{"id":2156182,"url":"https://alion.io/job/soch-street-ai-research-engineer","title":"AI Research Engineer","company":{"id":3798420,"name":"Soch Street","domain":"sochstreet.com","url":"https://alion.io/company/soch-street","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"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","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":"LLM","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":"Reward Modeling","optional":false},{"name":"RLAIF","optional":false},{"name":"RLHF","optional":false},{"name":"RTOS","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Tokenization","optional":false}],"status":"live","first_seen_at":"2026-10-09T05:23:53Z","employer_posted_date":null,"last_verified_at":"2026-10-09T05:23:53Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"AI Research Engineer role at a VC funded deep tech startup - AI Infrastructure for Embedded Systems\n\nSoch Street is a Strategic Talent Partner helping the company to hire.\n\nJob Title : AI Research Engineer\n\nJob Location : Whitefield, Bengaluru | 5 days WFO\n\nReports to : CEO & Co-Founder\n\nAbout the Company : \n\nThe 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.\n\nWhat you'll own : \n\nSynthetic data generation : \n\n- 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.\n\n- 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.\n\n- Evaluate data quality rigorously - not just statistical measures, but whether models trained on the data actually improve on real embedded engineering tasks.\n\n- Identify the highest-value data gaps in our training corpus and design generation pipelines to fill them.\n\nDomain-specific model training and adaptation : \n\n- Own continued pre-training and instruction-tuning runs across our company's domain-specific model families.\n\n- Design and evaluate training recipes : data mixture, tokenizer configuration, instruction format, curriculum, and RLHF/RLAIF alignment approaches.\n\n- Benchmark model families rigorously - not just perplexity, but task-level accuracy on hardware-specific code generation, compliance repair, and specification-grounded reasoning.\n\n- Maintain our company's model evaluation infrastructure : curated benchmark suites, regression pipelines, and human eval protocols tied to real customer tasks.\n\nRL with hardware feedback : \n\n- 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.\n\n- Develop reward models and preference data pipelines from hardware pass/fail signals, user feedback, and formal verification outcomes.\n\n- Investigate and prototype sample-efficient RL approaches suitable for the low-throughput, high-cost signal that physical hardware evaluation provides.\n\nNeurosymbolic methods and formal verification : \n\n- Research and prototype approaches that combine neural code generation with symbolic reasoning and formal analysis tools.\n\n- Investigate feedback loops between generative models and verification systems - how verification outcomes can improve model behavior over time.\n\n- Explore training techniques that make model-generated code more amenable to formal analysis without requiring explicit instruction at inference time.\n\nResearch translation : \n\n- 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.\n\n- Run experiments to evaluate whether promising techniques hold up on embedded/systems tasks - many results from general coding benchmarks do not transfer.\n\n- Produce clear findings that drive product and model decisions : what to adopt, what to discard, and what to invest in further.\n\nRequired experience : \n\n- 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.\n\n- 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.\n\n- 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.\n\n- Rigorous empirical methodology : you design controlled experiments, track what changes between runs, and resist overclaiming from noisy results.\n\n- 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.\n\nStrong-to-have : \n\n- 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).\n\n- Experience with reinforcement learning from human feedback (RLHF), AI feedback (RLAIF), or execution-based reward (RL from compiler/test/verifier outcomes).\n\n- Knowledge of embedded or systems software : C/C++, RTOS, safety standards (MISRA, AUTOSAR, IEC 61508, DO-178C), hardware abstraction layers, or MCU architecture.\n\n- Experience with synthetic data generation for language model training - not just data augmentation, but designing generation pipelines that produce novel, high-quality training signal.\n\n- Published work on code generation, program synthesis, neurosymbolic methods, or domain adaptation for LLMs.\n\n- Familiarity with the industrial deployment constraints of our customers : air-gapped environments, on-prem inference, compute-constrained hardware.\n\nWhat we don't need : \n\nA 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.\n\nFirst 90 days : \n\n1. 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.\n\n2. 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.\n\n3. 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.\n\nCompensation & logistics : \n\n- Competitive early-stage equity + salary.\n\n- In-person, Bangalore office.\n\n- Small team - direct access to founders, platform engineers, and applied AI engineers.\n\n- Support for publishing research where findings are non-proprietary.\n\nSkills\nArtificial Intelligence, Machine Learning, IT Research, NLP, LLM, Python, Deep Learning","description_format":"text","description_chars":7066,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-09T06:37:45Z"}],"visa":[],"liveness":{"score":84,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.843,"p_room":1,"age_days":5,"expected_fill_days":19,"reasons":["seen:5","win:early"],"computed_at":"2026-10-14T06:34:16Z"},"pay":null,"html_url":"https://alion.io/job/soch-street-ai-research-engineer","json_url":"https://alion.io/job/soch-street-ai-research-engineer.json","meta":{"generated_at":"2026-10-15T03:30:47Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":4951,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-16T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":3798420},"rest":"https://alion.io/mcp/rest/get_company?id=3798420"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsoch-street-ai-research-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fsoch-street-ai-research-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fsoch-street-ai-research-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/soch-street-ai-research-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsoch-street-ai-research-engineer"}]}