ML Infra Engineer (TPU/Jax/Optimization)

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Not verified

Salary

Not verified

401(k) match

Not verified

Was this benefit information wrong? Tell us.

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 checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

Not verified

Company

Company stage

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

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

ML Infra Engineer (TPU/Jax/Optimization) San Francisco, California, United States In this role you will help scale and optimize our training systems and core model code. You'll own critical infrastructure for large-scale training, from managing GPU/TPU compute and job orchestration to building reusable and efficient JAX training pipelines. You'll work closely with researchers and model engineers to translate ideas into experiments-and those experiments into production training runs. This is a hands-on, high-leverage role at the intersection of ML, software engineering, and scalable infrastructure. The Team The ML Infrastructure team supports and accelerates PI's core modeling efforts by building the systems that make large-scale training reliable, reproducible, and fast. The team works closely with research, data, and platform engineers to ensure models can scale from prototype to production-grade training runs. In This Role You Will - Own training/inference infrastructure: Design, implement, and maintain systems for large-scale model training, including scheduling, job management, checkpointing, and metrics/logging. - Scale distributed training: Work with researchers to scale JAX-based training across TPU and GPU clusters with minimal friction. - Optimize performance: Profile and improve memory usage, device utilization, throughput, and distributed synchronization. - Enable rapid iteration: Build abstractions for launching, monitoring, debugging, and reproducing experiments. - Manage compute resources: Ensure efficient allocation and utilization of cloud-based GPU/TPU compute while controlling cost. - Partner with researchers: Translate research needs into infra capabilities and guide best practices for training at scale. - Contribute to core training code: Evolve JAX model and training code to support new architectures, modalities,

Read the full description at jobs.ashbyhq.com. FewerJobs shows a preview and links to the original posting.