Machine Learning Research Engineer

Syntensor Group; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

Join Syntensor Group as an ML Research Engineer to work on revolutionary projects in drug discovery using large-scale representation learning methods on genomic datasets. This fully remote role offers competitive compensation, stock options, and the opportunity to work with leading experts while contributing to impactful research that aims to transform medicine accessibility and efficacy.

Benefits

Top quartile salary

Stock options

401K

Health, dental and vision insurance

Qualifications

• 4+ years in applied machine learning and software engineering.
• 2+ years experience if holding a PhD.
• Experience with biological systems and drug discovery.

Responsibilities

• Develop foundational models based on biological data for drug discovery.
• Productionalize research code and add features to models.
• Identify and maintain model performance metrics.

Skills

Experience with large scale representation learning

Experience with long context sequence models

Python Expertise

Knowledge of statistics for scientific data

Experience with ML + data science libraries

Education

Masters in Computer Science, Engineering, or other STEM field

PhD in Computer Science, Engineering, or other STEM field

Tools

AWS

Git

Job description

Representation Learning | Long Context Sequence Modeling | Tabular Data | Statistics | Software Engineering | Biological Systems | Genomics

Fully remote | Top quartile salary | Stock options | 401K | Health, dental and vision insurance

We are no longer hiring for this role, but please feel free to reach out if you are interested in joining our talent pool.

We are hiring an ML research engineer to interface large-scale representation learning methods with experimental genomic and transcriptomic datasets. Fully remote.

Join a team of world-leading experts in this domain and help drive the field forward as we tackle a problem space only recently made tractable by advances in ML and HPC.

The role

• Use your expertise to develop foundational models based on biological and biochemical data corpuses for applications in early stage drug discovery
• Productionalize research code and add features to state of the art models
• Implement and train baseline models for downstream tasks
• Identify, implement, and maintain model performance metrics and indicators
• Lead and/or contribute to publications in top-tier journals
• Stay up to date with relevant research and industry trends for integration into our products
• Take on a grand challenge and share our purposeful mission - we’re building a biological systems simulator that will transform how medicines are developed - you will be making it accessible and powerful for scientists and doctors, with a big downstream impact on the wellbeing of millions of patients
• Competitive compensation - A starting salary in the top quartile for role and level, based on local benchmarks
• Stock options - you are joining an early-stage startup we want you to have ‘skin in the game’ and your options package will reflect this
• Self-manage - we are a distributed-first company, working from home and collaborating asynchronously from Seattle, NY, Vermont, and Manchester

About you

• Masters in Computer Science, Engineering, or other STEM field and 4+ years of experience in applied machine learning and software engineering
• PhD in Computer Science, Engineering, or other STEM field and 2+ years of experience in applied machine learning and software engineering
• Knowledge of statistics for scientific data (e.g. Design of Experiments)
• Experience working within the domain of biological systems
• Experienced with large scale representation learning
• Experienced with long context sequence models
• Experience with tabular data
• Experience with ML + data science libraries such as PyTorch, Pandas, Scikit-learn etc.
• Python Expertise
• Familiarity with cloud platforms like AWS
• Understanding of version control (Git) and software engineering best practices
• Experience with early-stage startups

About us

Our mission

We are on a mission to provide access to more effective medicines for millions of patients. We’re building a model of human molecular physiology for research scientists and clinicians that can answer the fundamental question, "will it work?"

Every modernized field of engineering has a systems simulator to test complex interactions in bits rather than atoms. This doesn’t yet exist for biology. Without one, drug development is expensive because risk of failure is very high; 30-60% of prescribed medicines have no clinical benefit to patients and adverse reaction to treatment is the 4th leading cause of death in the US, ahead of pulmonary disease and diabetes. Syntensor is taking on this grand challenge, developing fundamental machine learning methods and applying them at scale to biological data so every individual patient can be prescribed the most effective, least toxic treatment possible.

What we do

We are productionizing and scaling up a generalizable machine learning platform that predicts efficacy and toxicity for any drug in any indication. We are using an extensive, heterogeneous biomedical graph, novel fundamental ML methods and advanced engineering infrastructure to generate and explain model outputs for users of our app. Currently, our users are research scientists involved in drug development.

The team

We are a small team of people with diverse skills and a shared bias towards problem solving and execution. We are inventors and builders who believe in the scientific method; feedback and iteration is essential to our process and we share our work early and often. That said, we aim high. Our mission and the domain in which we operate demand that we take on some of the hardest problems researchers, scientists, engineers and designers face, and we are determined to build technology that solves them properly and usefully for users of our platform. We are looking for talented people who are motivated by the challenge of hard problems and who are already curious about the technological, scientific or cultural domains with which we engage.

We are a distributed-first team and very relaxed about where and when work happens, but come together as a whole team weekly to sync-up. We work with intrinsic curiosity and motivation towards well defined goals (even where there are unknown unknowns). Our diversity, great communication and respectful, supportive teamwork make us highly effective.