Role - Staff Machine Learning Engineer

Work From Office

Experience - 10+ Years

This role blends deep hands-on technical work with strategic influence, mentorship, and

ownership across multiple work streams, while contributing to broader Enterprise ML initiatives.

What youll do:

Design, build, and deploy advanced machine learning systems for retrieval,

classification, prediction, and generative use cases.

Apply advanced statistical and ML techniques to extract insights from large-scale

structured and unstructured healthcare datasets.

Lead model development across the ML lifecycle, including experimentation, training,

evaluation, deployment, monitoring, and iteration.

Develop and oversee scalable, reusable codebases and ML infrastructure to support

production use cases.

Collaborate cross-functionally with product managers, clinicians, data engineers, BI

engineers, and design teams to translate business and clinical needs into robust ML

solutions.

Drive experimentation by defining problem statements, forming falsifiable hypotheses,

and designing rigorous evaluation frameworks tied to business outcomes.

Review, communicate, and present ML insights and results to technical and

non-technical stakeholders, including executive leadership.

Serve as a technical mentor and advisor to junior engineers, providing guidance on ML

best practices, experimentation, and system design.

Contribute as an expert advisor across multiple initiatives, helping shape ML strategy

and performance tracking across the organization.

What youll need:

Must-haves

Masters degree (PhD preferred) in Computer Science, Data Science, Machine Learning,

or a closely related quantitative field.

7+ years of professional experience in applied machine learning or data science,

including ownership of production ML systems.

Deep expertise in Python and modern deep learning frameworks (e.g., PyTorch).

Hands-on experience building and deploying deep learning models (e.g., transformers)

for NLP tasks.

Strong understanding of experimental design, model evaluation, and optimization for

real-world production environments.

Experience leveraging cloud platforms (AWS preferred) across the ML lifecycle (training,

deployment, monitoring).

Proven ability to collaborate with product, business, and clinical partners to drive

data-informed decision-making.

Excellent written and verbal communication skills, with experience presenting to both

technical and non-technical audiences.

Staff Machine Learning Engineer