We're always on the lookout for people who share our passion for improving patient care.


Machine Learning Engineer

Who we are

A biotech startup based in Austin, Klaris was founded to help hospitals combat the problem of antibiotic resistance.  Our team is developing an in vitro diagnostic platform based on our patented phenotypic single-cell technology that can rapidly diagnose serious infections by avoiding traditional time-consuming culture steps, allowing clinicians to predict antibiotic success days faster but at comparable cost to the standard of care.

Brought together by a shared passion for our mission, we are dedicated to establishing a great place to work and an atmosphere that allows you to bring your best to your life and to the business.

Who you are 

We are seeking talented individuals who will embrace our mission and flourish in a dynamic, rigorous, and entrepreneurial environment.

The successful candidate will have an exemplary track record in machine learning with demonstrated experience with both algorithm development and integration. Specific experience with deep learning and data science is considered a plus.

The successful candidate will combine analytical rigor with a thorough understanding of machine learning concepts to innovate at the intersection of data science, artificial intelligence, and clinical microbiology.

The successful candidate will be insightful, highly motivated, capable of working independently, and enjoy working in a collaborative setting.

Most of all, we value individuals who are excellent communicators with relentless initiative, adaptability, and problem-solving skills.

Candidates must have strong proficiency in C, C++, and Python with a solid grasp of computer science fundamentals.

Candidates should have strong proficiency with deep learning frameworks (e.g. TensorFlow, Caffe, PyTorch, Keras).

Candidates should have experience with pattern recognition and machine vision including time series algorithms.

Candidates should have familiarity with applications of Bayesian inference to categorization.

Candidates should have experience integrating with modern relational databases.

Candidates should have a strong publication and/or patent record and a MS with at least three years of industry experience or a PhD in computer science or related discipline.

How you will contribute

Design and implement machine learning algorithms for pathogen identification and anti-microbial susceptibility.

Analyze data and provide key insights to technical team.

Work closely with other scientists and engineers to develop next generation anti-microbial susceptibility in vitro diagnostic products and technologies.

Assume responsibility for timely completion of projects, consistent with the project critical path.

Expand the company’s patent portfolio by creating valuable solutions to multidisciplinary problems.

Effectively adjust to changing priorities.

Manage, capture, and present data: accurately and consistently recording methods, and results.

Analyze data, form conclusions, and aid in determination of future experimental plans.

Recommend expansion or curtailment of activities based on the latest information.

Present findings and comprehensive status reviews at internal meetings.

Support submission of in vitro diagnostic devices to the FDA and other international regulatory agencies.


Location: Austin, TX

Type of Position: Full-Time, Salary

Department: Research & Development

Reports To: Vice President, Research & Development

Web: klarisdx.com

Contact:  If you are interested in joining our team please email a copy of your resume/CV and a cover letter to talent@klarisdx.com. We look forward to hearing from you.


Klaris is a biotech startup based in Austin, Texas, dedicated to establishing a great place to work and an atmosphere that allows you to bring your best to your life and to the business.


"It is not acceptable that much of the technology used to inform the prescription of important medicines like antibiotics has not evolved substantially in more than 140 years"

— Jim O'Neill, The Review on Antimicrobial Resistance

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