-tune, condition, and deploy state-of-the-art models for specific scientific use cases in molecular modeling and design.
You’ll collaborate closely with ML researchers, software engineers, and domain experts in chemistry and biology to translate partner needs into concrete modeling strategies. This includes curating and adapting datasets, selecting and tuning model architectures and objectives, and iterating rapidly to maximize performance on applied tasks. You’ll own the end-to-end applied modeling loop from problem formulation and experimentation to evaluation and delivery.
You’ll also feed applied learnings back into core model development, identifying gaps and failure modes in practice and contributing concrete improvements that strengthen Boltz’s foundational models.
This role is ideal for someone who enjoys operating at the interface between cutting-edge ML and real-world deployment: a technically strong, execution-focused scientist or engineer who wants to turn powerful foundational models into reliable, high-impact capabilities for partners and end users.
About you
Essentials:
- Strong hands-on experience applying machine learning to real-world problems, including applied ML in biology, chemistry, or drug discovery.
- Familiarity with datasets, tools, data formats, and workflows commonly used in computational biology and chemistry.
- Strong hands-on experience with PyTorch and the scientific Python ecosystem (NumPy, SciPy, Pandas, etc.).
- Experience contributing to and maintaining deep-learning codebases, with a high bar for engineering quality, reproducibility, and testing.
Nice to have:
- Experience collaborating with external partners or stakeholders, translating biological or chemical questions into concrete modeling and evaluation strategies.
- Strong publication record in ML or life-science venues (e.g. NeurIPS, ICLR, ICML, Nature Methods), especially where research was driven by applied or translational goals.
- Experience working in an interdisciplinary scientific environment, especially across ML, biology, chemistry, and physics.
What we offer
- Opportunity to drive outsized real-world impact by building tools that empower thousands of scientists across the industry.
- Work alongside one of the most talent-dense teams in the field.
- Significant ownership and independence, with responsibility for driving projects from concept to deployment.
- Highly competitive salary with substantial equity ownership.
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
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Source & posting history
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
London
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- Work authorization
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- Status in our records
- Active
- First seen by us
- Jun 2, 2026
- Recorded sightings
- 50
- Last seen by us
- Oct 2, 2026
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