---
title: 'Machine Learning Researcher - Agentic Science at PostEra'
canonical: 'https://feeny.ai/job/machine-learning-researcher-agentic-science-postera-remote-nw7qzty7v6qt'
type: 'job'
last_seen: '2026-10-18'
---

# Machine Learning Researcher - Agentic Science at PostEra

- **Company:** PostEra
- **Location:** Remote
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-09-28
- **Last confirmed live:** 2026-10-18
- **Apply:** https://jobs.lever.co/postera/c7d50a36-db0a-4574-bfe4-8700b74a22c0

## Job description

PostEra is building an AI-first biotech. We use Proton, our AI platform for medicinal chemistry, to accelerate the discovery of new medicines for patients. PostEra is advancing an internal pipeline focused on Women's Health and Fertility, and has used Proton to nominate multiple clinical candidates across PMOS and Fertility. PostEra also advances small molecule programs through partnerships with pharma. We've closed over $1B in AI partnerships including 4 multi-year agreements with Pfizer and Amgen. PostEra is also leading an antiviral drug discovery center for pandemic preparedness, funded by one of the largest grants in NIH history. PostEra's Organizational Structure We believe time spent navigating complex hierarchies in organizations is better invested in the pursuit of novel technology and scientific discoveries. As such, our organizational structure is intentionally minimalist. We promote a culture where individual achievement is celebrated through proportional compensation and internal recognition, including meaningful promotions. Ultimately, our collective focus is delivering cures to patients, which we believe needs a huge amount of cross-disciplinary collaboration, so we've fitted our organizational structure to serve that end.

## Role Overview

In this role, you will develop the agentic research vertical at PostEra, using agentic systems to automate the development of mechanistic models for biochemical and physiological processes, and analyse biological data for target validation. You will interact closely with chemists and biologists to use these models to drive drug discovery decisions. You will also develop machine learning methods that can rapidly adapt to new drug discovery problems from limited labeled data. A particular focus is molecular and tabular in-context learning and building foundation models from PostEra’s proprietary multimodal data. You will build models that use the context of drug discovery effectively, determine which prior examples and tasks are relevant, quantify when transfer is helpful or harmful, and provide reliable predictions under distribution shift. You will help drive the full research loop: defining tasks, constructing datasets and evaluation episodes, developing strong baselines, training and scaling models, performing rigorous ablations, and translating successful methods into capabilities used by PostEra’s scientists. Prior drug discovery experience is not required, but you should be motivated to learn the domain and work closely with medicinal chemists, computational chemists, and other scientists.

## Key Responsibilities

Agentic Research for Chemistry and Biology: Develop and benchmark agentic systems to automate the development of quantitative models to simulate biological and physiological processes, and the analysis of biological data. Research Direction and Execution: Independently identify, formulate, and lead research projects involving in-context learning, agentic systems, few-shot adaptation, tabular foundation models, and molecular machine learning. In-Context Learning for Molecules: Design and train models that adapt to new assays, endpoints, targets, or chemical series using limited labeled context and heterogeneous historical data. Benchmarking: Rigorously compare new approaches against strong baselines, curating test cases that deconfounds impact of different sources of bias. Cross-Functional Collaboration: Work with scientists to connect modeling objectives and evaluation metrics to practical decisions in potency modeling, ADME prediction, selectivity, lead optimization, and the design of early clinical studies. Model Training and Scaling: Develop efficient training and data pipelines and, where appropriate, scale models across large collections of molecular and tabular tasks. Research Engineering: Produce readable, reproducible research code; maintain well-tracked experiments; and contribute through code review, documentation, and shared modeling infrastructure. Scientific Dissemination: Publish results in leading machine learning, medicinal chemistry, or computational biology venues. You will be an ambassador of PostEra to the scientific community.

## Key Requirements

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, backed by understanding of the theory behind machine learning algorithms 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, including datasets, task definitions, baselines, metrics, and validation schemes Ability to design careful experiments, benchmarks, and ablations that distinguish improvements from biases, and understand which aspects of the model led to the improvements Comfort working in a startup environment where priorities evolve, data is imperfect, and high-quality judgment matters as much as raw model complexity Nice to Haves Candidates are more likely to succeed in this role if they also have experience with one or more of the following: 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 Moving research models into production scientific software or computational workflows

Salary: 200k - 300k Equity: 0.05 - 0.1% Visa Sponsorship: Not at this time Hiring Manager: Alpha Lee

