{"id":64820,"url":"https://alion.io/job/ubds-digital-ai-research-engineer","title":"AI Research Engineer","company":{"id":8401,"name":"UBDS Digital","domain":"ubdsdigital.com","url":"https://alion.io/company/ubds-digital","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":94,"open_postings":8,"ghost_share":0,"stale_share":0.25,"repost_share":0,"time_to_fill_p50_days":23,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":54000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"Data Augmentation","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Perplexity","optional":false},{"name":"Pre-training","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLHF","optional":false},{"name":"RTOS","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Tokenization","optional":false}],"status":"live","first_seen_at":"2026-08-20T03:53:22Z","employer_posted_date":null,"last_verified_at":"2026-08-20T03:53:22Z","board_verified":false,"closed_at":null,"days_open":58,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":58},"description":"The core responsibilities for the job include the following:\nSynthetic data generation:\nDesign, 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.\nInvent and test data generation strategies: specification-to-code synthesis, compliance annotation, formal requirement extraction from natural language, and multi-step reasoning trace generation from hardware documentation.\nEvaluate data quality rigorously, not just statistical measures, but whether models trained on the data actually improve on real embedded engineering tasks\nIdentify the highest-value data gaps in our training corpus and design generation pipelines to fill them.\nDomain-specific model training and adaptation:\nOwn continued pre-training and instruction-tuning runs across H2Loop's domain-specific model families.\nDesign and evaluate training recipes: data mixture, tokenizer configuration, instruction format, curriculum, and RLHF/RLAIF alignment approaches.\nBenchmark model families rigorously not just on perplexity, but on task-level accuracy on hardware-specific code generation, compliance repair, and specification-grounded reasoning.\nMaintain H2Loop's model evaluation infrastructure: curated benchmark suites, regression pipelines, and human eval protocols tied to real customer tasks.\nRL with hardware feedback:\nDesign and run reinforcement learning experiments using real hardware boards as the reward environment, generating code that either works on the hardware or doesn't, producing a ground-truth training signal that no synthetic benchmark can replicate.\nDevelop reward models and preference data pipelines from hardware pass/fail signals, user feedback, and formal verification outcomes.\nInvestigate and prototype sample-efficient RL approaches suitable for the low-throughput, high-cost signal that physical hardware evaluation provides.\nNeurosymbolic methods and formal verification:\nResearch and prototype approaches that combine neural code generation with symbolic reasoning and formal analysis tools.\nInvestigate feedback loops between generative models and verification systems and how verification outcomes can improve model behavior over time.\nExplore training techniques that make model-generated code more amenable to formal analysis without requiring explicit instruction at inference time.\nResearch translation:\nMonitor 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.\nRun experiments to evaluate whether promising techniques hold up on embedded/systems tasks; many results from general coding benchmarks do not transfer.\nProduce clear findings that drive product and model decisions: what to adopt, what to discard, and what to invest in further.\nFirst 90 days:\nDays 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.\nDays 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.\nDays 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.\nRequirements:\nPhD 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.\nHands-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.\nStrong 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.\nRigorous empirical methodology: you design controlled experiments, track what changes between runs, and resist overclaiming from noisy results.\nStrong Python engineering skills: you can implement ideas cleanly, build evaluation pipelines, and productionize experiments without needing a separate engineering team to translate your notebooks.\nStrong-to-have:\nDomain knowledge in formal methods or programming verification: familiarity with model checkers (CBMC, Frama-C), theorem provers (Lean 4 Coq, Isabelle), or SMT solvers (Z3).\nExperience with reinforcement learning from human feedback (RLHF), AI feedback (RLAIF), or execution-based reward (RL from compiler/test/verifier outcomes).\nKnowledge of embedded or systems software: C/C++, RTOS, safety standards (MISRA, AUTOSAR, IEC 61508 DO-178C), hardware abstraction layers, or MCU architecture.\nExperience with synthetic data generation for language model training, not just data augmentation, but designing generation pipelines that produce novel, high-quality training signals.\nPublished work on code generation, program synthesis, neurosymbolic methods, or domain adaptation for LLMs.\nFamiliarity with the industrial deployment constraints of our customers: air-gapped environments, on-prem inference, and compute-constrained 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