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Member of Technical Staff, Model Training (applied research)

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 ; you can apply on micro1.

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Level's read on this job

  • On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
  • The posting lists this role as remote.
  • Tools and skills the posting cites include PyTorch, AI agents and fine tuning.
  • Level lists 314 other open roles at micro1.
  • The role was posted in the last few days.

AI in this role

pytorch
ai-agentsfine-tuningai-research

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

  1. Train, fine-tune, and adapt language, vision-language, and robotics models using supervised fine-tuning (SFT), reinforcement learning (RL), imitation learning, and distillation.
  2. Develop specialized models for complex applications, including multimodal understanding, robotic perception and control, and autonomous agents capable of reasoning, planning, and tool use.
  3. Build simulation-based training and evaluation environments to study policy learning, task generalization, and the transfer of capabilities from simulated to real-world settings.
  4. Combine expert demonstrations, human feedback, synthetic datasets, and environment-generated trajectories into effective training datasets, reward signals, and learning curricula.
  5. Develop specialized models that power micro1's flow ecosystem, improving data generation, annotation, quality assurance, and automated grading workflows.
  6. Incorporate expert corrections and model performance feedback into iterative training cycles to continuously improve accuracy, robustness, and task success.
  7. Optimize models for production inference, balancing quality, latency, throughput, and computational efficiency against frontier-model baselines.
  8. Collaborate with research and platform engineering teams to transition promising prototypes into scalable, production-ready model deployments.


What We're Looking For

  1. Strong foundations in machine learning and deep learning, with hands-on experience training, adapting, and evaluating modern AI models.
  2. Practical experience in one or more areas, including large language models, vision-language models, robotics, reinforcement learning, or autonomous agents.
  3. Strong proficiency in Python and PyTorch, with the ability to develop reliable training, evaluation, and inference pipelines.
  4. Experience translating application requirements into measurable model objectives, benchmarks, and performance improvements.
  5. Solid software engineering fundamentals, including the ability to build maintainable systems and move research prototypes toward production.
  6. Familiarity with model adaptation techniques such as fine-tuning, distillation, imitation learning, or reinforcement learning.
  7. Strong experimental judgment and the ability to independently identify performance bottlenecks, evaluate trade-offs, and iterate toward effective solutions.


Nice to Have

  1. Experience with robotics, embodied AI, vision-language-action models, or simulation-based policy learning.
  2. Familiarity with agent frameworks, tool-using models, and environment-driven training.
  3. Experience with synthetic data generation, human-in-the-loop learning, or automated evaluation systems.
  4. Knowledge of inference optimization techniques, including quantization, batching, model compression, and efficient serving.
  5. Experience deploying specialized models in production environments with demanding latency, reliability, or cost requirements.
  6. 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 –$320,000. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. micro1 provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high-performing, remote-first workforce.


micro1 is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to support@micro1.ai.


Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.

How we rate this

Member of Technical Staff, Model Training (applied research) at micro1 rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsFine-tuningAI ResearchPyTorch

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. Tell me about a research question you investigated. What did you find?
  4. What's a project where you used PyTorch hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: AI agents, Fine-tuning, AI Research, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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