Machine Learning Platform Lead Engineer, Training and Inference

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

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Salary

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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.

Schedule

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

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About this role

Machine Learning Platform Lead Engineer, Training and Inference New York, NY #WeAreParamount on a mission to unleash the power of content… you in? We've got the brands, we've got the stars, we've got the power to achieve our mission to entertain the planet - now all we're missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter - both for our audiences and our employees - and aim to leave a positive mark on culture. Overview We are seeking a Senior Lead / Lead ML Platform Engineer to architect and own the technical direction for our Training and Inference infrastructure. This is a high-leverage role designed for an expert who understands the deep technical stack required to shift ML models from research to global production. You will be responsible for the "engine room" of the AMLG, ensuring that our MLEs can train massive models efficiently and serve them with sub-millisecond reliability. This role requires a unique blend of expertise in distributed systems and hardware acceleration. You will lead the adoption and optimization of AnyScale (Ray) for distributed training and manage a high-performance Kubernetes-based inference environment. You aren't just managing clusters; you are building a seamless, scalable platform that abstracts the complexity of GPUs and distributed compute for the entire organization. Why This Role Matters The ML Platform Lead is the force-multiplier for

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