Senior Machine Learning Platform Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 13, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

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

Role

Role function

Engineering From the posting source

Seniority

Senior From the posting source

Schedule

Shift type

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Weekend work

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Company

Equity

Offered Verified - SEC 10-K source

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Senior Machine Learning Platform Engineer Pune, IN About the Role At PubMatic, data operates at unmatched scale. As a Senior Machine Learning Platform Engineer , you will design and scale the infrastructure and frameworks that enable machine learning development, experimentation, and production across a global ecosystem handling trillions of ad impressions. You will collaborate closely with ML Engineers, Data Scientists, and Product stakeholders to accelerate experimentation, maximize efficiency, and translate AI solutions from concept to production. This role offers direct exposure to petabyte-scale datasets , industry-standard ML efficiency tools (e.g., Triton inference, GPU/accelerated computing), and the opportunity to evaluate and adopt emerging AI/ML technologies. You will contribute to the next-generation ML platform for adtech , enabling advanced use cases such as troubleshooting issues in bid stream, competitive intelligence, benchmarking, forecasting, reinforcement learning, and retrieval-augmented generation (RAG) , while also establishing foundational capabilities like embeddings and observability frameworks . What You'll Do Platform Development: Design and maintain scalable ML pipelines and platforms for ingestion, feature engineering, training, evaluation, inference, and deployment. Big Data & Analytics: Build and optimize large-scale data workflows using distributed systems (Spark, Hadoop, Kafka, Snowflake) to support analytics and model training. Experimentation & Observability: Develop frameworks for experiment tracking, automated reporting, and observability to monitor model health, drift, and anomalies. Work with industry-standard ML efficiency tools to optimize training workloads, accelerate experiments, and monitor performance at scale . AI/ML Enablement: Provide reusable components, SDKs, and APIs that empower teams to leverage AI insights and ML models effectively. Automation:

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