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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 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. Days 30–60: Run a focused experiment: a new 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. 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
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.