Machine Learning Engineer

New York, New York, United States

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$110,000 – $250,000Compensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI & Machine Learning, Biotechnology, Data & AnalyticsIndustries

Requirements

Candidates should have a Bachelor's degree in Computer Science, Machine Learning, or a related technical field. They must possess 3+ years of experience in developing and implementing machine learning models and be proficient in Python with expertise in at least one major deep learning framework such as PyTorch, TensorFlow, or JAX. Experience with deep learning and generative architectures, handling large-scale datasets, and distributed computing environments is essential, along with familiarity with AWS for training, inference, and deployment. A strong understanding of machine learning fundamentals, efficient data pipeline design, and robust evaluation frameworks is required, as well as experience in collaborative software development practices and excellent problem-solving skills.

Responsibilities

The Machine Learning Engineer will design and implement advanced machine learning algorithms to enhance model performance and biological understanding. They will develop and fine-tune generative models for biological applications and work on distributed training systems to scale models to billions of parameters. Responsibilities include engineering efficient data pipelines for massive biological datasets and developing robust evaluation frameworks for complex biological models. The engineer will also ensure data integrity and prevent leakage across dataset splits while collaborating with team members to tackle challenges and adapt to new situations.

Skills

Python

PyTorch

TensorFlow

JAX

Transformers

Diffusion Models

Autoencoders

Machine Learning

Deep Learning

Data Pipelines

AWS

Data Handling

Model Evaluation

Version Control

Software Development

Output Biosciences

Develops preventative therapies for chronic diseases

About Output Biosciences

Output Biosciences focuses on developing preventative therapies aimed at extending human healthspan by addressing chronic diseases such as diabetes and heart disease. The company combines biotechnology with artificial intelligence to create therapies that can be discovered and manufactured more quickly and safely than traditional methods. This allows them to bring new treatments to market in just a few months, which is much faster than the usual drug development process. Their main clients include healthcare providers, pharmaceutical companies, and research institutions seeking effective solutions for chronic disease management. Output Biosciences differentiates itself by leveraging computational biology to streamline therapy development and commercialization, generating revenue through partnerships and licensing. The goal of Output Biosciences is to fundamentally change healthcare by preventing chronic diseases before they occur.

New York City, New YorkHeadquarters

2020Year Founded

$125KTotal Funding

PRE_SEEDCompany Stage

AI & Machine Learning, Biotechnology, HealthcareIndustries

1-10Employees

Risks

Increased competition from AI-driven biotech startups threatens market share.

Regulatory scrutiny on AI applications may delay approval processes.

Rapid technological advancements may render current methods obsolete without continuous innovation.

Differentiation

Output Biosciences integrates AI and computational biology for rapid therapy development.

The company focuses on preventative therapeutics to extend human healthspan.

Output Biosciences accelerates drug development timelines, bringing therapies to market in months.

Upsides

Growing interest in AI-driven drug discovery boosts investment and partnerships.

Rise of personalized medicine creates opportunities for tailored AI-integrated therapies.

FDA's acceptance of AI-driven drug development enables faster regulatory pathways.

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