Research Engineer, Post-Training

Jurisphere.ai; Bengaluru; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

Jurisphere is hiring a Research Engineer, Post-Training in Bengaluru, India, to advance legal AI systems. You will work across model training, datasets, evaluations, agents, and expert feedback, transforming observed failures into experiments and measurable improvements.

You will build datasets from expert feedback and legal workflows, design reward systems for legal reasoning, and push for scalable production systems. Strong Python/ML engineering and open‑weight LLM experience are required.

Qualifications

• Hands‑on experience training or post‑training LLMs, particularly open‑weight models.
• Strong Python and ML engineering skills.
• Experience with SFT, preference learning, RL, reward modelling, distillation, agents, or evaluations.
• Strong experimental judgment: formulate hypotheses, run controlled experiments, analyse model behaviour.
• Ability to independently own technically difficult, loosely defined problems.

Responsibilities

• Run post‑training experiments across SFT, preference optimization, RLHF/RLAIF, reward modelling, distillation, and related techniques.
• Build training datasets from expert feedback, model traces, legal workflows, and synthetic data.
• Design evaluations and reward systems for legal reasoning, research, drafting, document analysis, and citation accuracy.
• Study model and agent behaviour to identify systematic failure modes.
• Build agent environments involving retrieval, tools, subagents, validation loops, and long‑horizon workflows.
• Turn research findings into better training data, model recipes, evaluations, and production systems.
• Work closely with engineers and legal experts to translate expert judgment into scalable systems.

Skills

LLMs

Python

ML engineering

SFT

RLHF/RLAIF

Reward modelling

Distillation

Agents

Evaluations

Experimental judgment

Independent ownership

Job description

Jurisphere is building AI systems that can do serious legal work.

We recently raised a $2.2M seed round led by Info Edge Ventures, with participation from Flourish Ventures, Antler, and 8i Ventures. Today, Jurisphere is used by 500+ organisations and 20,000+ legal professionals, including ICICI Bank, Unilever, Philips, Tata Capital, and CMS IndusLaw.

We are a small team working at the intersection of frontier AI, legal reasoning, and high-stakes professional workflows.

Research Engineer, Post-Training

Location: Bengaluru, India

Type: Full Time

Team: Engineering / AI Research

Why Jurisphere

Jurisphere is building AI systems that can do serious legal work.

We recently raised a $2.2M seed round led by Info Edge Ventures, with participation from Flourish Ventures, Antler, and 8i Ventures. Today, Jurisphere is used by 500+ organisations and 20,000+ legal professionals, including ICICI Bank, Unilever, Philips, Tata Capital, and CMS IndusLaw.

We are a small team working at the intersection of frontier AI, legal reasoning, and high-stakes professional workflows.

The Role

We’re hiring a Research Engineer, Post-Training to help make our models meaningfully better at legal work.

You will work across model training, datasets, evaluations, agents, and expert feedback — taking problems from an observed model failure through to an experiment, training signal, and measurable improvement.

This is a hands‑on research engineering role for someone comfortable working with open-weight models and ambiguous research problems.

What You’ll Do

• Run post‑training experiments across SFT, preference optimization, RLHF/RLAIF, reward modelling, distillation, and related techniques.
• Build training datasets from expert feedback, model traces, legal workflows, and synthetic data.
• Design evaluations and reward systems for legal reasoning, research, drafting, document analysis, and citation accuracy.
• Study model and agent behaviour to identify systematic failure modes.
• Build agent environments involving retrieval, tools, subagents, validation loops, and long‑horizon workflows.
• Turn research findings into better training data, model recipes, evaluations, and production systems.
• Work closely with engineers and legal experts to translate expert judgment into scalable systems.

What You Have

• Hands‑on experience training or post‑training LLMs, particularly open‑weight models.
• Strong Python and ML engineering skills.
• Experience with some combination of SFT, preference learning, RL, reward modelling, distillation, agents, or evaluations.
• Strong experimental judgment: you can formulate hypotheses, run controlled experiments, analyse model behaviour, and interpret ambiguous results.
• Ability to independently own technically difficult, loosely defined problems.

Nice to have: distributed training or GPU experience, agent systems, evaluation infrastructure, research publications, open‑source contributions, or experience in high‑stakes domains such as law, finance, or healthcare.

Why This Role

Post‑training at Jurisphere is the feedback loop between models, real legal work, expert judgment, and engineering.

You’ll work across the full loop: understand why a model failed, build an evaluation that captures it, create a training signal to improve it, and measure whether the next model is actually better.

The goal: build models that are meaningfully better at doing legal work.