Contract Position:RL Engineer

Our client is a Palo Alto–based AI infrastructure and talent platform founded in 2018. It helps companies connect with remote software developers using AI-powered vetting and matching technology. Originally branded as the Intelligent Talent Cloud,enabled companies to spin up their engineering dream team in the cloud by sourcing and managing vetted global talent. In recent years, they have evolved to support AI infrastructure and AGI workflows, offering services in model training, fine-tuning, and deployment—powered by their internal AI platform, ALAN, and backed by a vast talent network. They reported $300 million in revenue and reached profitability. Their growth is driven by demand for annotated training data from AI labs, including major clients like OpenAI, Google, Anthropic, and Meta.

Job Description

Job Title: RL Engineer

Location: Pan India

Experience: 7+ yrs.

Employment Type: Contract to hire

Work Mode: Remote

Notice Period: - Immediate joiners

Roles & Responsibilities

Role Overview:

About The Role

builds large-scale datasets and reinforcement learning environments that power post-training for leading AI labs and enterprises. Our environments evaluate and improve AI models on complex, long-range, multi-step workflows across high-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, and Customer Experience.

Our Work Includes

Software engineering environments for coding agents

UI environments for computer-use and browser-use agents

MCP-based environments for function-calling agents across enterprise and consumer applications

We are looking for Staff Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training.

This role sits at the intersection of research and engineering. You will investigate high-impact questions, design rigorous experiments, build research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications.

You will contribute to areas including synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.

What You'll Do

Conduct Research on Frontier AI Systems

Investigate the capabilities, limitations, and training methods of frontier AI systems.

Formulate research questions that inform Turing's products, platforms, and technical strategy.

Explore new 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.

Build and Evaluate Research Systems

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 strong practices for experimental rigor, data quality, reproducibility, and interpretation.

Iterate quickly from initial hypotheses to validated technical insights.

Translate Research into Practical Impact

Collaborate with Research, Engineering, Product, and Operations teams.

Translate research findings into improvements for Turing's products, platforms, and AI capabilities.

Identify which ideas are ready to move from exploration into scalable, real-world applications.

Communicate technical findings clearly to both specialized and cross-functional audiences.

Contribute to the Research Community

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 thoughtfully within the broader AI research community.

What We're Looking For

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.

Minimum 7+ years of professional experience with 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 the following areas:

Synthetic or agentic data generation

Reinforcement learning or post-training

Model understanding

AI evaluation

Benchmarks

AI agents or tool-using systems

Strong programming skills, particularly in Python, with the ability to implement, test, and iterate quickly in a research environment.

Experience with modern AI and machine learning frameworks and tooling.

Strong scientific judgment around 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.