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Machine Learning Science Manager - job 1 of 2

Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients.

Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems.

We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense.

The Role

From transcribing medical conversations to delivering key takeaways, our trailblazing work in machine learning research makes the Abridge experience possible. We're currently looking to hire a Machine Learning Science Manager to lead and manage a team of applied scientists with experience in machine learning and natural language processing and a passion for developing technology to solve both clinical and administrative problems in the medical domain. The ideal candidate will bring technical mastery, fluency with statistics and deep learning (including foundation models), a genuine interest in the medical domain, and strong critical thinking skills to the role. At Abridge, all of our ML work has a strong research component, and all of our research scientists contribute directly to real products that impact the lives of doctors.

What You'll Do

  • Lead and Manage Research Team: Oversee a team of research scientists, providing guidance, mentorship, and support to foster their growth and ensure the success of research projects.

  • Advance Medical NLP: Drive the advancement of the state of the art in medical NLP, focusing on areas such as conversation summarization, evidence extraction, outcome prediction, evaluation techniques, and experimentation.

  • Contribute Research: Actively contribute to the wider research community by sharing and publishing original research, and encourage team members to do the same.

  • Define and Develop Solutions: Collaborate with the team to define important problems, identify appropriate baselines, develop state-of-the-art methods, and integrate them into production.

  • Incorporate Feedback: Engage with clinicians to gather real-time feedback, guiding further refinements and innovations in our products.

  • Results-Oriented Approach: Maintain a focus on results in the face of ambiguous problems and uncertain outcomes, ensuring that research initiatives have a tangible impact.

What You'll Bring

  • Demonstrated through papers and (most likely but not necessarily) advanced graduate degrees in, Computer Science, Electrical Engineering, Mathematics, or equivalent experience

  • Demonstrated ability to lead and manage a team, providing both technical and professional guidance

  • Significant contributions to open source and deployed technology, showcasing the real-world impact of your work

  • Strong programming skills with proven experience crafting, prototyping, and delivering machine learning solutions into production.

  • Proven experience shipping inspiring LLM-based experiences

  • Proven track record of high-impact publications at peer-reviewed AI conferences (e.g. *CL, NeurIPS, ICML, ICLR)

  • Experience with deep learning libraries (e.g. PyTorch, Jax, Tensorflow) and platforms, multi-GPU training, and statistical analyses of observational and experimental data.

Base Salary: $250,000 USD - $300,000+ USD per year + Equity

The salary range provided is based on transparent pay guidelines and is an estimate for candidates residing in the San Francisco and New York City metro areas. The actual base salary will vary depending on the candidate's location, relevant experience, skills, qualifications, and other job-related factors. Additionally, this role may include the opportunity to participate in a company stock option plan as part of the total compensation package.

Must be willing to work from our SF office at least 3x per week

This position requires a commitment to a hybrid work model, with the expectation of coming into the office a minimum of (3) three times per week. Relocation assistance is available for candidates willing to move to San Francisco.

Must be willing to travel up to 10%

Abridge typically hosts a three-day builder team retreat every 3-6 months. These retreats often feature internal hackathons, collaborative project sessions, and social events that allow the team to connect in person.

We value people who want to learn new things, and we know that great team members might not perfectly match a job description. If you’re interested in the role but aren’t sure whether or not you’re a good fit, we’d still like to hear from you.

Why Work at Abridge?

  • Be a part of a trailblazing, mission-driven organization that is powering deeper understanding in healthcare through AI!

  • Opportunity to work and grow with talented individuals and have ownership and impact at a high-growth startup.

  • Flexible/Unlimited PTO — Salaried team members can take off as much approved time off as they need, plus 13 paid holidays

  • Equity — For all salaried team members

  • Medical insurance — We pay 100% of the premium for you + 75% for dependents. 3 Aetna plans to choose from.

  • Dental & Vision insurance — We pay 100% of the premium for you + 75% for dependents. 2 Aetna plans to choose from.

