Description review
Member of Technical Staff, Model Training (infra & platform), Remote- Full TIme
Xperteez Technology · United States · back to the listing
HR standards
66/100
needs work
Title ↔ description
78/100
solid
Reads as
Machine Learning Engineer
88% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- 27 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Job Title: Member of Technical Staff, Model Training (infra & platform)
Job Type: Full-time
Location: Remote
Required Skills:
• Distributed ML Training Infrastructure
• GPU Performance & Inference Optimization
• Distributed Systems & Platform Architecture
• Technical Leadership & System Ownership
The Role:
In this role, you will take technical ownership of the distributed systems and platforms that enable researchers to train increasingly capable models, execute large-scale experiments, and deploy specialized AI systems efficiently.
You will work across distributed training, GPU infrastructure, inference optimization, simulation environments, and agent execution frameworks, making critical architectural decisions that directly influence research velocity and model performance.
What You'll Do:
• Architect, build, and scale shared infrastructure for supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD), supporting language, multimodal, and robotics workloads.
• Design distributed training and execution pipelines that coordinate GPU workers, rollout generation, teacher-model inference, reward computation, and simulation environments.
• Develop high-performance infrastructure for model training and inference, optimizing GPU utilization, memory management, communication, storage, and distributed execution.
• Build and evolve the core architecture of micro1's agent ecosystem, including execution runtimes, tool interfaces, environment integrations, model registries, and serving APIs.
• Develop robust systems for experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability to improve research reproducibility and operational reliability.
• Identify and eliminate infrastructure bottlenecks, reducing experiment turnaround time and enabling researchers to run increasingly complex training workloads.
• Define technical standards, platform interfaces, and architectural patterns that support collaboration across research, infrastructure, and applied engineering teams.
• Lead critical engineering initiatives from system design through implementation and production operations, mentoring engineers while maintaining direct ownership of technically challenging components.
What We're Looking For:
• Strong computer science fundamentals, particularly in distributed systems, concurrency, networking, and performance engineering.
• Extensive experience designing, building, and operating scalable backend systems or distributed computing platforms.
• Proficiency in Python and at least one systems-oriented programming language, such as C++, Rust, or Go.
• Hands-on experience with GPU-based machine learning infrastructure, distributed training, model inference, or large-scale compute orchestration.
• Strong understanding of system performance, including compute utilization, memory efficiency, communication overhead, scheduling, and fault tolerance.
• Experience designing reliable APIs, services, and infrastructure components that support complex engineering or research workflows.
• Demonstrated technical ownership, including making architectural decisions, leading cross-functional initiatives, and delivering systems that other engineers depend on.
• Ability to operate effectively in fast-moving research environments where requirements evolve and infrastructure must support rapid experimentation.
Nice to Have:
• Experience with distributed ML frameworks and technologies such as PyTorch Distributed, DeepSpeed, Ray, Megatron-LM, or similar systems.
• Familiarity with GPU inference engines and optimization frameworks such as vLLM, TensorRT-LLM, or equivalent technologies.
• Experience building reinforcement learning infrastructure, rollout orchestration systems, or distributed agent execution environments.
• Knowledge of CUDA, GPU kernel optimization, NCCL, or low-level GPU performance engineering.
• Experience with Kubernetes, container orchestration, cluster scheduling, and cloud-based GPU infrastructure.
• Familiarity with simulation platforms, robotics infrastructure, or large-scale agent evaluation systems.
• Contributions to open-source infrastructure, distributed computing frameworks, or high-performance machine learning systems.
Compensation & Benefits Notice:
All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies.
Originally posted on Himalayas
Job Type: Full-time
Location: Remote
Required Skills:
• Distributed ML Training Infrastructure
• GPU Performance & Inference Optimization
• Distributed Systems & Platform Architecture
• Technical Leadership & System Ownership
The Role:
In this role, you will take technical ownership of the distributed systems and platforms that enable researchers to train increasingly capable models, execute large-scale experiments, and deploy specialized AI systems efficiently.
You will work across distributed training, GPU infrastructure, inference optimization, simulation environments, and agent execution frameworks, making critical architectural decisions that directly influence research velocity and model performance.
What You'll Do:
• Architect, build, and scale shared infrastructure for supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD), supporting language, multimodal, and robotics workloads.
• Design distributed training and execution pipelines that coordinate GPU workers, rollout generation, teacher-model inference, reward computation, and simulation environments.
• Develop high-performance infrastructure for model training and inference, optimizing GPU utilization, memory management, communication, storage, and distributed execution.
• Build and evolve the core architecture of micro1's agent ecosystem, including execution runtimes, tool interfaces, environment integrations, model registries, and serving APIs.
• Develop robust systems for experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability to improve research reproducibility and operational reliability.
• Identify and eliminate infrastructure bottlenecks, reducing experiment turnaround time and enabling researchers to run increasingly complex training workloads.
• Define technical standards, platform interfaces, and architectural patterns that support collaboration across research, infrastructure, and applied engineering teams.
• Lead critical engineering initiatives from system design through implementation and production operations, mentoring engineers while maintaining direct ownership of technically challenging components.
What We're Looking For:
• Strong computer science fundamentals, particularly in distributed systems, concurrency, networking, and performance engineering.
• Extensive experience designing, building, and operating scalable backend systems or distributed computing platforms.
• Proficiency in Python and at least one systems-oriented programming language, such as C++, Rust, or Go.
• Hands-on experience with GPU-based machine learning infrastructure, distributed training, model inference, or large-scale compute orchestration.
• Strong understanding of system performance, including compute utilization, memory efficiency, communication overhead, scheduling, and fault tolerance.
• Experience designing reliable APIs, services, and infrastructure components that support complex engineering or research workflows.
• Demonstrated technical ownership, including making architectural decisions, leading cross-functional initiatives, and delivering systems that other engineers depend on.
• Ability to operate effectively in fast-moving research environments where requirements evolve and infrastructure must support rapid experimentation.
Nice to Have:
• Experience with distributed ML frameworks and technologies such as PyTorch Distributed, DeepSpeed, Ray, Megatron-LM, or similar systems.
• Familiarity with GPU inference engines and optimization frameworks such as vLLM, TensorRT-LLM, or equivalent technologies.
• Experience building reinforcement learning infrastructure, rollout orchestration systems, or distributed agent execution environments.
• Knowledge of CUDA, GPU kernel optimization, NCCL, or low-level GPU performance engineering.
• Experience with Kubernetes, container orchestration, cluster scheduling, and cloud-based GPU infrastructure.
• Familiarity with simulation platforms, robotics infrastructure, or large-scale agent evaluation systems.
• Contributions to open-source infrastructure, distributed computing frameworks, or high-performance machine learning systems.
Compensation & Benefits Notice:
All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies.
Originally posted on Himalayas