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Machine Learning Engineer (All Ranks)
Abridge AIUKRemotefull_timeVerifiedPosted 9 Mar 2023
About the role
Health care is all about conversations, with over 2B spoken conversations each year between patients and their care teams in just the United States. However, people forget up to 80% of those conversations, leading to worse patient outcomes. And doctors are burning out writing notes in their EMRs instead of focusing on their patients. That’s where Abridge comes in — our audio-based standalone and integrated solutions record and summarize medical conversations, anywhere care happens.
Our technology is powered by machine learning models that transcribe medical conversations, extract structured data, and compose abstractive summaries in the archetypal styles that doctors know and expect. Getting this machine learning research into production requires solving complex engineering problems at the intersection of software, science, and business processes. We are looking to hire an engineer ready to tackle the massive problems that arise when stitching together machine learning components to form seamless experiences. This requires thoughtful tooling to enable ML teams to rapidly hot-swap components and run end-to-end tests, mature processes around staging and deployment of machine learning pipelines, and tools to support the rapid prototyping and evaluation of improvements to our ML systems. We are looking for an ML engineer ready to work with our current ML ops specialist to take on these challenges. This position requires a mix of engineering skill, ML literacy, the ability to rapidly pick up new tools, and mature thinking around building sustainable pipelines and processes.
At Abridge, we’re driven by our mission to bring understanding and follow-through to every medical conversation. Our culture is founded on doing things the “inverse” way in a legacy system—focusing on patients, instead of the system; focusing on outcomes, instead of billing; and focusing on the end-user experience, instead of a hospital administrator's mandate. Abridgers are engineers, scientists, designers, and health policy experts from a diverse set of backgrounds—an
Our technology is powered by machine learning models that transcribe medical conversations, extract structured data, and compose abstractive summaries in the archetypal styles that doctors know and expect. Getting this machine learning research into production requires solving complex engineering problems at the intersection of software, science, and business processes. We are looking to hire an engineer ready to tackle the massive problems that arise when stitching together machine learning components to form seamless experiences. This requires thoughtful tooling to enable ML teams to rapidly hot-swap components and run end-to-end tests, mature processes around staging and deployment of machine learning pipelines, and tools to support the rapid prototyping and evaluation of improvements to our ML systems. We are looking for an ML engineer ready to work with our current ML ops specialist to take on these challenges. This position requires a mix of engineering skill, ML literacy, the ability to rapidly pick up new tools, and mature thinking around building sustainable pipelines and processes.
What You'll Do:
- Architect, design, and implement machine learning software applications, infrastructure, and evaluation tools.
- Collaborate with machine learning scientists and engineers to deploy models and pipelines.
- Work with stakeholders across machine learning, engineering, and operations teams to build infrastructure to support the movement of models and feedback across the organization.
- Create re-usable software and systems to accelerate development.
- Profile, tune, and optimize system performance and debug production issues.
- Maintain high standards by participating in reviews, designing for fault tolerance, and creating continuous improvement mechanisms.
- Help us to scale our services and compute infrastructure to efficiently serve a growing client base.
Who You Are:
- 3+ years of industry software development experience, with a background in design patterns, data structures, and test-driven development.
- Proficient in developing production-quality software in languages such as C++, Python, or Java.
- Proficient with professional software engineering practices & standard practices for the full software life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
- Experience with cloud based environments
- Experience with Kubernetes or Docker
- Excellent interpersonal and written communication skills.
Ideally, You Have:
- Experience in one or more relevant technical areas: natural language processing, machine learning, distributed systems, or building infrastructure for engineering/science users.
- Expertise with machine learning tools, such as Pytorch, Jax, or Tensorflow.
- Demonstrated experience incubating and productionizing new technology, working closely with research scientists and technical teams from idea generation through implementation.
Why work at Abridge
- Be a part of a trailblazing, mission driven organization that uses audio as the wedge to improve the healthcare delivery experience
- Unlimited PTO, plus 12 national holidays
- Comprehensive and generous benefits package: 100% coverage of employee medical, dental and vision75% coverage for dependent medical, dental and vision401k program
- 16 weeks paid parental leave, for all employees
- Flexible working hours — we care more about what you accomplish than what specific hours you’re working
- Remote work environment
- Equity for all new employees
- Generous equipment budget for your home office setup ($1600)
- Opportunity to work and grow with talented individuals, and have ownership and impact at a high growth startup.
- Plus much more!
At Abridge, we’re driven by our mission to bring understanding and follow-through to every medical conversation. Our culture is founded on doing things the “inverse” way in a legacy system—focusing on patients, instead of the system; focusing on outcomes, instead of billing; and focusing on the end-user experience, instead of a hospital administrator's mandate. Abridgers are engineers, scientists, designers, and health policy experts from a diverse set of backgrounds—an
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