{"id":1597666,"url":"https://alion.io/job/svitla-systems-physics-informed-machine-learning-engineer-3","title":"Physics-Informed Machine Learning Engineer","company":{"id":2216,"name":"Svitla Systems","domain":"svitla.com","url":"https://alion.io/company/svitla-systems","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":78,"open_postings":8,"ghost_share":0,"stale_share":0.875,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Argentina"],"countries":["AR"],"hiring_countries":["AR"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-08-06T09:29:03Z","employer_posted_date":"2026-08-06","last_verified_at":"2026-10-30T20:59:55Z","board_verified":true,"closed_at":null,"days_open":85,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":85},"description":"Svitla Systems Inc. is looking for a Physics-Informed Machine Learning Engineer for a full-time position (40 hours per week) in Argentina.\nRequirements\nExperience in building and training physics-informed models - a physics-based term in the loss function of a real project (PINN, physics-regularized NN, or equivalent). \nStrong understanding of time-series/sequence modeling (LSTM, temporal CNN, transformers, or state-space models) on sensor or telemetry data. \nUnderstanding of parameter calibration / inverse problem: fitting mechanistic model parameters to noisy observational data (Bayesian calibration, MLE, or optimization-based). \nExpert knowledge of Python scientific stack (Pandas, NumPy, scikit-learn, PyTorch or JAX) and be comfortable owning a data pipeline end to end, including data-quality investigation. \nExpertise in reading and reasoning about physics/reliability equations governing degradation; you don't need to derive them, but they can't be a black box. \nNice to have\nExperience in reliability engineering/PHM (prognostics and health management) background: RUL estimation, degradation modeling, accelerated-life testing. \nExposure to semiconductor or hardware degradation physics at a \"read the literature critically\" level. \nFamiliarity with nonlinear dynamics/recurrence or dynamical-systems features (e.g., RQA or comparable techniques). \nFamiliarity with hardware/datacenter telemetry or fleet analytics. \nExperience working in small teams alongside domain scientists/mathematicians; comfortable turning research feedback into production code. \nResponsibilities\nBuild the temporal model: design and train a physics-informed sequence model (e.g., LSTM or similar temporal architecture) for degradation and health prediction, incorporating a physics-based loss term alongside the data-driven loss. \nDesign the fusion layer: define how physics-based stress features, dynamical/mathematical features, and other signals combine into model inputs and a defensible health score, replacing today's simple hand-set weighting. \nCalibrate the physics-informed components: the stress-proxy parameters are currently engineering priors. You'll help design and execute calibration strategies against whatever outcome labels are available. \nHarden the feature pipeline: the pipeline is Python/Pandas over time-aligned multi-sensor telemetry; you'll extend and maintain it (feature audits, label engineering, data-quality gates) as modeling needs dictate. \nWrite clear analysis docs and defend modeling choices to technical stakeholders and clients.","description_format":"text","description_chars":2562,"description_truncated":false,"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":[{"name":"Argentina","iso":"AR","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["DevOps & Platform Engineering","IT Outsourcing & Dedicated Teams","Custom Software Development"],"lifecycle":[{"event":"open","at":"2026-10-01T18:15:47Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"Filed H-1B visas in the last two years","filings_12m":0,"filings_prev_12m":1,"green_card_filings_12m":0,"median_offered_wage_usd":null,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":0}],"liveness":{"score":19,"band":"cold","label":"Long shot","p_open":1,"p_active":0.531,"p_room":0.36,"age_days":84,"expected_fill_days":44,"reasons":["conf:2","velocity","win:tail","crowd:"],"computed_at":"2026-10-30T05:48:23Z"},"pay":null,"html_url":"https://alion.io/job/svitla-systems-physics-informed-machine-learning-engineer-3","json_url":"https://alion.io/job/svitla-systems-physics-informed-machine-learning-engineer-3.json","meta":{"generated_at":"2026-10-31T00:27:03Z","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":702,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-01T00:00:00Z"}},"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":2216},"rest":"https://alion.io/mcp/rest/get_company?id=2216"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsvitla-systems-physics-informed-machine-learning-engineer-3"},{"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%2Fsvitla-systems-physics-informed-machine-learning-engineer-3"},{"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%2Fsvitla-systems-physics-informed-machine-learning-engineer-3"},{"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/svitla-systems-physics-informed-machine-learning-engineer-3\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsvitla-systems-physics-informed-machine-learning-engineer-3"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":1597666}},{"tool":"search_jobs","arguments":{"company":"svitla.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":1597666},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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