Member of Technical Staff, Research Engineering

SaidGig; New York (NY); À distance; USD 180 000 - 240 000; Plein temps

Avantages offerts par ce poste

Up to 100% health-insurance premium Re

Paid time off

401(k) with company match

Remote-first workforce benefits

Résumé du poste

SaidGig is seeking a research-focused reinforcement learning engineer to turn ambitious ideas into scalable training, evaluation, and data-generation capabilities for modern AI models. You will design RL environments, build training pipelines, and collaborate with a fast-moving research team to advance real-world applications.

This role offers equity and a remote-friendly setup, with compensation ranges reflecting seniority in a cutting-edge field.

Qualifications

• Deep reinforcement learning experience with environment design and training dynamics.
• Track record building and scaling RL systems, workflows, or experimentation frameworks.
• Experience with ML-oriented data design, synthetic-data pipelines, and RL workflows.
• Knowledge of automated evaluation, model validation, and QA processes.
• Experience fine-tuning and evaluating open-source ML models.
• Clear technical communication and writing skills.
• Ability to thrive in a fast-paced, research-driven, collaborative setting.

Responsabilités

• Design self-contained RL environments with reward functions and evaluation logic.
• Develop scalable episode pipelines and multi-component training processes.
• Create automated synthetic-data generation systems to speed training.
• Build AI-driven evaluation and QA systems for grading, feedback, and model assessment.
• Fine-tune and optimize open-source RL models using internal data and strategies.
• Establish benchmarking frameworks to assess model capability, robustness, and data quality.
• Contribute to releasing evaluations on internal and external benchmarks.

Connaissances

Deep RL

RL environ design

Experimentation frameworks

ML data design

Synthetic-data pipelines

Automated evaluation

Model fine-tuning

Technical communication

Fast-paced collaboration

Outils

Open-source ML models

RL infra tooling

Description du poste

Role Overview

Build production-ready reinforcement learning systems that turn ambitious research ideas into scalable training, evaluation, and data-generation capabilities for modern AI models. You will develop novel RL environments, training pipelines, and evaluation systems while working closely with a fast-moving, collaborative research team.

Key Responsibilities

• Design self-contained RL environments for complex real-world tasks, including reward functions, verifiers, and evaluation logic.
• Develop and scale episode pipelines and multi-component training processes to enable reproducible experimentation.
• Create automated synthetic-data generation systems that accelerate training cycles while maintaining quality.
• Build AI-driven evaluation and quality-assurance systems for automated grading, validation, feedback loops, and model assessment.
• Fine-tune and optimize open-source RL models using internally generated datasets and custom training strategies.
• Establish benchmarking frameworks to assess model capability, robustness, and data quality across tasks.
• Contribute to releasing and analyzing evaluations on internal and external benchmark platforms.

Qualifications

• Deep reinforcement learning experience, including RL environment design and training dynamics.
• A strong record of building and scaling RL systems, workflows, pipelines, or experimentation frameworks.
• Experience with ML-oriented data design, automation, synthetic-data pipelines, and RL workflows.
• Knowledge of automated evaluation, model validation, and quality-assurance processes.
• Experience fine-tuning and evaluating open-source machine learning models.
• Clear technical communication and writing skills.
• Ability to thrive in a fast-paced, research-driven, highly collaborative setting.

Preferred Qualifications

• Experience publishing benchmarks, evaluations, or research artifacts.
• Familiarity with model-evaluation ecosystems and benchmark frameworks.
• Experience with infrastructure that supports large-scale RL experimentation.

Work Terms

Full-time, remote position.

Compensation

Listed compensation range: $300, 000 to $600, 000 per year.

The stated national base-salary range for this full-time role is $180, 000 to $240, 000 USD.

Equity compensation is available to all employees. Performance-based bonuses may also be available depending on the role and applicable company policies.

Benefits

• up to 100% reimbursement of health-insurance premiums
• paid time off
• a 401(k) plan with company match
• additional benefits for a remote-first workforce

Eligibility

Qualified applicants are considered without regard to race, color, religion, sex, including pregnancy, sexual orientation, or gender identity, national origin, age, disability, genetic information, veteran status, or other characteristics protected by applicable law.