AI Research Engineer
First seen by Alion on Oct 9, 2026.
AI Research Engineer role at a VC funded deep tech startup - AI Infrastructure for Embedded Systems
Soch Street is a Strategic Talent Partner helping the company to hire.
Job Title : AI Research Engineer
Job Location : Whitefield, Bengaluru | 5 days WFO
Reports to : CEO & Co-Founder
About 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.
Skills
Artificial Intelligence, Machine Learning, IT Research, NLP, LLM, Python, Deep Learning