Responsibilities

We’re looking for a Research Engineer to build and improve the systems that turn complex knowledge into formal representations and use them to reason and prove. You’ll work closely with researchers and formal methods engineers on Pramaana’s domain formalizer, autoformalizer, and prover, making these systems more accurate, repeatable, and efficient.
This is a hands-on role combining software engineering with careful experimentation. Your work could include building model and tool orchestration, improving evaluation, developing data pipelines and training environments, or implementing new representations. You’ll own a focused workstream based on your strengths and the team’s needs, with room to contribute across the research stack.
You’ll take ideas from an initial experiment to a tested implementation, investigate failures, and measure whether changes improve correctness, coverage, latency, and cost.
Build next-generation neuro-symbolic AI systems at the intersection of foundation models, formal methods, automated reasoning, and verification.
Design rigorous evaluation frameworks and research benchmarks to study reasoning capabilities, generalization, robustness, and computational efficiency.
Develop advanced post-training and data synthesis pipelines, exploring techniques in synthetic data generation, reinforcement learning, and model self-improvement.
Research and implement approaches to knowledge representation, compositional reasoning, program synthesis, and constraint-based inference for complex real-world domains.
Drive end-to-end research and engineering, from rapid prototyping and controlled experimentation to ablation studies, performance optimization, and scalable production systems.
Strong software engineering fundamentals and hands-on experience building large-scale ML systems, research infrastructure, or high-performance training pipelines.
Experience training or post-training large foundation models, including distributed training, fine-tuning, reinforcement learning, and large-scale experimentation.
Deep proficiency in Python and modern ML frameworks, such as PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, or equivalent distributed training and inference infrastructure.
Experience building agentic AI systems, including tool use, multi-step reasoning, agent orchestration, and evaluation of complex model behaviors.

Research Engineer at Pramaana Labs · LumiJobs

Research Engineer

Responsibilities

Qualification

Required

Company

Pramaana Labs

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