AI Research Engineer

AI Research Engineer role at a VC funded deep tech startup - AI Infrastructure for Embedded SystemsSoch Street is a Strategic Talent Partner helping the company to hire.Job Title : AI Research EngineerJob Location : Whitefield, Bengaluru | 5 days WFOReports to : CEO & Co-FounderAbout the Company : The company builds sovereign, domain-specific AI models and developer tooling for regulated, hardware-first industries automotive, semiconductor, telecom, and defence. We are converting enterprise pilots to contracts and need our first marketing leader to own the brand and GTM narrative end-to-end.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 our company'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 our company'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 our company'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 our company'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 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.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. (ref:hirist.tech)