We enable any scientist to access AI-powered drug discovery. Thousands of scientists from large pharma companies, top biotechs, and academic institutions use Tamarind to design protein drugs, improve industrial enzymes, and create cutting edge molecules that weren’t feasible until now.
New AI models are quickly eclipsing physics-based tools in computational drug discovery. Scientists often struggle to fine-tune, deploy, and scale these models, leaving breakthroughs on the table. Tamarind provides a simple interface to the vast array of tools being released daily.
💻About the Role
We're looking for two Infrastructure Engineers to lead the scaling of our machine learning inference system. You'll be responsible for architecting and maintaining infrastructure that serves 150+ biological ML models, scaling our platform several orders of magnitude to meet rapidly growing demand.
You’ll work closely with the founders to design to the constraints of customer needs, unpredictable workloads, and unique Bio-ML models. You'll work with Kubernetes and other tools to orchestrate containerized workloads, optimize resource allocation, and ensure high availability across our model serving infrastructure.
Most importantly, you should thrive in a fast-paced startup environment where you'll wear multiple hats, learn new technologies quickly, and help solve novel technical challenges. We value engineering judgment, problem-solving ability, and the capacity to build systems that can evolve with our growing needs.
Techstack:
Python, React, AWS (EC2, S3, DynamoDB), Docker, CUDA, Conda, TensorFlow/PyTorch; notebooks; bash/Slurm; APIs & web apps.
Requirements
Solid programming and automation skills
Experience with containerization and orchestration concepts
Cloud platform knowledge (AWS/GCP/Azure)
Located in the SF Bay Area or able to relocate to the Bay Area
Onsite expectation: Team currently onsite in SF ~5 days/week.
Preferred
Experience scaling production systems
Kubernetes experience
Infrastructure as code tools (Terraform, Pulumi)
Monitoring and observability tools
Experience with GPU workloads
Technology
Our technology sits at the intersection of DevOps, MLOps, and Computational Biology. We deal with problems ranging from scaling ML inference on AWS for hundreds of GPUs to dissecting pdb files with Biopython. We deploy a wide range of open source ML models for customers, navigating between Docker containers, Colab notebooks, bash scripts, slurm jobs, and more.
🧩 Our Interview Process
We keep our process focused, transparent, and designed to give both sides a clear sense of fit.
1. Recruiter Screen (15–30 minutes) — Virtual
Meet with our recruiter to dive into your background, interests, and what you’re looking for next. We’ll also walk you through the company, team, and role.
2. Technical Interview (90 minutes) — Virtual
Co-Founder Interview (30 minutes): A conversation with Deniz Kavi (CEO & Co-founder) about product, collaboration, and how you approach building in an early-stage environment.
Technical Deep Dive (60 minutes): A live coding and problem-solving session with Sherry Liu (CTO & Co-founder) or a member of our engineering team.
3. Onsite (1 day) — San Francisco
Spend a day with us working on a mini project and meeting the team.