Machine Learning Engineer

Liquid XR is hiring a Machine Learning Engineer to build advanced models that uncover meaningful signals from multimodal time-series sensor data. This hybrid role in Los Angeles, CA focuses on robust real-time algorithms designed for noisy, high-frequency inputs , with an emphasis on taking models from research to production under latency and compute constraints. What you’ll do Design and implement machine learning models for time-series and sequential data. Develop algorithms to extract structured signals and latent variables from noisy sensor inputs. Build and optimize real-time inference pipelines that respect latency and compute constraints . Work on multi-modal learning and sensor fusion . Replace or augment classical signal processing pipelines with learned models. Create training strategies for windowed and streaming data . Develop training approaches for weakly labeled or partially observed datasets. Design and evaluate multi-task learning setups. Evaluate models using statistical metrics and application-driven performance criteria . Collaborate with cross-functional teams to move models from research to production . Explore and apply sequence model architectures including Temporal convolutional networks (TCNs) , RNNs / LSTMs / GRUs , and Transformer-based sequence models . What you bring Strong experience with machine learning for time-series data . Experience with transfer learning and knowledge distillation . Proficiency in Python and PyTorch (or similar frameworks). Solid understanding of signal processing fundamentals including filtering, noise, and the frequency domain. Experience working with real-world, noisy datasets . Experience building or deploying low-latency / real-time systems . Experience with sensor data such as IMUs . Familiarity with sensor fusion methods such as Kalman filters and probabilistic models . Experience with multi-modal or multi-task learning . Exposure to embedded or edge deployment constraints . Background in applied domains involving physical systems or human data . Ability to reason about temporal structure, causality, and latency. Strong intuition for modeling tradeoffs versus deployment constraints. Comfort working with imperfect, real-world data. End-to-end ownership from modeling to validation to deployment. BSc or MSc in quantitative fields (examples: computer science, engineering, physics, applied math). Team-oriented mindset and clear communication across cross-functional teams. Proactive, adaptable, resilient approach; ability to manage multiple priorities. Detail-oriented and committed to high-quality, well-documented work. Ownership mindset with full accountability from concept to completion. Benefits Employee stock option program . Health care benefits (currently, gold PPO coverage with Blue Shield ) plus dental and vision , starting within 30 days of employment. Open PTO company policy. Role details Full-time employee position, working remotely or in our Los Angeles office . Compensation will be commensurate with experience and competitive with the market. Occasional travel may be required domestically and internationally. Technologies: Python, PyTorch, RNNs, LSTMs, GRUs, Transformer-based sequence models, Temporal convolutional networks (TCNs), Kalman filters. #J-18808-Ljbffr Liquid XR