{"id":2183520,"url":"https://alion.io/job/applied-computing-time-series-researcher-forward-deployed","title":"Time Series Researcher - Forward deployed","company":{"id":3872728,"name":"Applied Computing","domain":"appliedcomputing.com","url":"https://alion.io/company/appliedcomputing-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Teamtailor","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-14T06:34:16Z"}},"role":"Solutions","role_family":"Solutions","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Houston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":93000,"max_usd":200000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":399},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Interpretability","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-08-04T11:39:04Z","employer_posted_date":"2026-08-04","last_verified_at":"2026-10-14T08:57:36Z","board_verified":true,"closed_at":null,"days_open":70,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":70},"description":"About Applied Computing\nApplied Computing was founded in 2024 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable abundance for a growing planet.\nThe hydrocarbon industry keeps the world running. But its complexity has left operators tied to legacy systems, making critical decisions on less than 10% of available data.\nWe built Orbital to change that. It’s a foundation model built specifically for energy that lets companies use AI at scale, harnessing all of their operational data and optimising in real time for any metric. Decisions get faster, operations get safer, and carbon intensity falls.\nWe’ve raised over $32 million, including one of the largest seed rounds for an AI company in the UK. We’re just getting started.\nThe Role\nThe Time Series Researcher owns the core of Orbital’s temporal intelligence. This role exists to design, validate, and deploy foundational time-series models that operate under real world constraints: noisy sensors, partial observability, physical laws, and high economic stakes.\nThis is a customer facing role part of forward deployed team working with customer engieering teams to fine tune our model to their plants.\nThis is not offline research. You will own the full lifecycle; from theoretical formulation and experimentation to real-time inference, uncertainty estimation, and continuous retraining in production.\nWhat You’ll Own\nOrbital’s foundational time-series modelling stack\nPhysics-informed and probabilistic model design\nUncertainty quantification and robustness under sensor faults\nResearch → production translation for time-series models\nBenchmarking standards and validation protocols used across the company\nMust-Have Qualifications\nPhD in Computer Science, Statistics, Applied Mathematics, Physics, or related field\n\nFirst-author publications in time-series modelling, forecasting, signal processing, or physics-informed ML\n\n3+ years of hands-on research experience in time-series or sequence modelling\n\nDemonstrated experience in Deep Learning & Probabilistic modelling\n\nExpert Python skills with production-grade PyTorch code\n\nExperience deploying ML models into real systems\n\nHow We Work\nResearch is judged by production impact, not paper count\nWe value principled models that survive contact with reality\nWe iterate aggressively, benchmark honestly, and ship responsibly\nPhysics, statistics, and learning are treated as complementary, not competing\nWhat This Role Is Not\nNot offline academic research disconnected from deployment\nNot pure deep-learning experimentation without domain grounding\nNot feature engineering on static datasets\nNot a support role; this position owns core IP\n1. Design & Implement Foundational Time Series Models\nDesign core time-series architectures supporting:\no Forecasting\no Classification / anomaly detection\no Optimisation & control-adjacent tasks\nExplore and select appropriate objectives examples:\no Probabilistic losses\no Generative formulations\no Reinforcement-learning-inspired objectives where appropriate\nDevelop hybrid approaches that blend:\no Classical statistical models\no Deep learning architectures\no Physics-based constraints\n2. Embed Physics-Informed Structure\nIntegrate domain physics into learning systems, including:\no Conservation laws\no Process constraints\no Differential-equation-based priors\n\nImprove generalisation, interpretability, and extrapolation beyond training regimes\n\nEnsure models respect physical feasibility in production settings\n\n3. Uncertainty, Robustness & Reliability\nDesign uncertainty-aware models (Bayesian, ensemble, hybrid)\nQuantify confidence under:\no Sensor drift and failure\no Regime change\no Sparse or delayed ground truth\nEnsure outputs are usable by operations and engineering teams, not just statistically\nelegant.\n4. Production Structured AI Code\nContainerise and deploy models using Docker on AWS / Azure (EKS, ECS, SageMaker)\nBuild or integrate CI/CD pipelines for:\no Training\no Evaluation\no Rollout and rollback\no Automated retraining triggers\n5. Benchmarking & Validation\nDefine rigorous back-testing and evaluation protocols\nBuild automated benchmarking pipelines across datasets, regimes, and failure modes\nCompare against classical baselines and modern deep-learning approaches\nEnsure claims are defensible to customers, partners, and internal stakeholders\nWhat Success Looks Like\nFirst 90 Days\nDeep understanding of Orbital’s data, domains, and production constraints\nContribution to at least one core time-series model or experimental track\nClear ownership of a modelling problem with defined success metrics\n6-12 Months\nOne or more foundational models running reliably in production\nDemonstrable improvements in:\no Forecast accuracy\no Robustness under faults\no Uncertainty calibration\nModels actively used by downstream agents and optimisation layers\nBenchmarking standards adopted across the research team","description_format":"text","description_chars":4991,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Chemical Manufacturing","Petroleum & Mining Engineering"],"lifecycle":[{"event":"open","at":"2026-10-09T15:33:49Z"}],"visa":[],"liveness":{"score":10,"band":"cold","label":"Long shot","p_open":1,"p_active":0.359,"p_room":0.28,"age_days":70,"expected_fill_days":19,"reasons":["conf:3","win:tail","crowd:"],"computed_at":"2026-10-14T06:34:16Z"},"pay":null,"html_url":"https://alion.io/job/applied-computing-time-series-researcher-forward-deployed","json_url":"https://alion.io/job/applied-computing-time-series-researcher-forward-deployed.json","meta":{"generated_at":"2026-10-14T10:07:33Z","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":11631,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-15T00:00:00Z"}},"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":3872728},"rest":"https://alion.io/mcp/rest/get_company?id=3872728"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fapplied-computing-time-series-researcher-forward-deployed"},{"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%2Fapplied-computing-time-series-researcher-forward-deployed"},{"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%2Fapplied-computing-time-series-researcher-forward-deployed"},{"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/applied-computing-time-series-researcher-forward-deployed\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fapplied-computing-time-series-researcher-forward-deployed"}]}