Applied ML Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Childcare support

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

Mid From the posting source

Work mode

Hybrid From the posting source

In-office days

2 days From the posting source

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Applied ML Engineer San Francisco | Hybrid | Remote About Knowtex Knowtex is building the future of voice AI operating systems for clinicians, transforming how healthcare documentation happens at the point of care. Founded by Stanford AI scientists with deep clinical experience, we're experiencing explosive growth across both commercial health systems and federal healthcare, with our ambient documentation platform scaling rapidly to thousands of clinicians across hundreds of specialties. We're at an inflection point where cutting-edge AI meets real clinical impact, giving clinicians hours back each day to focus on what matters most - their patients. Position Overview We are seeking an Applied ML Engineer to productionize and scale machine learning systems powering our voice AI platform. This role bridges research and engineering - transforming models into reliable, low-latency, production-grade systems deployed across enterprise healthcare environments. You will work closely with ML Scientists, Backend Engineers, and Platform teams to optimize inference performance, build evaluation pipelines, and ensure robust model deployment in regulated environments. Key Responsibilities - Productionize ML models for real-time clinical applications - Optimize inference pipelines for low latency and high throughput - Deploy and scale models using AWS-based infrastructure - Build automated evaluation and regression testing frameworks for LLM outputs - Implement monitoring systems for model performance and drift detection - Collaborate with Backend teams to integrate ML services into APIs and workflows - Improve model efficiency through quantization, batching, caching, and optimization techniques Support specialty-level model evaluation and performance analysis - Contribute to CI/CD workflows for ML

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