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.