{"id":3170895,"url":"https://alion.io/job/skan-ai-applied-scientist-i","title":"Applied Scientist I","company":{"id":63247,"name":"Skan AI","domain":"skan.ai","url":"https://alion.io/company/skan-ai","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":{"grade":"A","score":92,"open_postings":9,"ghost_share":0,"stale_share":0.111,"repost_share":0,"time_to_fill_p50_days":80,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","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":18000,"max_usd":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":18},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Computer Use","optional":false},{"name":"Computer Vision","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RLAIF","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"LoRA","optional":true},{"name":"Machine Learning","optional":true},{"name":"NER","optional":true},{"name":"PEFT","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Structured Outputs","optional":true}],"status":"live","first_seen_at":"2026-10-23T16:00:50Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-26T13:44:43Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Be at the Forefront of the Agentic AI Revolution\nAt Skan AI, we are pioneering the context engine for human and agentic execution, bringing context from enterprise operators, systems, and processes to power how the world's largest organizations execute their most complex, mission-critical work.\nWhy Skan AI\nWe're in hyper-growth mode at exactly the right moment in history. As enterprises race to adopt agentic AI, we're uniquely positioned to deliver the clear signal they desperately need: a platform that trains and grounds AI Agents in trillions of real execution signals, enabling reliable, compliant automation of their most complex processes.\nBacked by Dell Technologies Capital and other leading investors, we're the only company that can bridge the gap between AI's promise and enterprise reality, making us perfectly positioned to define the agentic era for modern enterprises.\nOur diverse, collaborative team of 250+ innovators is solving category-defining challenges at the intersection of AI, process intelligence, and enterprise work. Diverse perspectives fuel breakthrough thinking, cross-functional collaboration is the norm, and our work directly transforms how Fortune 500 companies operate. We are shaping the future of work itself.\nABOUT THE ROLE\nBefore an agent can reliably operate software on a user's behalf, it needs to understand what should happen next and why: the underlying process, its steps, decision points and governing logic, branches, variations, dependencies, and long-tail edge cases.\nIn practice, this knowledge is rarely represented explicitly. It is distributed across messy event logs, unstructured documents, screen recordings, UI interaction traces, historical executions, and the tacit knowledge of domain experts.\nAs an Applied Scientist on this team, you will work across teams to develop process-agnostic, scalable learning systems that recover the latent structure, decision logic, and dynamics underlying complex process execution trajectories from sparse, noisy, highly correlated observations spanning substantial behavioral heterogeneity and organizational scale.\nYou will take heterogeneous data such as action logs, noisy screen traces, semi-structured documents, and variant-heavy workflows, and build systems that recover canonical process structure, meaningful variations, decision rules, dependencies, exceptions, and provenance. These representations are consumed by downstream agentic execution systems and must preserve enough detail for an agent to act correctly while remaining structured, compact, transparent, and auditable.\nThe same observational data and learned representations also contribute directly to our work on computer-use models for last-mile interaction with enterprise software.\nThe problems here rarely arrive with clean formulations. You may begin with a branch the system consistently fails to recover, two superficially different execution paths that should collapse into the same process, or an algorithm that works across thousands of traces but fails at millions.\nYour job is to turn those failures into tractable research problems: identify the right abstraction, formulate hypotheses, design evaluations that isolate the problem, and develop methods that solve it. Depending on the problem, this may draw from process and decision mining, graph algorithms, structured prediction, representation learning, information retrieval, program synthesis, model post-training and distillation, or agentic systems.\nWe care less about allegiance to a particular technique than about finding the simplest method that reliably recovers the underlying structure and scales.\nRESPONSIBILITIES\nProcess discovery and representation\nBuild pipelines that discover, extract, and formalize end-to-end business processes from event logs, UI interaction traces, screen recordings, and unstructured documents.\nDevelop algorithms for process variant detection, noise removal, and trace consolidation, turning noisy, high-cardinality action sequences into clean canonical representations.\nDesign representations that preserve the information downstream agents need while collapsing irrelevant execution-level variation.\nStudy the tradeoff between abstraction and fidelity: determine when superficially different trajectories represent the same underlying behavior and when small differences encode materially different decisions.\nBuild retrieval and grounding systems that incorporate SOPs, documents, and domain knowledge when process understanding requires evidence beyond execution traces.\nEvaluation and reliability\nDevelop rigorous evaluation frameworks for process discovery and extraction, including LLM-as-Judge pipelines, conformance checking against ground-truth processes, and regression suites that catch subtle degradation.\nDevelop evaluation methods for settings where there may not be a single canonical ground truth, including behaviorally equivalent process representations.\nEnumerate and systematically test exception paths, conditional branches, loops, rare variants, and the failure modes that naive extraction approaches miss.\nMake deliberate algorithmic tradeoffs so systems remain tractable across millions of traces and thousands of variants, including choices around data structures, approximation, indexing, and level of abstraction.\nDiagnose failures in production and turn recurring failure patterns into new algorithmic or research directions.\nCollaboration\nWork closely with the computer-use agents team so the representations you produce are expressed at the right level of abstraction, contain the necessary execution context, and are robust enough for downstream autonomous execution.\nWork across research, engineering, and product to move promising methods from prototype to production and measure their effect on downstream agent behavior.\nREQUIREMENTS\nAmbitious and Hungry. “Far and away the best prize that life offers is the chance to work hard at work worth doing.” - Theodore Roosevelt\nWe screen for comparable experience and strong fundamentals, not exact tool matches. Equivalent technologies are welcome.