Karl Heyer

Machine Learning Engineer with a focus on drug discovery and bioscience

San Francisco, CA

Los Angeles, CA

Honolulu, HI

LinkedIn
KH

About

As a Machine Learning Engineer, I have successfully deployed high impact solutions to problems in drug discovery, RNA-Seq, small molecule design, and more. I leverage my background as a wet lab scientist to work effectively between developer teams and scientist teams, ensuring critical domain expertise is incorporated into ML solutions. Currently I work mainly in Python, PyTorch and the AWS stack.

Work Experience

Darkmatter AI
Remote

2022 - Present

Founder

Developed open source python libraries applying machine learning and generative AI to problems in cheminformatics and drug design. Technologies: PyTorch, Generative AI, LLMs, RDKit, FastAPI, Docker

Independent Contractor
Remote

2022 - 2023

Machine Learning Engineer

Worked for client companies developing in-house PyTorch libraries, ETL data pipelines, and production systems for training and deploying machine learning models. Technologies: PyTorch, Python, RDKit, RabbitMQ , Docker, Kubernetes, AWS (EC2, Lambda, Sagemaker, S3)

Neumora
Remote

2021 - 2021

Data Scientist

Analyzed RNA-Seq data to derive insights for patient subtyping, drug selection and improved treatment outcomes for mental health therapeutics. Technologies: R, Bioconductor

BlackThorn Therapeutics (Acquired by Neumora)
Remote

2020 - 2021

Data Scientist

Designed, developed and productionized BlackThorn's chem/ML systems to support multiple drug design programs with a suite of tools including data tracking/warehouse, ETL pipelines, predictive models, and generative models for drug design. Technologies: Python, PyTorch, RDKit, Docker, Kubernetes, AWS (EC2, Sagemaker, S3, Redshift)

Zymergen (Acquired by Ginkgo Bioworks)

2017 - 2019

Research Associate

As a member of the R&D team, I helped develop, scale, and productionize novel molecular biology methods for large scale pooled DNA assembly and high throughput bioinformatics analysis pipelines. I worked across the full scale range from microliter bench top scale to factory production.

Education

University of Southern California

2016 - 2017
Master's Degree in Chemical Engineering and Materials Science (GPA 3.85)

University of Southern California

2012 - 2016
Bachelor's Degree in Chemical Engineering (GPA 3.9)

Skills

Python
PyTorch
Numpy
scikit-learn
HuggingFace
LLMs
Generative AI
Vector Databases
Diffusion Models
PostgreSQL
MongoDB
Docker
Kubernetes
Redis
FastAPI
AWS
RDKit
Schrodinger
CCDC
Autodock Vina

Projects

Vector Virtual Screen

A platform for using vector databases to accelerate virtual screening for drug discovery

PyTorch
Qdrant
MongoDB
Redis
Docker
Python
FastAPI

CPU Embedding Server

A containerized FastAPI server for computing embeddings on CPU, optimized for concurrent requests

Python
FastAPI
Pytorch
HuggingFace
Docker

Roberta Zinc 480m

A model trained on 480m SMILES strings to compute meaningful molecule embeddings

Pytorch
HuggingFace

GPT Zinc 87m

A GPT-2 style generative model trained on 480m SMILES strings to generate molecular compounds

Pytorch
HuggingFace

Emb Opt

A python library for running hill climbing algorithms in embedding spaces, with a focus on searching vector databases

Python
Numpy

Emb Opt Server

A multi-container service for running search with the Emb Opt library, including a RESTful API server, backend database, job queue, and worker container

FastAPI
Docker
MongoDB
Redis

Chem Templates

A flexible and extensible python library for filtering large molecular libraries and defining chemical spaces

Python
RDKit

Chem Templates Server

A FastAPI inference server for the Chem Templates library with a focus on scalability and parallel processing

FastAPI
Docker
MongoDB

Molecular Reinforcement Learning

A python library for designing molecular compounds using a combination of machine learning and generative AI

Python
Pytorch
RDKit

In-House ML Library

A custom machine learning library for a scientist team with a focus on auto-ML and usability by lab scientists with little programming background

Contract
Python
Pytorch
scikit-learn

GB-GA Molecular Design

A production system for molecular design using graph based genetic algorithms (GB-GA) compatible with an arbitrary reward function

Contract
Python
RDKit
Docker

ML Inference Endpoint Templates

A template library to help scientists deploy custom ML models as docker containers using ECS and Sagemaker

Contract
Python
Docker

Molecule Vector Database

Built a vector database system for large chemical libraries, including vector database backend, RESTful API query server, and batch process embedding computation

Contract
Python
Pytorch
Qdrant

RNA-Seq Patient Subtyping

Used RNA-Seq analysis to identify patient subtypes in patient populations with mental health indicators

Neumora
R
Bioconductor

Autoscaling Docking Service

Built an autoscaling kubernetes service running CCDC molecular docking

BTRX
Python
Docker
Kubernetes
CCDC

Reinforcement Learning Molecular Design

Developed a system combining generative AI and reinforcement learning to design optimized molecules for multiple drug programs

BTRX
Pytorch
RDKit

ADMET Prediction Pipeline

Deployed a production system to predict ADMET properties of molecules using in-house laboratory data

BTRX
Pytorch
RDKit

Pooled DNA Assembly

A molecular biology technique for assembling DNA plasmids in a deterministic multiplexed fashion

Zymergen