Mission:

Flagship Labs 63 Inc. (FL63) is a privately held, well-funded, early-stage platform biotechnology company that is leveraging emerging insights in RNA biology to develop a novel class of human therapeutics.

FL63 was founded by Flagship Pioneering, an innovation enterprise that conceives, resources and grows first-in-category life science companies. The firm’s institutional innovation foundry, Flagship Labs, is where teams of scientific entrepreneurs systematically evolve ideas and turn previously undiscovered areas of science into real-world ventures. Flagship has created over 100 scientific ventures resulting in >$19 billion in aggregate value, 500+ issued patents, and >50 clinical trials for novel therapeutic agents. Flagship’s portfolio companies include Moderna, Rubius Therapeutics, and Indigo Agriculture.

FL63 is a highly dynamic, entrepreneurial and innovation-driven organization seeking to hire an exceptional Engineer to join our growing team. A successful candidate will have a B.S. or M.S. in computer science, computational biology, or a related field, and 2+ years of experience applying machine learning to biological problems. The candidate’s career goals, technical skills, and core competencies should be aligned with the description below. 

Key Outcomes:

1. Leverage your machine learning expertise to design and evaluate ML models for use in biological sequence design.
2. Implement and scale sequence exploration algorithms to accelerate our platform.
3. Integrate diverse high-throughput data sources and multiple optimization criteria in model design and evaluation.
4. Visualize and communicate your results to cross-functional teams, and advocate for experimental designs and data streams that augment downstream ML applications.
5. Produce well-documented code and APIs to facilitate collaboration and use by other computational biologists at FL63.
6. Continually cultivate scientific and technical expertise through critical review of the scientific literature and attendance at scientific meetings.

Required Skills:

1. Strong computational skills, with fluency in Python and a demonstrated ability to produce high-quality, well-documented code.
2. Demonstrated ability to implement, develop, and scale ML approaches to analyze biological datasets.
3. Experience working in at least one ML development environment (i.e., Tensorflow, Pytorch, Caffe, etc).
4. Excellent communication and presentation skills, with an ability to discuss complex methods and results in a clear and accessible manner for all audiences.
5. Comfortable working in a cloud-computing environment (AWS).
6. Experience with computational notebooks (i.e., Jupyter), version control (i.e., Git), unit tests, and coding standards.

Preferred Skills:

1. Experience using ML models for biological sequence design and selection.
2. Experience with data visualization libraries and tools to construct interactive visualizations
3. Experience working with large datasets and GPU-accelerated computing.

Core Competencies:

1. Fast-acting/efficient. Moves quickly and proactively with a strong work ethic to produce high-quality results while fostering a positive work environment. Focuses on key priorities. Demonstrates tenacity and willingness to go the distance to get something done.
2. Integrity. Does not cut corners ethically. Earns trust and maintains confidences. Does what is right not just what is politically expedient. Speaks plainly and truthfully. Follows-through on commitments. Expects a high level of personal performance and team performance.
3. Critical thinking. Learns quickly. Demonstrates ability to proficiently understand new information and independently achieve meaningful outcomes. Able to structure and process qualitative/quantitative data and draw insightful conclusions.
4. Creativity & Innovation. Generates new and creative approaches to problem solving. Positive ‘can-do’ attitude. Views the toughest challenges as the greatest opportunities for personal growth and company innovation.
5. Teamwork. Fully engaged in facilitating personal and team success. Reaches out to peers and cooperates with the team to establish an overall collaborative work environment. Often solicits and responds well to constructive feedback. Possesses good written and oral communication skills with the ability to clearly and concisely convey ideas and opinions.
6. Flexibility/adaptability. Adjusts quickly to changing strategic and tactical priorities. Comfortable with ambiguity.