\nProblem Decomposition and Algorithmic Thinking. You can take a loosely defined problem, such as recovering a reliable process from messy action logs, decompose it into tractable sub-problems, reason carefully about edge cases, and arrive at an approach. You are comfortable reasoning from first principles about data structures, graph algorithms, approximation, indexing, and time and space complexity when algorithms must operate over millions of executions.\nML / DL Breadth. Strong working knowledge across representation learning, sequence modeling, clustering, structured prediction, information retrieval, generative modeling, and modern foundation-model adaptation. You have the judgment to recognize when a problem calls for a learned model, an algorithmic solution, an LLM-based system, or some combination of the three.\nCross-Domain Research Translation. You read research from adjacent areas including frontier AI, computer vision, language modelling, process mining, program synthesis, information retrieval, graph learning, and knowledge representation, and can judge whether it applies. You can identify the core idea inside a method, understand the assumptions that make it work, determine whether those assumptions hold in your setting, and adapt the idea into a working prototype.\nResearch Judgment. You can distinguish a difficult engineering problem from a research problem, design experiments that isolate the important variable, and abandon an elegant approach when the evidence suggests a simpler method works better.\nApplied Foundation Model Systems. Hands-on experience building reliable systems with modern language or multimodal models, including retrieval, structured generation, evaluation, grounding, and model adaptation. You understand the failure modes that arise when model outputs are used to recover structured process knowledge from noisy evidence.\nThis may include experience with:\nRetrieval design, embedding strategies, chunking, re-ranking, and grounding against source evidence.\nLLM-as-Judge systems, rubric design, calibration against human labels, and knowing when automated evaluation is insufficient.\nGetting hands-on experience with LLMs, RL, RLHF/RLAIF, post-training (SFT), evals, graders, synthetic data, model training and production ML systems.\nFaithfulness verification, constrained generation, confidence calibration, and methods for reducing unsupported model outputs.\nGuardrails, input and output validation, graceful degradation, and monitoring for drift in production.\nPython Proficiency. Strong software engineering in Python, experience with ML frameworks such as PyTorch, comfort with data processing at scale, and the ability to write clean, tested, production-quality code.\nCommunication. You can explain an algorithmic tradeoff to both a researcher and a product manager, and you write clear technical and design documents.\nPREFERRED EXPERIENCE\nA strong research track record, demonstrated through publications in top-tier machine learning, artificial intelligence, reinforcement learning, natural language processing, computer vision, or data mining venues.\nExperience translating research ideas from published work into production systems with measurable impact.\nProcess mining: event log analysis, process discovery algorithms, conformance checking, or variant analysis.\nInformation extraction from documents: NER, relation extraction, table extraction, or structured output generation from unstructured text.\nMultimodal data processing: extracting signals from screen recordings, UI screenshots, or interaction traces alongside textual data.\nFoundation-model adaptation, including supervised fine-tuning, LoRA, synthetic-data methods, distillation, reinforcement-learning-based post-training, and evaluation of resulting model behavior.\nYOU MIGHT BE A GREAT FIT IF YOU\nLook for the hidden structure inside messy observations before deciding what to build. You are excited by the possibility of discovering how a process actually works without being handed its rules in advance.\nEnjoy problems where the right abstraction, model, and formulation are not obvious. You are willing to move between algorithms, learned representations, graph structures, retrieval, and foundation models until the system can explain what it has observed.\nAre drawn to the idea of building an end-to-end, process-agnostic machine: one that can observe unfamiliar work, infer its underlying boundaries, steps and decision logic, distinguish meaningful variation from noise, and produce concretized knowledge that gives an agent the full state representation to successfully navigate complex real-world processes.\nRead deeply across fields and adapt ideas from process mining, program synthesis, representation learning, graph algorithms, information retrieval, multimodal learning, and agentic systems. You care about the underlying insight more than the category it came from.\nAre comfortable moving between theory and reality: papers, hypotheses, experiments, raw traces, screen recordings, production failures, and systems that must work at organizational scale.\nWant to work with a small, unusually ambitious team where research judgment and engineering execution are inseparable, and where your ideas can shape the architecture of an entirely new kind of system.\nSkan AI is an equal opportunity employer committed to building a diverse, inclusive, and respectful workplace around the world. We do not discriminate based on race, color, religion or belief, sex (including pregnancy, sexual orientation, gender identity, or gender expression), national origin, ancestry, age, disability, medical condition, genetic information, marital or family status, military or veteran status, or any other characteristic protected by applicable laws in the locations where we operate.\nWe welcome people from all backgrounds and provide reasonable accommod...","description_format":"text","description_chars":12036,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Workflow & Process Automation (RPA, BPM)","Process Mining","Computer Vision"],"lifecycle":[{"event":"open","at":"2026-10-23T18:29:16Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":2,"expected_fill_days":80,"reasons":["conf:0","velocity","win:early","comp:junior"],"computed_at":"2026-10-26T05:57:25Z"},"pay":null,"html_url":"https://alion.io/job/skan-ai-applied-scientist-i","json_url":"https://alion.io/job/skan-ai-applied-scientist-i.json","meta":{"generated_at":"2026-10-26T14:59:08Z","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":19851,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-27T00:00:00Z"}},"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":63247},"rest":"https://alion.io/mcp/rest/get_company?id=63247"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fskan-ai-applied-scientist-i"},{"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%2Fskan-ai-applied-scientist-i"},{"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%2Fskan-ai-applied-scientist-i"},{"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/skan-ai-applied-scientist-i\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fskan-ai-applied-scientist-i"}]}