Role overview Svitla Systems Inc. is looking for a Physics-Informed Machine Learning Engineer for a full-time position (40 hours per week) in Ukraine. Our client is a technology startup.
The team is building a Physics-Informed Foundational Model to understand GPU and compute health. They derive physics-grounded stress signals: effective-stress proxies, semiconductor degradation estimates, and dynamical mathematical features, and use them to assess hardware health over time. The feature pipeline runs end-to-end. You’ll build the model and the fusion layer on top of it. You will own the modeling: designing the fusion layer (how physics-based and dynamical features combine into a coherent health signal) and the temporal modeling layer (a physics-informed model, PINN-style, where empirical stress signals drive part of the loss and a physics-based degradation model informs another part). The exact formulation of the physics term is still evolving; you’ll be involved in shaping it. You’ll work as part of a small, technical team alongside the founder and other domain experts. Requirements * Experience 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). * Strong understanding of time-series/sequence modeling (LSTM, temporal CNN, transformers, or state-space models) on sensor or telemetry data. * Understanding of parameter calibration / inverse problem: fitting mechanistic model parameters to noisy observational data (Bayesian calibration, MLE, or optimization-based). * Expert 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. * Expertise 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.
Nice to have * Experience in reliability engineering/PHM (prognostics and health management) background: RUL estimation, degradation modeling, accelerated-life testing. * Exposure to semiconductor or hardware degradation physics at a “read the literature critically” level. * Familiarity with nonlinear dynamics/recurrence or dynamical-systems features (e.g., RQA or comparable techniques). * Familiarity with hardware/datacenter telemetry or fleet analytics. * Experience working in small teams alongside domain scientists/mathematicians; comfortable turning research feedback into production code.
Responsibilities * Build 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. * Design 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. * Calibrate 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. * Harden 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. * Write clear analysis docs and defend modeling choices to technical stakeholders and clients.
We offer * US and EU projects based on advanced technologies. * Competitive compensation based on skills and experience. * Regular performance appraisals to support your growth. * Flexibility in workspace, either remote or in one of our development offices. * Comprehensive medical insurance, including dental and massages. * Personalized learning program tailored to your interests and skill development. * Sport reimbursement program for onsite and online activities. * Bonuses for recommendations of new employees. * Bonuses for article writing, public talks, and other activities. * 20 vacation days, 10 national holidays and 5 sick leaves. * Maternity leave policy and family days off. * Free tech webinars and meetups organized by Svitla. * Welcome and anniversary presents, gifts for children, and more. * Regular corporate events and meetups. * Awesome team, friendly and supportive community!
Why join Svitla Svitla Systems is a global digital solutions company headquartered in the U.S. and operating across the Americas, Europe, Asia, and APAC. Since 2003, we have served a wide range of clients — from innovative start-ups to Fortune 500 companies. Our success is built on partnership. By integrating seamlessly with clients’ teams, we create lasting collaborations that drive real results. We are strong advocates of workplace flexibility, remote culture, individual approach to professional and personal growth.
Our global mission is to build a business that contributes to wellbeing of our partners, personnel, and their families, improves our communities, and makes a lasting difference in the world. Together, we are coding a brighter tomorrow — and living it.
