At a Glance
- Tasks: Create cutting-edge datasets and evaluate AI-generated code for efficiency and reliability.
- Company: Join Turing, a leading research accelerator for frontier AI labs.
- Benefits: Flexible hours, remote work, and potential for contract extensions.
- Other info: Engage in a dynamic environment with opportunities for growth and innovation.
- Why this job: Make an impact in AI while working with top-tier engineers and researchers.
- Qualifications: 3+ years of software engineering experience, strong Python skills, and full-stack application knowledge.
The predicted salary is between 60000 - 80000 € per year.
About Us: Based in San Francisco, California, Turing is the world's leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialise in software engineering, logical reasoning, STEM, multilinguality, multimodality, and agents; and by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L.
Ideal Background: This role is ideal for engineers who have built production systems at companies like Google, Microsoft, Apple, Amazon, Meta, or similar high-scale engineering organisations. We especially welcome graduates from top computer science programmes such as Stanford, MIT, Carnegie Mellon, UC Berkeley, Georgia Tech, and comparable institutions — though exceptional experience and skill always take precedence over pedigree.
Project Overview: As a Software Engineering evaluator, you will create cutting-edge datasets for training, benchmarking, and advancing large language models, collaborating closely with researchers. This includes curating code examples, providing precise solutions, and making corrections — with a primary focus on Python across backend services, data pipelines, and ML infrastructure, alongside JavaScript (including ReactJS), C/C++, Java, Rust, and Go. You will evaluate and refine AI-generated code for efficiency, scalability, and reliability, and work with cross-functional teams to enhance enterprise-level AI-driven coding solutions.
What Does a Typical Day Look Like?
- Work on AI model training initiatives by curating code examples, building solutions, and correcting code — primarily in Python, with additional work in JavaScript (including ReactJS), C/C++, Java, Rust, and Go.
- Evaluate and refine AI-generated code to ensure that it is efficient, scalable, and reliable.
- Collaborate with cross-functional teams to enhance AI-driven coding solutions against industry performance benchmarks.
- Build agents and automated verification tools in Python that can verify the quality of code and identify error patterns.
- Hypothesize on steps in the software engineering cycle (prototyping, architecture design, API design, production implementation, launch, experiments, monitoring, operational maintenance) and evaluate model capabilities on them.
- Design verification mechanisms that can automatically verify a solution to a software engineering task.
Required Skills:
- Several years of software engineering experience (3 years or more).
- Strong expertise in Python with deep knowledge of frameworks, tooling, and best practices for building production-grade software.
- Experience building full-stack applications and deploying scalable software using modern languages and tools.
- Deep understanding of software architecture, design, development, debugging, and code quality/review assessment.
- Excellent oral and written communication skills for clear, structured evaluation rationales.
Engagement Details:
- Commitment: flexible engagement, minimum 10 hrs/week, up to 40 hrs/week.
- Type: Contractor (no medical/paid leave).
- Duration: 1 month (potential extensions based on performance and fit).
- Location: Candidates must be based in the United States.
Evaluation Process: The application process takes 15–30 minutes. Completion of an AI video interview is required. Note: As part of assessments you will go through an AI video interview. After applying, you will receive an email with a login link. Please use that link to access the portal and complete your profile.
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Remote Senior Software Engineer – LLM Evaluation (US-based) in Ashton-under-Lyne employer: Turing
Turing is an exceptional employer that fosters a dynamic and innovative work culture, perfect for Senior Software Engineers looking to make a significant impact in the AI field. With a focus on employee growth, Turing offers flexible engagement opportunities and the chance to collaborate with top-tier researchers, all while working remotely from the comfort of your home in the US. Join us to be part of a pioneering team that is at the forefront of AI advancements, where your contributions will drive meaningful results and shape the future of technology.
StudySmarter Expert Advice🤫
We think this is how you could land Remote Senior Software Engineer – LLM Evaluation (US-based) in Ashton-under-Lyne
✨Tip Number 1
Get your networking game on! Reach out to folks in the industry, especially those who work at companies like Google or Amazon. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Prepare for that AI video interview! Brush up on your coding skills and be ready to showcase your problem-solving abilities. Practising common coding challenges can really help you stand out.
✨Tip Number 3
Show off your projects! Whether it's a GitHub repo or a personal website, having a portfolio of your work can make a huge difference. It gives potential employers a taste of what you can do.
✨Tip Number 4
Don't forget to apply through our website! It's the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Remote Senior Software Engineer – LLM Evaluation (US-based) in Ashton-under-Lyne
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your application to highlight your experience with Python and other relevant languages. We want to see how your skills align with the role, so don’t hold back on showcasing your best projects!
Showcase Your Communication Skills:Since clear communication is key in this role, ensure your written application reflects that. Use structured language and be concise while explaining your past experiences and how they relate to the job.
Highlight Relevant Experience:If you've worked on AI model training or have experience with full-stack applications, make it known! We love seeing how your background fits into our needs, so don’t forget to mention any relevant projects or roles.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother process. It’s quick and easy, and you’ll get all the info you need about the role and our company!
How to prepare for a job interview at Turing
✨Know Your Tech Stack
Make sure you’re well-versed in Python and the other languages mentioned, like JavaScript, C/C++, and Rust. Brush up on frameworks and best practices for building production-grade software, as you’ll likely be asked to discuss your experience with these technologies.
✨Showcase Your Problem-Solving Skills
Prepare to demonstrate how you've tackled complex software engineering challenges in the past. Think of specific examples where you’ve built scalable solutions or improved code efficiency, as this will highlight your ability to evaluate and refine AI-generated code.
✨Communicate Clearly
Since excellent communication skills are a must, practice articulating your thought process clearly and concisely. Be ready to explain your evaluation rationales and how you collaborate with cross-functional teams, as this will show you can work effectively in a team environment.
✨Familiarise Yourself with AI Concepts
Given the role's focus on AI model training and evaluation, it’s beneficial to brush up on AI concepts and methodologies. Understand how large language models work and be prepared to discuss how you would approach curating datasets and evaluating model capabilities.
