Parallel Partners

AI Engineer

• Build and operate backend systems that serve AI-powered insurance workflows in production
• Design and implement AI orchestration layers connecting models, APIs, workflows, and business logic
• Build inference pipelines for LLM-based and AI-assisted automation systems
• Optimize latency, throughput, and cost across AI services through caching, batching, streaming, and routing
• Design stable service boundaries between backend systems, ML components, and product APIs
• Implement observability through logging, metrics, tracing, alerting, and incident response workflows
• Debug production issues across distributed AI systems and resolve root causes
• Collaborate with frontend, product, operations, and ML teams to ship end-to-end features
• Continuously improve system reliability, scalability, and performance
• Deliver reliable, low-latency, highly available AI backend systems at scale
• Ensure AI workflows are stable, observable, and production-ready across multiple products
• Improve system performance through real-world feedback and optimization
• Detect, diagnose, and resolve production incidents quickly
• Integrate AI capabilities into global customer and internal workflows

AI Backend Engineer, Artificial Intelligence

• Build AI-powered product features integrated into real insurance workflows
• Design and optimize LLM-based interactions across customer and internal systems
• Develop prompts, reasoning flows, and structured AI outputs for production use cases
• Evaluate model performance using real-world data, feedback loops, and experiments
• Improve AI output reliability through guardrails, fallback logic, and validation layers
• Work with backend engineers to integrate AI capabilities into APIs and workflows
• Design and implement AI-driven automation features such as chat, recommendations, and decision support
• Analyze user behavior and model responses to continuously improve AI performance
• Collaborate with product, design, and engineering teams to ship end-to-end AI features
• Contribute to AI system design decisions focused on usability and real-world impact
• Deliver reliable, consistent, production-ready AI features across workflows
• Improve user-facing AI interactions and task completion rates
• Ensure model outputs are measurable, testable, and continuously improving
• Reduce manual work in insurance processes through AI-driven automation
• Enable product teams to safely deploy AI features with controlled behavior

Applied AI Engineer, Artificial Intelligence, Work From Home

• Build and ship iOS features using Swift
• Create clean mobile flows for insurance, payments, claims, renewals and financial services
• Work with product and design to simplify complex user journeys
• Integrate backend APIs and ensure mobile flows are stable and reliable
• Improve performance, crash rate, loading states, responsiveness, memory usage and battery efficiency
• Use analytics, user behavior and production issues to improve the product
• Build AI-assisted mobile experiences where they improve the user journey

iOS Software Engineer, Artificial Intelligence, Remote

• Build and maintain production-grade Android applications using Kotlin
• Design and implement intuitive, responsive mobile interfaces for insurance, payments, and customer workflows
• Integrate backend APIs for quotations, policy management, claims, and transactional services
• Contribute to mobile features incorporating AI-assisted capabilities, including chat experiences, recommendations, and automation flows
• Optimize application performance, responsiveness, battery usage, and memory efficiency across devices
• Implement analytics, crash reporting, and user behavior tracking
• Collaborate with backend, product, design, and AI/ML teams throughout the development lifecycle
• Ensure stability, scalability, and security across production mobile systems
• Improve engineering standards, architecture, and mobile best practices
• Deliver stable, high-performance Android experiences used at scale
• Reduce crashes and production incidents through better engineering practices
• Ship mobile features with strong system design and testing discipline
• Enable AI-assisted product capabilities through mobile integration
• Contribute to scalable and maintainable mobile architecture

Android Software Engineer, Artificial Intelligence

Platform Engineer

• Build and operate the Machine Learning (ML) infrastructure and platforms powering Artificial Intelligence (AI) products
• Design systems for model training, evaluation, deployment, inference, and experimentation
• Build and optimize model serving and inference infrastructure for high-throughput and low-latency workloads
• Improve reliability, scalability, latency, and cost efficiency of Artificial Intelligence (AI) systems
• Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement
• Build platforms and tooling that enable Artificial Intelligence (AI) engineers and researchers to experiment, evaluate, and ship models faster
• Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions
• Build production observability, monitoring, tracing, and alerting for Artificial Intelligence (AI)/Machine Learning (ML) workloads
• Identify bottlenecks across the Machine Learning (ML) stack and continuously improve system performance
• Work closely with Artificial Intelligence (AI) engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure
• Ensure AI infrastructure reliably supports production workloads at scale
• Ensure models can be trained, evaluated, deployed, and improved efficiently
• Ensure inference systems deliver strong latency, throughput, reliability, and cost efficiency
• Ensure Machine Learning (ML) pipelines are reproducible, observable, maintainable, and robust
• Detect and diagnose model and infrastructure regressions quickly
• Create reusable Machine Learning (ML) infrastructure platform primitives
• Enable the AI stack to evolve rapidly as new models, architectures, and inference techniques emerge

Machine Learning Platform Engineer, Machine Learning, Artificial Intelligence

Trader

• Develop new trading strategies utilizing proprietary software
• Customize current trading strategies
• Trade across global futures, equities, commodities, options, fixed income, and cryptocurrencies marketplaces
• Participate in a culture emphasizing teamwork, continuous integration, and test-driven development

Experienced Trader

• Define end-to-end requirements for AI capabilities, from model behavior to system behavior to user experience
• Translate model capabilities, evaluation results, and technical constraints into clear product and system decisions
• Make trade-offs across quality, latency, cost, reliability, safety, and UX
• Work closely with ML, backend, and client engineers on system design, evaluation, and iteration
• Define and evolve evaluation frameworks across offline metrics, online experiments, and human feedback
• Establish clear quality bars and feedback loops for AI-powered experiences
• Drive execution through clear specifications, prioritization, and strong technical judgment
• Identify failure modes and ensure AI systems behave predictably and recover gracefully
• Own product quality end-to-end, including correctness, reliability, usefulness, and user trust
• Align AI product strategy with user needs and company priorities
• Deliver understandable, predictable, and trusted AI features
• Clarify roadmaps and priorities and iterate based on real user feedback
• Align teams to execute AI product goals with minimal friction