Visit us at postera.ai

## About PostEra

## Company Overview
- **One-liner**: PostEra is an AI-first biopharmaceutical company using its Proton machine learning platform to accelerate medicinal chemistry and drug discovery for patients.
- **Entity Type**: Private (venture-backed; Y Combinator Winter 2020 batch)
- **Headquarters**: Cambridge, Massachusetts, USA (office also referred to as Boston, MA)
- **Founded**: 2019
- **Founders**: Aaron Morris (CEO), Alpha Lee (CSO), Matthew Robinson (CTO) — listed as active founders on Y Combinator and the PostEra team page

## Core Business
- **Primary industry**: AI-driven drug discovery / biopharmaceuticals / medicinal chemistry
- **Target customers**: B2B — large pharmaceutical companies (Pfizer, Amgen), government/public health (NIH), plus an internal drug pipeline for patients
- **Mission or purpose statement**: Use machine learning to close the Design-Make-Test cycle of medicinal chemistry, making drug discovery faster, more efficient, and ultimately delivering better medicines to patients

## Products & Services
- **Proton (AI Platform)**: End-to-end machine learning platform for medicinal chemistry. Supports all three stages of the drug discovery cycle — Design (using proprietary data + chemistry foundation models), Make (state-of-the-art synthesis and route design enabling parallel molecule production), and Test (active learning to gather the most informative data). Deployed both in internal pipelines and in pharma collaborations.
- **AI Drug Discovery Partnerships**: Multi-year collaborations with Pfizer — expanded to $610M in March 2024 — and Amgen, bringing total AI drug discovery collaborations to over $1B.
- **Internal Pipeline**: Advancing small-molecule programs focused on Women's Health and Reproductive Medicine.
- **Open Science / Pandemic Preparedness**: Creator and leader of COVID Moonshot, the world's largest open-science drug discovery initiative, and leader of a $68M antiviral drug discovery center for pandemic preparedness supported by the NIH.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Total funding of $26M raised; over $1B in AI drug discovery partnerships (Pfizer and Amgen combined)
- **Notable Investors/Partners**: Y Combinator (W2020), Pfizer, Amgen, NIH
- **Growth Signals**: Pfizer collaboration expanded to $610M (March 2024); Amgen collaboration added to reach $1B+ in AI partnerships; $68M NIH-backed antiviral center; ~40-person team per Y Combinator (with active hiring across drug discovery, ML, and engineering)

## Competitive Advantages
- **Proprietary AI platform (Proton)**: Combines proprietary experimental data with chemistry foundation models and active learning, giving a full-cycle, end-to-end advantage rather than point solutions.
- **Interpretable AI**: Explicitly emphasizes that its AI is "not a black box," building trust with regulatory/commercial pharma partners.
- **Real-world validation**: Practically proven via large, multi-year agreements with top-tier pharma (Pfizer, Amgen) and public-sector work (NIH, COVID Moonshot).
- **Open-science credibility**: Led the world's largest open-science drug discovery effort (COVID Moonshot), demonstrating ability to coordinate large-scale, mission-critical research.

## Strategic Focus
- Advancing an internal pipeline in Women's Health and Reproductive Medicine.
- Scaling AI partnerships with major pharma players (four confirmed multi-year agreements with Pfizer and Amgen).
- Continuing to improve Proton's design-make-test capabilities to shorten drug discovery timelines.
- Maintaining pandemic preparedness and open-science initiatives as a public-health differentiator.

## Why Work Here
- **Mission-driven**: Directly focused on bringing cures to patients faster through cross-disciplinary collaboration between drug hunters, researchers, and machine learning engineers.
- **Organizational philosophy**: Intentionally minimalist structure designed to reduce hierarchy and maximize time spent on science and technology; proportional compensation tied to individual achievement.
- **Remote-friendly**: All US-based roles are remote-eligible; physical office available in Cambridge, MA.
- **Benefits**: Comprehensive health, vision, and dental coverage plus life insurance; 20 days PTO and unlimited sick leave; 401K with up to 4% company match; generous budget for home office setup, co-working space, and a weekly lunch stipend; paid parental leave.

## Sources
1. [postera.ai](https://postera.ai/)
2. [postera.ai/about](https://postera.ai/about/)
3. [postera.ai/team](https://postera.ai/team/)
4. [ycombinator.com](https://www.ycombinator.com/companies/postera)
5. [jobs.lever.co](https://jobs.lever.co/postera/f89193c2-d755-47e9-95f3-3dc1c99f41e6)

## Other roles at PostEra

- [General Interest](https://feeny.ai/job/general-interest-postera-remote-vqge8s8xaq10)