or apply directly on Postera's site. We never take the application ourselves.
Is this posting real?
- This role has been open
- 0 days Postera's roles stay open a median of 32 days
- Reposted
- No
- Salary listed
- No 0% of Postera's roles list one
- Ghost-job risk at Postera
- high 1 stale, 0 reposted of 2 open
- Hiring momentum
- 3 roles opened in the last 90 days ↑ up vs. the prior 90 days
- Last confirmed on the employer's board
- 2026-10-06
Measured from postings appearing on and disappearing from Postera's own lever board since 2026-08-04. Full hiring picture for Postera.
About this role
As a Machine Learning Researcher at PostEra, you will focus on developing agentic systems to automate the creation of mechanistic models for biochemical and physiological processes, while analyzing biological data to support drug discovery. Your role involves collaborating with chemists and biologists, designing machine learning methods for drug discovery problems, and driving the full research loop from task definition to model training and evaluation. You will also be responsible for publishing your research findings and contributing to the scientific community.
- benefits
- 2/5
- freshness
- 5/5
- career value
- 5/5
- role clarity
- 5/5
- pay transparency
- 0/5
Scored from the posting itself — how clearly the role is described, how much it says about pay and benefits, and how recently it was listed. Not a judgement of Postera as an employer.
What you need
- PhD degree in machine learning or STEM research involving the development of novel machine learning approaches
- Track record of high-quality research, such as publications and open-source contributions
- Strong research or engineering experience in modern machine learning, deep learning, or statistical modeling
- Demonstrated expertise in at least one relevant area: agentic workflows for science, machine learning approaches to bioinformatics and clinical data modelling, in-context learning, tabular learning, few-shot learning
- Hands-on experience training, debugging, and evaluating ML models in Python using frameworks such as PyTorch or JAX
- Ability to independently translate ambiguous scientific or technical problems into well-defined ML projects
Nice to have
- Training tabular foundation models, particularly for sparse, heterogeneous, small-data, or high-missingness settings
- Developing molecular in-context learning systems or adapting general-purpose in-context models to molecular or scientific data
- Large model training, including 1B+ parameter models, distributed training, sharding, data parallelism, model parallelism, and large-scale data pipelines
- The development of AI 'co-scientist' systems for physical or biological problems
- Hands-on experience in modelling biological, biochemical or clinical data using machine learning approaches
What you get
- Salary: 200k - 300k
- Equity: 0.05 - 0.1%
- Visa Sponsorship: Not at this time
Worth weighing
- No specific mention of prior drug discovery experience required, but a motivation to learn the domain is necessary
- The role involves a significant amount of cross-disciplinary collaboration which may require adaptability
- The startup environment may lead to evolving priorities and imperfect data, which could impact project direction
Summarised from Postera's posting. Read the full original.
Listed by Postera on their lever job board, last confirmed open on 2026-10-06. PitchMeAI is not the employer.
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