---
title: "Member of ML Technical Staff"
company: "Pragmatike"
company_url: "https://www.remjobs.works/companies/pragmatike"
url: "https://www.remjobs.works/job/pragmatike-member-of-ml-technical-staff-5d634954-7a88-4eda-ae5f-0dbe12f6837a"
apply_url: "https://jobs.ashbyhq.com/pragmatike/ad59529e-b796-4f8b-827d-ecb72614d075"
workplace: onsite
location: "San Francisco"
employment_type: full-time
seniority: staff
role: people
region: united-states
skills: ["llm", "python", "pytorch"]
salary: "$200,000–$350,000 per year"
date_posted: 2026-08-19T16:36:52.659Z
first_seen_by_remjobs: 2026-09-15T19:10:33.408Z
---

# Member of ML Technical Staff

**Pragmatike** · San Francisco

Salary: $200,000–$350,000 per year

Apply: https://jobs.ashbyhq.com/pragmatike/ad59529e-b796-4f8b-827d-ecb72614d075

## About Pragmatike

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## About the role

### Member of Technical Staff — LLM Research & Training

#### About the Role

We are looking for an exceptional **Member of Technical Staff specializing in Machine Learning and Large Language Models** to join an early-stage AI company building and training state-of-the-art foundation models.

This role sits at the intersection of **LLM research, large-scale training infrastructure, post-training, and GPU/kernel optimization**.

We are particularly interested in highly motivated researchers and engineers who want to contribute directly to training powerful models — whether their strengths are in theoretical model research, training systems, distributed infrastructure, or low-level performance optimization.

You will work in a small, highly technical team where researchers and engineers collaborate closely and are expected to take ownership across the stack.

#### Responsibilities

- Research, design, and implement new techniques for training and improving large language models.

- Build and optimize large-scale pre-training and post-training pipelines.

- Improve model training efficiency, throughput, stability, and scalability.

- Work on distributed training across large GPU clusters.

- Design and optimize model-parallel training strategies, including tensor, pipeline, sequence, and data parallelism.

- Optimize GPU workloads using technologies such as CUDA and Triton.

- Improve inference and training kernels when necessary.

- Explore new model architectures, training methodologies, and post-training techniques.

- Run experiments, analyze results, and rapidly iterate on research ideas.

- Collaborate on software/hardware co-design to maximize training throughput.

- Contribute to internal research infrastructure and potentially open-source initiatives.

#### What We're Looking For

##### LLM / ML Research Experience

- At least 1+ years of experience in theoretical LLM research or as an ML researcher/engineer at a highly technical AI or technology organization.

- Hands-on experience working with large language models beyond simply consuming existing APIs.

- Experience with one or more of:LLM architecture research

- Pre-training

- Post-training

- Reinforcement learning / preference optimization

- Training framework development

- Kernel or inference optimization

- Large-scale distributed trainingExperience working on language models at organizations or research environments comparable to **OpenAI, Google DeepMind, Mistral AI, Qwen, DeepSeek, **[Z.ai](http://Z.ai)**, Allen Institute for AI, or leading academic labs** is highly relevant.

##### Large-Scale Training

Strong understanding of large-scale AI infrastructure and at least some of the following:

- Distributed GPU training

- Model parallelism

- Tensor parallelism

- Pipeline parallelism

- Sequence parallelism

- Data parallelism

- Communication optimization

- Memory optimization

- Training throughput optimization

- Software/hardware co-designExperience contributing to initiatives such as **NanoGPT Speedrun, Marin**, or similar open-source model-training projects is a strong plus.

#### Technical Skills

Strong proficiency with:

- Python

- PyTorch

- CUDA

- TritonExperience with **JAX** is highly valued.

Additional experience with distributed training frameworks, custom kernels, GPU profiling, compiler optimization, or high-performance computing is a plus.

#### Research Background

We value candidates who have demonstrated strong technical depth through one or more of:

- ML/AI research during undergraduate, master's, or PhD studies

- Publications or meaningful research contributions

- Open-source ML contributions

- Competitive programming

- Building large-scale ML systems from first principlesA strong undergraduate degree is expected, ideally from a highly selective technical university. Advanced degrees are welcome but **not required**.

#### What Makes Someone Successful Here

You are likely to thrive in this role if you:

- Have extremely strong technical fundamentals.

- Are genuinely interested in understanding how modern language models work internally.

- Prefer building and improving models rather than simply applying existing LLMs to business use cases.

- Are comfortable moving between research and engineering.

- Have high energy, intellectual curiosity, and low ego.

- Enjoy working in small, fast-moving teams.

- Are comfortable tackling problems that do not yet have established solutions.

- Can independently turn research ideas into working systems and experiments.

#### Nice to Have

- Experience at an early-stage AI startup.

- Contributions to open-source ML frameworks or research projects.

- Experience optimizing GPU kernels or inference engines.

- Experience building training infrastructure from scratch.

- Experience training models across large GPU clusters.

- Strong systems engineering or HPC background.

#### Not a Fit If

This role is probably not the right fit if your experience is primarily:

- Integrating existing LLM APIs into applications.

- Building RAG or chatbot applications without working on the underlying models.

- Prompt engineering without model training experience.

- Working exclusively in large, highly structured engineering organizations with narrowly defined responsibilities.

#### Location

**San Francisco, CA**

This is an **on-site position, 5 days per week**, based in San Francisco's Financial District.

#### Visa Sponsorship

Visa transfers may be supported, including candidates currently on statuses such as **OPT or H-1B**, depending on individual circumstances.

#### Compensation

**Base Salary: $200,000 – $350,000**

Plus **competitive equity**.

Compensation will depend on experience, technical depth, research background, and expected impact.

#### Hiring Plan

We are looking to hire **multiple exceptional engineers and researchers** for this team.

---

Source: Pragmatike's own career page, read by RemJobs. Canonical HTML version: https://www.remjobs.works/job/pragmatike-member-of-ml-technical-staff-5d634954-7a88-4eda-ae5f-0dbe12f6837a