--- title: 'CADD / Application Scientist at Achira' canonical: 'https://feeny.ai/job/cadd-application-scientist-achira-new-york-7sw69t5gkw6h' type: 'job' last_seen: '2026-09-12' --- # CADD / Application Scientist at Achira - **Company:** Achira - **Location:** New York, NY - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-05-27 - **Last confirmed live:** 2026-09-12 - **Apply:** https://jobs.ashbyhq.com/achira/0a301a58-64ea-458e-8378-f5a90d0367d8 ## Job description ## Why Achira At Achira, you will join a team of scientists, ML researchers, and engineers working together to move beyond the beaten path in drug discovery. We are developing physics-grounded models for molecular simulation that can make the chemical and biological systems behind drug discovery more learnable, predictable, and designable. You will be part of the journey from our first protein-ligand applications in potency and lead optimization toward a broader vision: bringing more of the wet lab in silico, from selectivity and ADMET to eventual de novo molecular design. You will work at the frontier of AI x chemistry in a well-funded, talent-dense organization that values rigor, speed, execution, ownership, and the shared urgency required to turn ambitious models into real scientific tools. ## About the Role We are looking for CADD / Application Scientists who want to help define the next generation of computational drug discovery tools, not just operate the current one. You will be a scientific design partner for our model and training teams, bringing real discovery program experience into what we train on, what model behaviors matter, and which applications are worth building toward. As the models mature, you will also help bring these tools to pharma and biotech partners, shaping collaborations around problems where better molecular simulation could change real program decisions. ## What You’ll Do - Shape training data strategy for our models: identify which experimental, structural, partner-accessible, and synthetic data sources are likely to improve affinity prediction, selectivity, generalization, and downstream drug discovery utility. - Own data and structure curation for high-value protein-ligand systems end-to-end: connect affinity measurements to assay context, prepare structures, assign protein and ligand states, review or generate poses, and label assumptions and uncertainty. - Work with model and training teams to interpret model successes and failures, separating data problems, setup problems, and model limitations. - Help decide which applications are worth pursuing, from lead optimization and selectivity to pose assessment, scaffold transfer, affinity prediction, hit rescoring, and future extensions beyond potency. - Shape partner programs with BD and leadership, translating model capabilities into scientifically credible collaborations with pharma and biotech teams. ## About You - You have significant experience in CADD, structure-based drug design, computational chemistry, medicinal chemistry collaboration, or related work in drug discovery. - You have experience supporting, or leading external collaborations with pharma, biotech, or discovery partners. - You have strong intuition for protein-ligand binding, ligand poses, assay artifacts, protonation / tautomer states, waters, cofactors, ligand strain, and where modeling workflows quietly go wrong. - You are comfortable making expert judgments from imperfect data and can tell which benchmarks, application ideas, or partner case studies would matter to a real discovery team. - You are excited to work closely with ML researchers, simulation scientists, and platform teams. ## Nice to Have Even if you hit none of these bonus features, we encourage you to apply. - Experience curating and running protein-ligand affinity or FEP benchmarks, including OpenFE or related evaluation efforts. - Experience across the full discovery arc from target identification through hit finding, hit-to-lead, and lead optimization. - Experience designing scientific case studies, technical reports, or partner-facing demonstrations for new computational methods. - Familiarity with ML-assisted drug discovery, active learning, synthetic data generation, or model evaluation for molecular systems. ## About Achira ## Company Overview - **One-liner**: Achira builds atomistic foundation simulation models that blend geometric deep learning, generative AI, physics, and quantum chemistry to power the next generation of drug discovery and materials design. - **Entity Type**: Private (Seed Stage) - **Headquarters**: New York, New York, United States - **Founded**: 2024 - **Founders**: John Chodera (CEO/President), Theofanis Karaletsos, Zavain Dar (Board) ## Core Business - **Primary industry**: Biotechnology Research / AI-driven Drug Discovery - **Target customers**: Pharmaceutical and biotech R&D teams (B2B); drug discovery scientists seeking advanced computational