{"id":2290234,"url":"https://alion.io/job/luvu-inc-founding-ml-engineer-3","title":"Founding ML Engineer","company":{"id":4398972,"name":"Basis","domain":"withbasis.co","url":"https://alion.io/company/withbasis-2","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":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":136000,"max_usd":262000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":413},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"C++","optional":false},{"name":"Claude Code","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Google Bigtable","optional":false},{"name":"Keras","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Rust","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"closed","first_seen_at":"2026-06-14T20:24:30Z","employer_posted_date":"2026-06-14","last_verified_at":"2026-10-11T22:45:02Z","board_verified":false,"closed_at":"2026-10-11T22:45:02Z","days_open":119,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":119},"description":"About Electrokare\nElectrokare builds foundation models for human physiology.\nWe develop ai models for a subset of human health use cases, starting with models that estimate blood-level physiology from cardiovascular signals, enabling real-time insight without lab tests or invasive sensors. Our systems are already in production, backed by proprietary datasets, and used across high-performance and medical-adjacent settings.\nThe Role\nThis is a core applied ML role focused on building and improving our foundation models.\nYou will sit at the intersection of signal processing, data science, and deep learning, working directly with the CTO to develop models that are already live and evolving toward even better performance. We see this as a founding level role with opportunity to own the AI work.\nThe role is roughly 50% applied research and 50% production ML. You’ll read and dissect papers, design experiments, train and evaluate models, and then deploy them into real systems.\nWhat You'll Do\nDesign, train, and improve ML and deep-learning models\n\nWork with large proprietary physiological datasets and real-world signal noise\n\nApply signal processing techniques alongside modern deep-learning approaches\n\nRead, evaluate, and implement ideas from current scientific literature\n\nMove models from research to production (training → validation → deployment)\n\nCollaborate closely with the CTO on model architecture, evaluation, and roadmap\n\nBuild tooling for experimentation, validation, and performance tracking\n\nCommunicate results clearly through metrics, visualizations, and reports\n\nTechnical Requirements:\nMachine Learning & Data\nDeep learning with TensorFlow/Keras and/or PyTorch\n\nStrong foundation in signal processing and time-series analysis\n\nSolid data science fundamentals and statistical reasoning\n\nProgramming\nProficiency in Python\n\nExperience with one or more of: C++, Rust, R, C\n\nAbility to read and work across multiple languages as needed\n\nInfrastructure & Deployment\nModel deployment using Docker and Kubernetes\n\nCloud experience (GCP and/or Azure)\n\nWorking knowledge of databases (SQL, MongoDB, Bigtable, etc.)\n\nCommunication\nClear data visualization and reporting\n\nAbility to explain complex models and results to technical and non-technical teammates\n\nAI Tools\nComfortable using modern AI-assisted development tools (e.g., Claude Code, CodeX, similar)\n\nUses tools to move faster, not to replace judgment\n\nResearch\nStrong ability to read, understand, and critically evaluate scientific papers\n\nExperience implementing methods from literature, not just using libraries\n\nBackground in applied research, with publications strongly preferred\n\nIdeal Background\nMS or PhD in machine learning, computer science, electrical engineering, applied math, biomedical engineering, or related field\n\n3-7 years of experience\n\nPrior work on physiological signals, biosignals, medical ML, or similar domains is a strong plus\n\nExperience working in environments where models are deployed and used, not just published\n\nCulture Fit\nLow ego, high ownership\n\nYou like being useful\n\nYou care about doing things well, even when no one’s watching\n\nYou’re comfortable jumping in wherever needed\n\nYou care about correctness, not just accuracy metrics\n\nYou’re comfortable working with messy real-world data\n\nYou’re curious and skeptical in the right ways\n\nYou want your models to matter in practice\n\nYou work VERY 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