What you'll own

Synthetic data generation

- Design, extend, and evaluate our agentic pipeline for generating domain-specific pre-training and instruction-tuning data from public datasheets, reference manuals, and open-source embedded codebases

- Invent and test data generation strategies: specification-to-code synthesis, compliance annotation, formal requirement extraction from natural language, multi-step reasoning trace generation from hardware documentation

- Evaluate data quality rigorously — not just statistical measures, but whether models trained on the data actually improve on real embedded engineering tasks

- Identify the highest-value data gaps in our training corpus and design generation pipelines to fill them

Domain-specific model training and adaptation

- Own continued pre-training and instruction-tuning runs across H2Loop's domain-specific model families

- Design and evaluate training recipes: data mixture, tokenizer configuration, instruction format, curriculum, and RLHF/RLAIF alignment approaches

- Benchmark model families rigorously — not just perplexity, but task-level accuracy on hardware-specific code generation, compliance repair, and specification-grounded reasoning

- Maintain H2Loop's model evaluation infrastructure: curated benchmark suites, regression pipelines, and human eval protocols tied to real customer tasks

RL with hardware feedback

- Design and run reinforcement learning experiments using real hardware boards as the reward environment — generated code that either works on the hardware or doesn't, producing ground-truth training signal no synthetic benchmark can replicate

- Develop reward models and preference data pipelines from hardware pass/fail signals, user feedback, and formal verification outcomes

- Investigate and prototype sample-efficient RL approaches suitable for the low-throughput, high-cost signal that physical hardware evaluation provides

Neurosymbolic methods and formal verification

- Research and prototype approaches that combine neural code generation with symbolic reasoning and formal analysis tools

- Investigate feedback loops between generative models and verification systems — how verification outcomes can improve model behavior over time





- Explore training techniques that make model-generated code more amenable to formal analysis without requiring explicit instruction at inference time

Research translation

- Monitor the research landscape across the areas relevant to H2Loop's stack: code generation, program synthesis, neurosymbolic AI, continual learning, RL for code, formal verification, and domain adaptation

- Run experiments to evaluate whether promising techniques hold up on embedded/systems tasks — many results from general coding benchmarks do not transfer

- Produce clear findings that drive product and model decisions: what to adopt, what to discard, and what to invest in further

Required experience

- PhD or equivalent research experience in machine learning, NLP, or a closely related field — or 4+ years of industry research with a publication record you can defend

- Hands-on experience training or fine-tuning large language models: you have run training jobs, debugged training instabilities, and evaluated results against real task benchmarks, not just held-out loss

- Strong foundations in deep learning and the transformer architecture: you understand what is happening during pre-training, instruction tuning, and RLHF, not just how to call the APIs

- Rigorous empirical methodology: you design controlled experiments, track what changes between runs, and resist overclaiming from noisy results

- Strong Python engineering skills — you can implement ideas cleanly, build evaluation pipelines, and productionize experiments without needing a separate engineering team to translate your notebooks

Strong-to-have

- Domain knowledge in formal methods or program verification: familiarity with model checkers (CBMC, Frama-C), theorem provers (Lean 4, Coq, Isabelle), or SMT solvers (Z3)

- Experience with reinforcement learning from human feedback (RLHF),



AI feedback (RLAIF), or execution-based reward (RL from compiler/test/verifier outcomes)

- Knowledge of embedded or systems software: C/C++, RTOS, safety standards (MISRA, AUTOSAR, IEC 61508, DO-178C), hardware abstraction layers, or MCU architecture

- Experience with synthetic data generation for language model training — not just data augmentation, but designing generation pipelines that produce novel, high-quality training signal

- Published work on code generation, program synthesis, neurosymbolic methods, or domain adaptation for LLMs

- Familiarity with the industrial deployment constraints of our customers: air-gapped environments, on-prem inference, compute-constrained hardware

What we don't need A researcher who optimizes benchmark scores on standard datasets. We are building for a domain where the interesting problems are off the benchmark — hardware-specific, proprietary, constrained by physical reality. We need someone who is motivated by problems that don't have leaderboards yet, who can design their own evaluations, and who cares about the gap between a research result and a system that works reliably in a customer's air-gapped datacenter.

First 90 days

1. Days 1–30:

Get deep on H2Loop's AI stack — our data generation pipelines, model families, RL environments, and verification tooling. Run existing training and evaluation pipelines end-to-end. Form a clear view of where the biggest research leverage is.

2. Days 30–60:

Run a focused experiment: a current data generation strategy, a training recipe improvement, a formal verification repair loop prototype, or an RL reward model evaluation. Produce findings with clear implications for the roadmap.

3. Days 60–90:

Propose a research agenda for the next two quarters. Own at least one research thread end-to-end — from experimental design through evaluation to a concrete product or model outcome.

Compensation & logistics

- Competitive early-stage equity + salary

- In-person, Bangalore office

- Small team — direct access to founders, platform engineers, and applied AI engineers

- Support for publishing research where findings are non-proprietary

📌 AI Research Engineer (Bangalore Metropolitan Area)
🏢 Soch Street
📍 Bangalore Metropolitan Area