Senior Machine Learning Engineer – Implementation, Scale

Trivelta

Senior ML Engineer focused on designing and optimizing ML models for Trivelta's gaming technology. Collaborating with engineers to integrate models into user products while maximizing performance.

Posted 6/9/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

ATS Keywords

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Hard Skills

Machine LearningDeep LearningTransformersGradient BoostingPythonC++GoPyTorchTensorFlowJAX

Soft Skills

Collaboration

Tools & Technologies

SQLSparkPineconeMilvusWeights & BiasesMLflowKubeflow

Certifications & Qualifications

MS in Computer SciencePhD in Computer ScienceMS in MathematicsPhD in Mathematics

Industry Keywords

End-to-End Pipeline DevelopmentData CentricityExploratory Data AnalysisModel OptimizationModel DeploymentFeature Engineering

Tech Stack

Tools & technologies

GoPythonPyTorchSparkSQLTensorflow

About the role

Key responsibilities & impact

Model Implementation: Design, train, and fine-tune state-of-the-art ML models (Deep Learning, Transformers, Gradient Boosting, etc.) specifically optimized for our internal datasets.
End-to-End Pipeline Development: Build and maintain robust data pipelines and training workflows to ensure reproducible and scalable model development.
Optimization & Performance: Profile and optimize model latency and throughput for production environments.
Data Centricity: Perform deep exploratory data analysis (EDA) to identify biases, signal-to-noise ratios, and feature engineering opportunities within our unique data silos.
Collaboration: Work closely with Data Engineers to streamline data ingestion and Backend Engineers to integrate model APIs into our user-facing products.

Requirements

What you’ll need

5+ years of professional experience in Machine Learning or Software Engineering, with at least 3 years focused on deploying models to production.
Expert-level Python (and ideally C++ or Go for performance-critical components).
Deep fluency in PyTorch, TensorFlow, or JAX.
Experience with SQL, Spark, and vector databases (e.g., Pinecone, Milvus).
Familiarity with Weights & Biases, MLflow, Kubeflow, or similar orchestration tools.
Strong understanding of linear algebra, calculus, and statistics as applied to ML optimization.
MS or PhD in Computer Science, Mathematics, or a related field (or equivalent 'battle-tested' industry experience).

Benefits

Comp & perks

Health insurance
Remote work options