Divisional AI Engineer III
Alliant Insurance ServicesLead the architecture and reliability of shared LLM infrastructure, while driving adoption across products. Build reusable orchestration, observability, and production services.
Open
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.
Machine Learning Engineer, Ranking and Retrieval
Build production machine learning systems for ranking, retrieval, classification, and personalization. Improve search relevance, model quality, and the reliability of AI applications.
Mali
United States
California$120,000 – $160,000 / year
RemoteFull-timeMiddleSeniorEnglish RequiredMachine Learning EngineerDescription
- Develop machine learning models for classification, ranking, retrieval, and personalization.
- Create efficient classifiers and improve their accuracy, precision, latency, and cost.
- Enhance search relevance and context retrieval with embeddings, hybrid search, and reranking.
- Create privacy-conscious personalization models that learn from user behavior.
- Build evaluation pipelines, labeled datasets, and metrics to assess model quality.
- Fine-tune and adapt existing models when enough data is available.
- Develop and maintain machine learning training, inference, and deployment pipelines.
- Troubleshoot production issues, track model performance, and deliver improvements based on real-world feedback.
- Work with engineering and product teams to deliver dependable machine learning features.
Requirements
- Prior experience in machine learning is required.
- Prior experience in artificial intelligence is required.
- Strong grounding in machine learning and modern neural network 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 abilities.
- Experience with embeddings, vector search, and ranking models.
- Experience operating 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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