Staff Research Engineer - Frontier AI and Reinforcement Learning - US and Canada

Job summary

Investigate frontier AI capabilities and training methods, then design and build research datasets, experiments, prototypes, tools, and evaluation frameworks. Analyze and validate findings rigorously, collaborate across teams to translate them into scalable systems, and communicate results through reports, publications, open-source work, and research forums.

Job details

Advance frontier AI through research and engineering across synthetic and agentic data, reinforcement learning, post-training, model understanding, benchmarks and evaluation. You will investigate high-impact questions, build research-grade prototypes and help turn validated findings into scalable AI systems. What You Will Do Investigate frontier-model capabilities, limitations and training methods, formulate research questions and explore new approaches. Build research datasets, experiments, prototypes, tooling and evaluation frameworks. Train, test and evaluate models using modern ML tools. Analyze results with scientific rigor, data quality, reproducibility and evidence-based interpretation. Iterate from hypotheses to validated insights. Collaborate across Research, Engineering, Product and Operations to translate findings into practical systems. Communicate findings through technical reports, publications, open-source work and research forums where appropriate. Contribute through peer review, mentorship and technical leadership. What You Will Bring 7+ years of professional experience with significant research engineering experience in machine learning or frontier AI systems. A PhD or master’s degree in AI, machine learning, computer science or a closely related technical field, or exceptional equivalent research experience. Strong ML foundations and experience designing experiments, training or evaluating models, or developing AI systems. Research experience in at least one of synthetic or agentic data generation, reinforcement learning or post-training, model understanding, AI evaluation, benchmarks, or tool-using AI agents. Strong Python programming and experience implementing, testing and iterating with modern ML frameworks and tooling. Scientific judgment, rigorous reproducible methods and attention to data quality. Strong written and verbal communication, independent work and effective cross-functional collaboration. Experience mentoring engineers or researchers and providing technical leadership. PROJECT DETAILS Expected project length is three months, with an immediate start. Availability of at least 40 hours per week, 8 hours per day and four hours of daily Pacific Time overlap is required. Selection includes an approximately 20-minute client AI interview and a 45-60-minute technical delivery interview. Include your LinkedIn profile URL in your required resume. LOCATION AND COMPENSATION This remote opportunity is open to candidates in the United States and Canada at $57 USD per hour.

What you’ll do

Investigate frontier AI capabilities and training methods, then design and build research datasets, experiments, prototypes, tools, and evaluation frameworks. Analyze and validate findings rigorously, collaborate across teams to translate them into scalable systems, and communicate results through reports, publications, open-source work, and research forums.

Requirements

Requires 7+ years of professional experience, significant research engineering experience in machine learning or frontier AI, and a PhD, master’s degree, or exceptional equivalent research experience. Candidates should have strong ML and Python skills, relevant research experience in at least one listed area, rigorous and reproducible research practices, strong communication and collaboration skills, and experience mentoring or providing technical leadership.

Listed skills

• Machine learning · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning
• Frontier AI
• Reinforcement Learning
• Synthetic Data Generation
• Agentic Data Generation
• Post-Training
• Model Evaluation
• AI Benchmarks
• Tool-Using AI Agents
• Python
• Modern ML Frameworks
• Experimental Design
• Research Engineering
• Data Quality
• Reproducible Research
• Technical Leadership

Job areas

• Technology
• Science & Research
• Engineering
• Software
• Data & Analytics

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