Pampa AgroTech is hiring a Senior Part-time ML Researcher — time-series + LLM hybrids on the Research team (Remote · Buenos Aires, Argentina). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
Pampa AgroTech — 20h/week: research prototypes blending classical forecasting with LLM-based feature suggestions.
Argentina remote.
Pampa AgroTech needs someone who can move LLM features, retrieval, agents, and evaluation from experiment to production—with tests, monitoring, and stakeholder-ready communication.
The Research group partners closely with product and domain experts; you will translate ambiguous requirements into measurable ML or LLM outcomes.
What you will do
- Design and ship LLM features, retrieval, agents, and evaluation with offline evals, dashboards, and rollback plans.
- Improve datasets, labeling, and metrics that correlate with user or business outcomes.
- Review model changes for safety, bias, latency, and infrastructure cost before wide release.
- Document decisions so on-call, support, and compliance teams can act without guesswork.
- Collaborate async with written specs, demos, and recorded design reviews.
What we look for
- Demonstrated production use of core skills such as Python, OpenAI API, PyTorch, SQL—with repos, writeups, or metrics we can verify.
- Comfort operating the listed toolchain (AWS, Docker) in real environments—not tutorials only.
- Ownership of at least one feature or model from design through post-launch review—including failure modes you handled.
- Clear communication on trade-offs (quality, cost, latency, safety) with product and non-technical stakeholders.
- Eligible to work in Argentina (or remote policy as stated in the listing).
Benefits & ways of working
- GPU cloud budget
- flexible schedule.