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Parallel Partners
Parallel Partners
1 – 10 Employees
B2BFintech
Parallel Partners recruits information technology professionals for direct-hire roles across financial services and trading. Its searches span international hedge funds, proprietary trading firms, investment banks, online brokerages, and trading software vendors, with particular expertise in high-frequency trading systems, algorithmic trading, financial engineering, and risk management. Founded in 1995 and based in Chicago, the staffing firm became part of Next Step Systems in 2014.
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Applied Machine Learning Engineer

Build production machine learning systems for classification, retrieval, ranking, and personalization. Develop reliable models, evaluation pipelines, and deployment infrastructure.

MLMali
$120,000 – $160,000 / year
RemoteFull-timeMiddleSeniorEnglish RequiredMachine Learning Engineer
PythonPyTorch

Description

  • Build and refine machine learning models for classification, ranking, retrieval, and personalization.
  • Create efficient classifiers and improve model accuracy, precision, latency, and cost.
  • Enhance search relevance and context retrieval with embeddings, hybrid search, and reranking.
  • Develop privacy-conscious personalization models that learn from user behavior.
  • Create evaluation pipelines, labeled datasets, and metrics to assess model quality.
  • Fine-tune and adapt existing models when sufficient data is available.
  • Build and maintain machine learning training, inference, and deployment pipelines.
  • Troubleshoot production issues, monitor model performance, and deliver improvements based on real-world feedback.
  • Work with engineering and product teams to deliver reliable ML-powered features.

Requirements

  • Experience in machine learning is required.
  • Experience in artificial intelligence is required.
  • Strong grounding in machine learning and modern neural architectures.
  • Practical experience building and deploying machine learning models in production.
  • Experience with classification, ranking, retrieval, recommendation systems, or personalization.
  • Knowledge of model evaluation, experimentation, and performance metrics.
  • Ability to write production-quality code and work with real-world datasets.
  • Ability to balance model quality, latency, cost, and reliability.
  • Strong ownership and problem-solving skills.
  • Experience with embeddings, vector search, and ranking models.
  • Experience with production machine learning systems and inference pipelines.
  • Proficiency in Python and PyTorch or JAX.
  • Willingness to complete a 60-minute coding assessment.

Benefits

  • Medical insurance
  • Dental insurance
  • Vision insurance
  • Savings plan options
  • Paid time off

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