  • Flexible Spending (FSA) & Health Savings (HSA) Accounts

  • Learning and Development budget — $3,000 per year for coaching, courses, workshops, conferences, etc.

  • 401k Plan — Contribute pre-tax dollars toward retirement savings.

  • Paid Parental Leave — 16 weeks paid parental leave, for all full-time employees

  • Flexible working hours — We care more about what you accomplish than what specific hours you’re working.

  • Home Office Budget — We provide up to $1,600 in a one-time reimbursement to set up your home office.

  • Sabbatical Leave — 30 days of paid Sabbatical Leave after 5 years of employment.

  • ...Plus much more!

Life at Abridge

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 experiment in alchemy that helps us transform an industry dominated by EHRs and enterprise into a consumer-driven experience, one recording at a time. We believe in strong ideas, loosely held, and place a high premium on a growth mindset. We push each other to grow and expose each other to the latest in our respective fields. Whether it’s holding a PhD-level deep dive into understanding fairness and underlying bias in machine learning models, debating the merits of a Scandinavian design philosophy in our UI/UX, or writing responses for Medicare rules to influence U.S. health policy, we prioritize sharing our findings across the team and helping each other be successful.

Diversity & Inclusion

Abridge is an equal opportunity employer. Diversity and inclusion is at the core of what we do. We actively welcome applicants from all backgrounds (including but not limited to race, gender, educational background, and sexual orientation).

Staying Safe - Protect Yourself From Recruitment Fraud

We are aware of individuals and entities fraudulently representing themselves as Abridge recruiters and/or hiring managers. Abridge will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Abridge recruiting team will come from an @abridge.com email address. You can learn more about how to protect yourself from these types of fraud by referring to this article. Please exercise caution and cease communications if something feels suspicious about your interactions. 

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CEO of Abridge
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Shivdev Rao
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What You Should Know About Machine Learning Science Manager, Abridge

Are you ready to lead the charge in transforming healthcare through AI? Abridge, a pioneer in generative AI for healthcare, is on the lookout for a Machine Learning Science Manager to join our dynamic team in San Francisco. Our mission since 2018 has been to harness the power of AI for better understanding in the medical field, and with our advanced platform, we ensure that clinical conversations are documented efficiently—allowing clinicians to focus on what truly matters: their patients. In this role, you will manage a talented group of applied scientists and drive advancements in medical natural language processing, tackling everything from conversation summarization to evidence extraction. With a strong research component, your work will directly touch the lives of healthcare providers, meaning every breakthrough has a real-world impact. We’re looking for someone with a solid technical background, deep understanding of deep learning, and a curiosity about healthcare that inspires innovation. You’ll also contribute to the scientific community through published research, engage with clinicians to refine our products, and maintain a results-oriented approach amid uncertainty. If you have a track record of impactful research, proficiency in modern ML technologies, and a passion for using AI to improve healthcare, we’d love for you to join us at Abridge. Together, we can set new standards in medical AI and make a difference in people's lives!

Frequently Asked Questions (FAQs) for Machine Learning Science Manager Role at Abridge
What are the main responsibilities of the Machine Learning Science Manager at Abridge?

As a Machine Learning Science Manager at Abridge, you will lead and manage a team of applied scientists, driving advancements in medical natural language processing. Responsibilities include overseeing research projects, promoting team growth, contributing to original research published in leading journals, integrating state-of-the-art methods into production, and engaging with clinicians to gather feedback on innovations.

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What qualifications are required to apply for the Machine Learning Science Manager position at Abridge?

To qualify for the Machine Learning Science Manager role at Abridge, candidates typically need advanced degrees in Computer Science, Electrical Engineering, or a related field, alongside demonstrated leadership experience in managing research teams. A proven track record of high-impact publications at recognized AI conferences, along with strong programming skills and machine learning solutions delivery, is also essential.

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How does Abridge approach diversity and inclusion for their Machine Learning Science Manager role?

At Abridge, diversity and inclusion are core values. We actively encourage applicants from all backgrounds for the Machine Learning Science Manager role. We believe that diverse perspectives enhance our innovation in AI solutions within the healthcare space, ensuring we cater to the needs of a broad user base.

