Machine Learning Engineer
liquid xrLos Angeles (CA)
About the role
Liquid XRis hiring a Machine Learning Engineer to build advanced models that uncover meaningful signals frommultimodal time-seriessensor data. This hybrid role inLos Angeles, CAfocuses onrobust real-time algorithmsdesigned fornoisy, high-frequency inputs, with an emphasis on taking models from research to production under latency and compute constraints.
What you’ll do:
Design and implementmachine learning modelsfor time-series and sequential data.
Develop algorithms to extractstructured signalsandlatent variablesfrom noisy sensor inputs.
Build and optimizereal-time inference pipelinesthat respectlatencyandcompute constraints.
Work onmulti-modal learningandsensor fusion.
Replace or augmentclassical signal processingpipelines with learned models.
Create training strategies forwindowed and streaming data.
Develop training approaches forweakly labeledorpartially observeddatasets.
Design and evaluatemulti-task learningsetups.
Evaluate models usingstatistical metricsandapplication-driven performance criteria.
Collaborate with cross-functional teams to move models fromresearch to production.
Explore and apply sequence model architectures includingTemporal convolutional networks (TCNs) , RNNs / LSTMs / GRUs, andTransformer-based sequence models.
What you bringStrong experiencewith machine learning fortime-series data.
Experience withtransfer learningandknowledge distillation.
Proficiency inPythonandPyTorch(or similar frameworks).
Solid understanding ofsignal processing fundamentalsincluding filtering, noise, and the frequency domain.
Experience working withreal-world, noisy datasets.
Experience building or deployinglow-latency / real-time systems.
Experience with sensor data such asIMUs.
Familiarity with sensor fusion methods such asKalman filtersandprobabilistic models.
Experience withmulti-modalormulti-task learning.
Exposure toembeddedoredge deployment constraints.
Background in applied domains involvingphysical systemsorhuman data.
Abilityto 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 ownershipfrom 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-orientedand committed to high-quality, well-documented work.
Ownership mindsetwith full accountability from concept to completion.
Benefits:
Employee stock option program.
Health care benefits(currently, gold PPO coverage with Blue Shield ) plusdental and vision, starting within30 daysof employment.
Open PTOcompany policy.
Role details:
Full-timeemployee position, workingremotelyor in ourLos Angeles office.
Compensation will becommensurate with experienceand competitive with the market.
Occasional travelmay be required domestically and internationally.
Technologies:
Python, PyTorch, RNNs, LSTMs, GRUs, Transformer-based sequence models, Temporal convolutional networks (TCNs), Kalman filters.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
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Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
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