Come join our team, and together we’ll realize the true potential of gene therapy!
Who we are
Dyno Therapeutics is a Cambridge based, VC-backed biotech startup that uses next-gen DNA technologies and machine learning to engineer Adeno-associated Virus (AAV) capsids for the effective delivery of gene therapies.
What we offer you
As a member of our quickly growing company, you’ll help us shape Dyno into a startup that takes its scientific mission seriously and provides a positive and supportive workplace environment. Dyno will have the opportunity to benefit from your insight, skills, and talent while enriching your professional and scientific experience as we grow the company together.
At Dyno, we are expanding the boundaries of gene therapy. AAV capsids are currently the vector of choice for gene therapy, but they are only a starting point in the gene therapy revolution. Dyno aims to dramatically extend the reach of gene therapy by overcoming the limitations of existing AAV capsids, allowing more therapies to reach the clinic. Doing so will enable treatment for millions of patients with currently incurable, often disabling and deadly diseases.
Dyno’s groundbreaking engineering pipeline harnesses advances in DNA library synthesis, high-throughput sequencing, and machine learning to generate transformative gene therapy vectors. We target the major barriers that separate AAV gene therapy research from real-world therapies, including delivery efficiency, tissue and cell-type specificity, immune evasion, and more. Our vectors will accelerate the transition of gene therapies from the lab to the clinic for the benefit of patients worldwide.
Dyno is located near Kendall Square in Cambridge. Situated within the dynamic LabCentral community, Dyno is working alongside other startups that are also creating the future of biomedicine.
The data science team is at the heart of Dyno’s platform, and your work as a part of this team will have a potentially major impact on the future of gene therapy. The team's responsibilities span biological data analysis, machine learning model building, computational optimization, and experimental design of high-throughput viral libraries. Opportunities to apply rigorous theoretical approaches from combinatorial optimization, bayesian optimization, bandit strategies, reinforcement learning, natural language processing, and compressive sensing in our platform abound. The successful candidate will have expertise in one of these (or related) topics and would like to apply them to a new domain. The field of capsid engineering is ripe with problems that are interesting from a learning perspective, and Dyno is well-placed to generate data at a scale at which these approaches can be fruitful.
As an early member of our team, you will have the opportunity to help craft our approach, shape our culture, and positively impact people’s lives. You will work closely with a group of talented, driven, and fun scientists. We are located in Lab Central in a dynamic community of biotech startups. The guideline below should give you a rough picture of who we are looking for, but depending on your background, you may bring different qualities to our team. Don't hesitate to write to us if you think you are a good fit. We offer competitive benefits.
- BS, MS, or Ph.D. in a quantitative field (Machine learning, Math, Physics, CS, Operations Research, Statistics, ...) or equivalent experience.
- Strong theoretical foundation in machine learning or a related domain.
- Expertise in one or more general-purpose programming languages (such as Python or C/C++.)
- Passion for creative problem solving.
- Proven experience with at least one machine learning library (such as Tensorflow or pyTorch).
- Papers in machine learning or EECS conferences (NIPS, ICLR, ICML, INFORMS, FOCS,...)
- AI Internships or residencies at leading AI companies.
- 1 or more years experience working in a data science team in a machine-learning driven company.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Job Type: Full-time, Internship