Role and Responsibilities: Development of our core algorithm is guided by performance benchmarking on real and simulated data (to forecast future performance and what-if scenarios). To support this process, the role may include: Development of a reproducible performance benchmarking framework to track current and predicted quantitative accuracy, sensitivity, and precision. Expansion of data simulation framework to model more complex biophysical phenomena where appropriate, as guided by fresh experimental data, literature review, and discussions with enthusiastic colleagues. As our technology platform develops, we expect to make fundamental changes to our core algorithmic approach. These changes will be implemented in our production C++ code-base to improve: Accuracy through more sophisticated modeling of biophysical phenomena and machine learning approaches where appropriate (as guided by data). Robustness through novel algorithmic approaches to process imperfect data. Transparency through introspection tools and visualizations. Speed through vertical and horizontal scalability approaches. As we are an early-stage company espousing the “all hands-on deck ethos, our needs are diverse and fluid. Expect ample opportunity to apply your strengths to help others including extensive collaboration with experimentalists to help assess statistical confidence in their data, design future experiments, or automate repetitive tasks. Qualifications and Education Requirements: Ph.D. or equivalent experience in computational biology, bioinformatics, or a related field. Alternatively, 4 years of experience in a software engineering role with applications in biology or related field. Experience and comfort writing software in Python and C or C++. Preferred Skills: Model evaluation and inference approaches (e.g. least squares regression, maximum likelihood estimation, expectation maximization, Gibbs sampling, Metropolis-Hastings sampling) Unsupervised and supervised classification approaches Algorithmic approaches in bioinformatics Statistics and probability, combinatorics Applied mathematics Familiarity with any of molecular biology, biophysics, analytical chemistry, systems biology Strong data visualization and communication skills Performance analysis and optimization of parallelized algorithms Implementation of distributed computational systems
