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Salary
$200k – $400k per year
Location
In office (San Francisco)
Seniority
Junior · 2+ years exp
Visa
No sponsorship (stated in the posting)
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Oct 21, 2026.

Overview
Company
Impact
Profile match
Headquartered in San Francisco, California, True Talent Solutions Partners is a specialized recruitment and talent acquisition consultancy partnering primarily with high-growth technology startups and venture-backed firms. The company focuses on sourcing and placing top-tier talent across core technical and business domains, including artificial intelligence, software engineering, product development, and executive leadership. Through tailored search strategies and direct collaboration with company founders, it helps emerging organizations rapidly scale their teams and build sustainable hiring pipelines.

About the Role

Join an AI team developing foundation models for physical systems, with weather as an initial application. You will build the distributed infrastructure that supports large-scale model training and inference, helping advance AI for science and physical AI.

What You'll Do

  • Design, deploy, and maintain distributed machine learning training and inference clusters.

  • Build scalable end-to-end pipelines for petabyte-scale datasets and the full model lifecycle.

  • Research and evaluate training approaches, including parallelization strategies and numerical precision trade-offs across model scales.

  • Profile and debug low-level GPU operations to improve performance.

  • Analyze experiment results, including through ablation studies, and apply relevant research to infrastructure work.

What We're Looking For

  • Two to ten years of experience building large-scale machine learning infrastructure for pre-trained or foundation models.

  • Hands-on experience across compute clusters, data infrastructure, distributed training, and inference, with a record of optimizing large-scale workloads.

  • Proficiency with distributed training frameworks such as FSDP or DeepSpeed, and experience with GPU performance optimization and debugging, including CUDA and JAX.

  • Knowledge of cloud platforms such as GCP, AWS, or Azure, plus containers and orchestration tools such as Docker and Kubernetes.

  • Familiarity with model serving, distributed task management, and ML system monitoring, logging, observability, and version control.

  • Experience in AI for science or physical AI, such as robotics, autonomous systems, or biology, and a generalist approach across the ML lifecycle.

  • Comfort working in fast-paced, time-sensitive environments and learning unfamiliar domains.

Compensation & Benefits

Base salary: $200,000 to $400,000 annually. Visa sponsorship is not available.

Location

On-site in San Francisco, California, United States, five days per week. Relocation support is provided.

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