Staff Research Engineer – Frontier AI, RL & Evaluation

Careerflow.ai•Texas, NY

About The Position

Careerflow Human Data Labs partners with AI companies to bring real-world professional expertise into their products. The company builds large-scale datasets and reinforcement learning environments that support post-training for AI labs and enterprises. These environments evaluate and improve AI models on complex, long-range, multi-step workflows in areas including Finance, Sales, Retail, Developer Tools, Collaboration, and Customer Experience. They include software engineering environments for coding agents, UI environments for computer-use and browser-use agents, and MCP-based environments for function-calling agents across enterprise and consumer applications. The Staff Research Engineer advances understanding of frontier AI systems and develops practical innovations in data, algorithms, evaluation, and training. The role sits at the intersection of research and engineering: investigating high-impact questions, designing rigorous experiments, building research-grade prototypes and tooling, and collaborating with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications. Areas of contribution include synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.

Requirements

PhD or Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, or a closely related technical field; exceptional equivalent research experience will also be considered.
At least 7 years of professional experience, including significant research engineering experience working on machine learning or frontier AI systems.
Strong foundations in machine learning and practical experience designing experiments, training models, evaluating models, or developing AI systems.
Demonstrated research experience in one or more of: synthetic or agentic data generation, reinforcement learning or post-training, model understanding, AI evaluation, benchmarks, or AI agents and tool-using systems.
Strong programming skills, particularly in Python, and ability to implement, test, and iterate quickly in a research environment.
Experience with modern AI and machine learning frameworks and tooling.
Strong scientific judgment concerning experimental rigor, data quality, reproducibility, and evidence-based decision-making.
Excellent written and verbal communication skills.
Ability to work independently and collaborate effectively across research and engineering teams.
Experience mentoring engineers or researchers and providing technical leadership.

Responsibilities

Investigate the capabilities, limitations, and training methods of frontier AI systems.
Formulate research questions that inform products, platforms, and technical strategy.
Explore approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
Stay current with advances in machine learning and identify opportunities for meaningful technical contributions.
Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks.
Train, test, and evaluate models using modern AI and machine learning tools.
Analyze experimental results and draw clear, evidence-based conclusions.
Establish practices for experimental rigor, data quality, reproducibility, and interpretation.
Iterate from initial hypotheses to validated technical insights.
Collaborate with Research, Engineering, Product, and Operations teams.
Translate research findings into improvements for products, platforms, and AI capabilities.
Identify ideas ready to move from exploration into scalable, real-world applications.
Communicate technical findings to specialized and cross-functional audiences.
Share findings through technical reports, publications, open-source work, workshops, or conferences where appropriate.
Contribute to research culture through technical discussions, peer review, mentorship, and collaboration.
Represent the organization thoughtfully within the broader AI research community.