Frequently Asked Questions:
What does a AI Researcher do?
An AI Researcher pushes the frontier of what models can do: novel architectures, new training methods, evaluation methodologies, and the experimental work that informs which approaches make it to production. They live in PyTorch, JAX, and Hugging Face, run experiments on multi-GPU clusters, and publish or share findings internally. The role demands deep math, deep coding, and the patience to spend weeks on experiments that often don't work.
How much does it cost to hire a AI Researcher?
AI Researcher salaries are among the highest in tech. Expect an average U.S. salary of $9,400 per month compared with an average Latin American salary of $3,500 per month, creating potential savings of up to 63%. LatAm has built a small but elite AI research bench, often holding PhDs from top programs and direct experience publishing or working at U.S. AI labs.
Can a U.S.-based company hire a remote AI Researcher from Latin America?
Yes, AI research is increasingly remote-friendly. South contracts the researcher locally, runs payroll and compliance, and bills you monthly. They join your GitHub, Weights & Biases, and Slack day one. Time-zone alignment matters for paper-reading group, model debugging sessions, and live discussion of experimental results.
What skills should I look for when hiring a AI Researcher?
Deep ML fundamentals (transformers, diffusion, RL), strong PyTorch or JAX skills, and a publication or open-source track record. Look for researchers who can walk through their own work and a recent paper with technical depth. Run a paper-reading interview: pick a recent paper and discuss what they'd change. The signal is genuine curiosity plus rigorous experimental discipline.
What tools and frameworks should a AI Researcher know?
Python with PyTorch or JAX. Hugging Face Transformers and datasets. Weights & Biases or MLflow for experiment tracking. CUDA, NCCL, and distributed training tooling (DeepSpeed, FSDP). Compute on AWS, GCP, or specialized providers. SLURM or Ray for cluster scheduling. ArXiv, papers with code, and OpenReview for staying current.
How is a AI Researcher different from a Machine Learning Engineer?
An ML Engineer ships models to production: training pipelines, deployment, monitoring. An AI Researcher pushes capability: new architectures, new methods, new evals. They share a coding stack but diverge on output. Researchers ship papers and prototypes. Engineers ship products. For applied work, hire an ML Engineer. For frontier capability work, hire a Researcher.
How can South help me hire a AI Researcher?
South vets LatAm AI researchers through technical interviews, paper deep-dives, and reviews of prior work. You see a shortlist in seven to ten days, run your own interviews, and onboard inside two to three weeks. South handles the contract, monthly payments, and a replacement guarantee. Schedule a free call here to get started!
Interview Questions:
- Walk me through your most ambitious experiment. What was the hypothesis and what did you learn?
- Pick a recent paper you'd want to reproduce. What would you change in their approach?
- How do you decide between scaling up versus changing the algorithm when results plateau?
- Walk me through how you'd evaluate a new fine-tuning method against an existing baseline.
- How do you debug a training run that diverged after 80% of the planned compute?
- When do you reach for RL versus supervised fine-tuning versus prompt-engineering?
- Walk me through your approach to ablation studies in a paper-quality experiment.
- Pitch me on a research direction you'd push for inside our company in your first 90 days.
- Walk me through an experiment you killed because results were flat. What did you learn?
- What's a research trend you think is overrated, and what fundamental gets ignored?
Related Roles:
Related Skills:
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