Salary
≈ $13k – $33k per year (Estimated)
Location
In office (Bengaluru)
Experience
2+ years exp
First seen by Alion on Oct 20, 2026.
Overview
Company
Impact
Profile match
Sparkinity is a UK B2B wholesale marketplace connecting independent retailers with brands and suppliers, offering curated products, free samples, flexible payment terms, ordering, logistics and retail growth tools.
We're seeking an AI Research Engineer to own model evaluations end-to-end, turning ambiguous capabilities into rigorous, reproducible benchmarks. Working closely with researchers, you will design datasets, analyze model behavior at scale, and directly shape the training lifecycle.
Responsibilities:
- Design rigorous benchmarks for reasoning, coding, and agentic capabilities across C/C++, Rust, embedded systems, hardware specifications, and engineering tasks.
- Develop deterministic evaluators using compilers, emulators, simulators, static analysis, formal verification, and hardware-in-the-loop systems.
- Build distributed infrastructure to run evaluations reliably against models and live training checkpoints.
- Diagnose regressions and anomalous results, separating model failures from issues in prompts, data, evaluators, or infrastructure.
- Design robust metrics, difficulty curricula, contamination-resistant tasks, and experiments around prompting, sampling, and scaffolding.
- Turn evaluation failures into targeted datasets, reward signals, and new training objectives in collaboration with RL researchers.
- Create dashboards, experiment tracking, and evaluation libraries that make model performance easy to understand and trust.
- Own the development of cutting-edge infrastructure from day one.
- Solve complex engineering challenges that directly shape our product roadmap.
- Accelerate your career alongside elite researchers in a fast-paced environment.
- Work closely with a brilliant, collaborative team of industry pioneers.
Requirements:
- Strong Python programming and software engineering fundamentals.
- Experience building benchmarks, evaluation systems, automated graders, research infrastructure, or similar testbeds.
- Strong understanding of LLMs and modern evaluation methodologies.
- Strong analytical and experimental thinking; you care deeply about whether a metric actually measures the intended capability.
- Experience debugging complex systems and investigating unexpected experimental results.
- Strong written and verbal communication.
Skills
Agentic AI
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