# Member of Technical Staff, Model Training (applied research) at micro1

micro1 is hiring a Member of Technical Staff, Model Training (applied research) for a remote role. It pays $200k-$320k a year and Level rates it Builds AI ●●●●; you can [apply on micro1](https://jobsbylevel.com/go/1e77c2b8-5b1c-415d-be43-dfe23a0bf51b?utm_medium=ai-agent).

AI Level 4, AI centrality 100 out of 100. Remote.

## Details

- Company: [micro1](https://jobsbylevel.com/companies/micro1)
- AI level: AI Level 4 (score 100 out of 100)
- Location: Remote
- Salary: $200k-$320k
- Posted: October 27, 2026
- Apply: https://jobsbylevel.com/go/1e77c2b8-5b1c-415d-be43-dfe23a0bf51b?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=member-of-technical-staff-model-training-applied-research-at-micro1-9f8f81

## Prepare for this job

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## Description

Job Title: Member of Technical Staff, Model Training (applied research) Job Type: Full-time Location : Remote The Role In this role, you will work across language models, multimodal systems, robotics, and autonomous agents, developing models optimized for specific applications and environments. You will own the full model development lifecycle, from understanding application requirements and designing training datasets to model adaptation, evaluation, and production deployment. Our objective is to build specialized models that match or exceed the quality of frontier models on targeted workloads while significantly improving inference efficiency, latency, and cost. You will operate at the intersection of applied machine learning research and production engineering, turning advances in model training into reliable, high-performance systems with measurable real-world impact. What You'll Do Train, fine-tune, and adapt language, vision-language, and robotics models using supervised fine-tuning (SFT), reinforcement learning (RL), imitation learning, and distillation. Develop specialized models for complex applications, including multimodal understanding, robotic perception and control, and autonomous agents capable of reasoning, planning, and tool use. Build simulation-based training and evaluation environments to study policy learning, task generalization, and the transfer of capabilities from simulated to real-world settings. Combine expert demonstrations, human feedback, synthetic datasets, and environment-generated trajectories into effective training datasets, reward signals, and learning curricula. Develop specialized models that power micro1's flow ecosystem, improving data generation, annotation, quality assurance, and automated grading workflows. Incorporate expert corrections and model performance feedback into iterative training cycles to continuously improve accuracy, robustness, and task success. Optimize models for production inference, balancing quality, latency, throughput, and computational efficiency against frontier-model baselines. Collaborate with research and platform engineering teams to transition promising prototypes into scalable, production-ready model deployments. What We're Looking For Strong foundations in machine learning and deep learning, with hands-on experience training, adapting, and evaluating modern AI models. Practical experience in one or more areas, including large language models, vision-language models, robotics, reinforcement learning, or autonomous agents. Strong proficiency in Python and PyTorch, with the ability to develop reliable training, evaluation, and inference pipelines. Experience translating application requirements into measurable model objectives, benchmarks, and performance improvements. Solid software engineering fundamentals, including the ability to build maintainable systems and move research prototypes toward production. Familiarity with model adaptation techniques such as fine-tuning, distillation, imitation learning, or reinforcement learning. Strong experimental judgment and the ability to independently identify performance bottlenecks, evaluate trade-offs, and iterate toward effective solutions. Nice to Have Experience with robotics, embodied AI, vision-language-action models, or simulation-based policy learning. Familiarity with agent frameworks, tool-using models, and environment-driven training. Experience with synthetic data generation, human-in-the-loop learning, or automated evaluation systems. Knowledge of inference optimization techniques, including quantization, batching, model compression, and efficient serving. Experience deploying specialized models in production environments with demanding latency, reliability, or cost requirements. Research contributions or open-source work in applied machine learning, multimodal AI, robotics, or agent systems. Compensation & Benefits Notice The national pay range for this full-time position is base salary of $200,000

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