AnuBio

Anu Bio is a techbio startup focused on reimagining drug discovery using AI to develop immunotherapies and antibody-based drugs. Their proprietary AI architecture enables data-driven decision making…

AIAntibody-Based DrugsCellular CommunicationClinical Trial SimulationDigital TwinDrug DiscoveryImmunotherapyPersonalized Medicineanubio.ai

AnuBio

AIAntibody-Based DrugsCellular CommunicationClinical Trial SimulationDigital TwinDrug DiscoveryImmunotherapyPersonalized Medicineanubio.ai

HQRemote

Team Size5

Open Jobs3

Total Funding-

Latest FundraiseUnknown

Join the Team

Machine Learning Scientist

On-SiteBengaluru, Karnataka, IN

On-Site • Bengaluru, Karnataka, IN

Startup jobs. A lot of them.

Your next opportunity is in here somewhere. Sign up to explore 70,000+ startups and their open roles. No spam. No gamification. Just jobs.

70,000+

Startups

81,000+

Open Roles

4,600+

New This Week

Mobile Developer

ContractAustin, US

Contract • Austin, US

Machine Learning Engineer

Full-timeAmsterdam, NL

Full-time • Amsterdam, NL

Machine Learning Engineer

ContractMunich, DE

Contract • Munich, DE

Technical Writer

Full-timeUtrecht, NL

Full-time • Utrecht, NL

DevOps Engineer

ContractHaifa

Contract • Haifa

Software Engineer

Part-timeNew York, US

Part-time • New York, US

Machine Learning Scientist

Carot labs (dba AnuBio)

📍 Chennai/Bengaluru, India

🌐 https://anubio.ai

🕒 Full-time

About Anubio:

AnuBio is an AI-driven deeptech company building next-generation multicellular foundation models for immune systems. Our platform, TRAILBLAZER™, creates predictive “digital twins” of patient immunity—enabling discovery of new therapies without predefined molecular targets.

About the role:

We’re building generative models that predict how cell populations respond to therapeutic interventions, at single-cell resolution and patient scale. Our work sits at the intersection of representation learning, generative modeling, and translational biology, and is used to prioritize treatments and simulate virtual clinical trials. We’re looking for an ML scientist who can own model architecture and training end-to-end and push the research forward alongside our team. This is a core ML role.

Strong modeling instincts and rigor matter most, but curiosity about the biological domain will help you do your best work.

What you’ll do:

Required qualifications:

Nice to have:

Experience with single-cell genomics or other high-dimensional biological data such as scRNA-seq.
Familiarity with computational biology tooling (e.g. Scanpy, AnnData, scVI) and single-cell foundation models.
Publications or open-source contributions in ML or comp-bio.

What we offer:

Close collaboration with founders and an elite ML team.

Competitive salary.

Apply:

Email your CV and GitHub/Portfolio to naveen@anubio.ai

Anubio is an equal opportunity employer. We hire based on merit and scientific rigor.

Design, implement and train deep generative models

(VAEs, transformers, and related architectures) on large-scale, high-dimensional data.

Shape and constrain latent spaces

(e.g. metric learning, contrastive objectives) so that learned latent spaces are controllable and support extrapolation to unseen states.

Build count-aware decoders

and likelihood heads (e.g. negative-binomial / zero-inflated parameterizations) and design composite training objectives with staged optimization.

Construct careful data pipelines

and samplers that prevent shortcut learning and control for confounders.

Design rigorous benchmarks: zero-/few-shot generalization, ablations, and comparisons against state-of-the-art baselines.

Translate model outputs into actionable predictions and rankings, and collaborate with domain experts to validate them.

MS or PhD in computer science, machine learning, statistics, physics, or a related quantitative field.

Strong track record building and training deep neural networks in PyTorch.

Solid grounding in generative modeling

(VAEs, diffusion, flow-based, or auto regressive) and in modern transformer/attention architectures.

Comfort with the math behind the methods: probability, linear algebra, optimization, and metric/representation learning.

Experience working with large datasets and the engineering practices (efficient data loading, distributed/GPU training, reproducibility) that make large-scale training tractable.

Ability to design clean experiments, interpret results honestly, and communicate findings clearly.