Machine Learning Engineer

Leash Biosciences•Salt Lake, UT

About The Position

At Leash Biosciences, we are at the cutting edge of integrating machine learning with drug discovery. Our unique approach focuses on predicting molecular and protein interactions, aiming to revolutionize the field of medicinal chemistry. Our team prides itself on its ability to generate and analyze vast datasets, directly contributing to groundbreaking advancements in drug development. We offer a supportive and inclusive environment, encouraging personal agency, collaboration, and sharing of knowledge. We're driven by an ambitious goal, and we aim to inspire each other to achieve groundbreaking results. We take big bets and are okay when only some of them pay off. Benefits include healthcare, 401K match, stock options, free lunches, and access to some of the best outdoor locations in the country. We are seeking a highly skilled and self-driven Machine Learning Engineer to join our team. In this role, you'll be instrumental in handling enormous datasets, orchestrating cloud-based computing resources, and training a multitude of advanced machine-learning models. Your work will directly contribute to our mission of creating foundational models for medicinal chemistry. While you will be dealing with massive amounts of chemical and biological information, biology and chemistry experience is not required. Our dataset can be thought of as billions of labeled sentences so experience with language models is highly relevant.

Requirements

Strong experience in machine learning engineering, including data handling, model training, and scaling in cloud environments.
Comfortable building ML infrastructure
Experience working with large amounts of text data, NLP, or training LLMs
Demonstrated capability to make informed decisions, take ownership of solutions, and drive projects forward in a startup environment.
Excellent collaboration skills, with the ability to work effectively with cross-functional teams.

Nice To Haves

Familiarity with common MLops tooling (e.g., Dagster, Prefect, Airflow, Docker, MLflow, Kubeflow, W&B, Ray, etc.)
Ability to manage own compute cluster
Ability to maximize GPU utilization and keep cluster busy 24/7
Ability to analyze model results and kick off new experiments in response
Experience with BERT or similar language models in PyTorch.
Experience or interest in biology, chemistry, or related fields is a plus.

Responsibilities

Manage and optimize data processing workflows for large-scale datasets, with an approach akin to language data handling.
Scale and maintain machine learning model training processes, with a focus on cloud environments (primarily Google Cloud, with flexibility to other platforms).
Collaborate closely with ML researchers, data scientists, and lab automation teams to ensure seamless integration of lab data and ML model training.
Innovate and iterate on our existing technology stack, taking the initiative to solve problems and improve our ML operations.
Act as a self-sufficient project manager, overseeing your projects from conception to completion.