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Machine Learning, Platform Engineer

Together AI - San Francisco

Posted Aug 22, 2025

Benefits

Parental leave
Not verified
Non-birth-parent leave
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Family-building benefits
  • Fertility benefits: Not verified
  • 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
$160K-$250K From the posting source checked Jun 20, 2026
401(k) match
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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

76% above the BLS role benchmark for software engineering aggregate.

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 checked Jun 20, 2026
Seniority
Mid From the posting source checked Jun 20, 2026

Schedule

Shift type
Not verified
Weekend work
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Company

Company stage
Growth-stage From the posting source checked Jun 20, 2026
Equity
Offered From the posting source checked Jun 20, 2026

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

Machine Learning, Platform Engineer San Francisco About the Role Our team focuses on enabling custom models and dedicated inference on Together. We are responsible for building a container platform, optimizing autoscaling, minimizing cold starts, achieving the best end-to-end model performance, and providing a best-in-class developer experience with great tooling. We often focus on video or audio generation across the stack: CUDA kernels, pytorch optimization, inference engines, container orchestration, queueing theory, etc. An ideal candidate will be great at profiling/optimization but know the word kubernetes, or be intimately familiar with multi-cluster scheduling and have some sense of ML bottlenecks. Responsibilities - New hires may work on multi-cluster orchestration, portfolio optimization, predictive autoscaling, control panes, model bring-up, model optimization, APIs for managing deployments, inference worker SDKs, and CLI tools. - Analyze and improve the robustness and scalability of existing distributed systems, APIs, databases, and infrastructure - Partner with product teams to understand functional requirements and deliver solutions that meet business needs - Write clear, well-tested, and maintainable software and IaC for both new and existing systems - Conduct design and code reviews, create developer documentation, and develop testing strategies for robustness and fault tolerance Requirements - 5+ years of demonstrated experience in building large scale, fault tolerant, distributed systems. - Experience running serverless inference platforms, doing model bring-up on short notice, being on call, or running a cloud provider is a very big plus - Good taste and ability to thoughtfully discuss how what you've built has failed over time - Experience

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