tools - **Mission or purpose statement**: To create a fundamentally new way of modeling and simulating the world at the atomistic scale, turning intractable extrapolation problems into tractable interpolation problems by learning the underlying physical laws that govern matter. ## Products & Services - **Molecular World Models**: A new class of foundation simulation models that blend geometric deep learning, generative AI, physics, quantum chemistry, and statistical mechanics. These models are designed to deliver unprecedented performance across biomolecular applications, transforming drug discovery and materials design into true inverse design problems. - **Agentic Discovery Platform (in partnership with NVIDIA)**: Announced at BIO2026 (June 2026), this platform advances molecular world models for agentic discovery, leveraging NVIDIA's computing infrastructure. ## Market Standing - **Valuation/Market Cap**: Not disclosed (private company) - **Key Metric**: **$33M in total funding** (Seed round, March 2025) - **Notable Investors/Partners**: Dimension, Amplify, NVIDIA, Compound - **Growth Signals**: - Headcount of 17 employees (+54.5% YoY, +6 people) - 82% of staff in technical roles (machine learners, quantum chemists, engineers) - High-profile hires from D. E. Shaw Research, Chan Zuckerberg Initiative, and other top computational science teams - Partnership with NVIDIA announced at BIO2026 ## Competitive Advantages - **Third-path approach**: Unlike pure ML methods (which are data-hungry) or traditional molecular dynamics (which is computationally limited), Achira's models learn the underlying physical laws, making them exceptionally data-efficient for out-of-distribution prediction. - **World-class team**: Founders and technical staff with deep expertise in quantum chemistry, machine learning, and drug discovery, drawn from top institutions. - **NVIDIA partnership**: Direct access to cutting-edge computing infrastructure and co-development resources. - **Foundational technology**: Their approach is positioned as a platform technology applicable across drug discovery and materials science, not just a single-product solution. ## Strategic Focus - **Current priorities**: Building and scaling molecular world models; advancing agentic discovery capabilities; expanding the team of machine learners and software engineers; partnering with drug discovery scientists to validate and deploy models in real-world scenarios. - **Direction for growth**: To become the foundational computational layer for programming matter, enabling inverse design across biomolecular and materials applications. ## Why Work Here - **Culture highlights**: A team of "machine learners, quantum chemists, engineers, computer scientists, and entrepreneurs" united by an "uncompromising drive to build the foundational technological substrate for programming matter." Emphasis on intellectual ambition and scientific rigor. - **Remote/hybrid/office policy**: Presence in New York, NY (HQ) and Italy. Specific policy not stated, but the team is currently 94% US-based. - **Notable perks or engineering culture**: Opportunity to work on the frontier of AI + physical sciences; high concentration of technical talent (82% of staff); direct collaboration with NVIDIA; chance to shape a foundational technology from an early stage. The company actively seeks "machine learners and software engineers" and encourages applicants even if no perfect role is listed. ## Sources 1. [achira.ai](https://achira.ai/) 2. [achira.ai/about](https://achira.ai/about/) 3. [achira.ai/careers](https://achira.ai/careers/) 4. [LinkedIn](https://www.linkedin.com/company/achira-ai) 5. [datanyze.com](https://www.datanyze.com/companies/achira/5000059998) ## Other roles at Achira - [People Operations Manager](https://feeny.ai/job/people-operations-manager-achira-san-francisco-2ww83xn8nzc3) — San Francisco, CA - [ML Research Scientist (MLRS) - Representation Learning for Molecular AI](https://feeny.ai/job/ml-research-scientist-mlrs-representation-learning-for-molecular-ai-achira-san-g4avp0c6b7k4) — San Francisco, CA - [ML Research Scientist (MLRS) - Generative AI](https://feeny.ai/job/ml-research-scientist-mlrs-generative-ai-achira-san-francisco-6ye40kf8h9a1) — San Francisco, CA - [Machine Learning Research Engineer (MLRE) - GPUs](https://feeny.ai/job/machine-learning-research-engineer-mlre-gpus-achira-san-francisco-j6mm7fjed50y) — San Francisco, CA - [Machine Learning Research Engineer (MLRE) - Workflows/Systems](https://feeny.ai/job/machine-learning-research-engineer-mlre-workflows-systems-achira-san-francisco-0vf8drtv0h8m) — San Francisco, CA - [Machine Learning Research Engineer (MLRE) - Research](https://feeny.ai/job/machine-learning-research-engineer-mlre-research-achira-san-francisco-nh4m4asmxj8h) — San Francisco, CA - [SWE - Distributed](https://feeny.ai/job/swe-distributed-achira-san-francisco-d8wh3r550t76) — San Francisco, CA