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What kinds of projects might a Machine Learning Science Manager work on at Abridge?

A Machine Learning Science Manager at Abridge would engage in projects focused on advancing the state of the art in medical natural language processing, such as developing methods for conversation summarization, evaluating techniques for evidence extraction, and predicting outcomes from clinical conversations, alongside contributing to impactful open-source technologies.

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What are the career development opportunities for Machine Learning Science Managers at Abridge?

Abridge offers numerous career development opportunities for Machine Learning Science Managers, including a generous learning and development budget, participation in research conferences, and the chance to contribute to meaningful projects that drive technological advancements in healthcare, allowing for both personal and professional growth.

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Common Interview Questions for Machine Learning Science Manager
How would you approach leading a team of applied scientists?

When leading a team of applied scientists, I would prioritize clear communication, set distinct goals, and encourage a collaborative environment. Fostering mentorship and providing continuous feedback can help cultivate talent while ensuring that everyone understands their individual contributions towards larger project objectives.

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Can you explain a recent project you've led in the area of natural language processing?

In my recent project, I led a team in developing a sophisticated model for conversation summarization. We focused on improving accuracy metrics while integrating clinician feedback. This involved extensive prototyping, rigorous testing, and collaboration with healthcare professionals, resulting in a tool that significantly enhanced clinical documentation processes.

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What is your experience with deep learning frameworks and how have they impacted your projects?

I have hands-on experience with various deep learning frameworks such as TensorFlow and PyTorch. Utilizing these frameworks has enabled me to develop more robust machine learning algorithms, particularly in scalability and efficiency, allowing for rapid iterations in model performance and fine-tuning based on real-world application needs.

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Describe how you ensure your research contributes to impactful real-world applications?

To ensure my research contributes impactfully, I involve stakeholders from the healthcare industry early in the process to understand their needs. Additionally, I focus on translating research findings into practical solutions and continually seek feedback from end-users, which allows me to refine and adapt technologies to maximize their utility in real-world settings.

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What strategies do you use to keep your team motivated during ambiguous projects?

I maintain motivation during ambiguous projects by promoting an atmosphere of open communication and continuous learning. I encourage my team to share challenges and brainstorm solutions collaboratively, enabling adaptability. Celebrating small wins throughout the project journey also helps to keep morale high and the team focused on the larger goal.

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How do you handle disagreements within your research team?

I address disagreements within my team by facilitating open discussions to explore differing viewpoints collaboratively. It’s essential to encourage respectful debate and ensure that everyone feels heard. We can often arrive at innovative solutions by synthesizing various perspectives, thus leading to stronger outcomes.

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In your view, what are the emerging trends in medical NLP?

Emerging trends in medical NLP include increased use of foundation models and the integration of attention mechanisms, enabling more nuanced understanding of clinical language. Additionally, incorporating real-time user feedback into models is becoming essential for ensuring that AI applications remain relevant and user-centric in healthcare environments.

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Why is clinician feedback important for developing AI in healthcare?

Clinician feedback is vital for developing AI in healthcare as it ensures that our products practically address real-world challenges faced by healthcare professionals. Their insights guide model refinement, alignment with clinical workflows, and ultimately, the trustworthiness of AI outputs, which are crucial for effective application in patient care.

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What methods do you use for evaluating the effectiveness of your research?

To evaluate the effectiveness of my research, I utilize clear performance metrics established at the project onset, conduct rigorous A/B testing, and gather qualitative feedback from users. This multidimensional approach allows for a comprehensive understanding of the research impact and informs future iterations.

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How do you envision the future of AI in healthcare?

I envision the future of AI in healthcare as one where AI systems seamlessly integrate into clinical workflows, significantly enhancing decision-making and patient outcomes. A continuous focus on ethics and user-centric designs will ensure these technologies uphold trust and provide tangible benefits to clinicians and patients alike.

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To encourage understanding and follow-through across every medical conversation.

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Full-time, hybrid
DATE POSTED
December 15, 2024

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