Backend Engineer

• Design, build, and deploy AI systems, including LLMs, RAG, agentic workflows, and ML/DL solutions.
• Select appropriate models and architectures while balancing accuracy, latency, cost, and complexity.
• Train and fine-tune models using PyTorch or Keras, including data preparation and evaluation.
• Implement and benchmark research methods before adoption.
• Build APIs, inference pipelines, and backend integrations.
• Own evaluation metrics and track quality, latency, and cost.
• Monitor, debug, and optimize AI systems post-launch.
• Ensure production readiness and identify regressions before they impact users.
• Partner with software engineers and product teams to integrate AI into applications.
• Contribute to technical discussions and technical decision-making.
• Document architecture, implementation approaches, and key technical decisions.
• Read and evaluate ML research papers.
• Implement promising methods and reproduce results where applicable.
• Adapt emerging techniques into production-ready systems.
• Contribute to continued innovation across AI and engineering initiatives.
• Collaborate with engineering leadership, product teams, software engineers, backend developers, and global AI research and development teams.
• Deliver production-ready AI systems with measurable impact.
• Translate research into practical engineering solutions.
• Document and share technical knowledge for scalability.

AI & Backend Engineer

• Own the performance and scalability of backend systems and the underlying data layer
• Design request flows, synchronous versus asynchronous work, state placement, dependency behavior, and scalable service boundaries
• Diagnose latency and performance issues using traces, execution plans, metrics, and evidence
• Establish instrumentation, review standards, and regression gates to prevent customer-visible problems
• Design strategies for read scaling, write scaling, read/write separation, background processing, real-time updates, traffic spikes, high availability, reliability, search, data growth, safe changes, and API efficiency
• Review feature-team architecture proposals
• Write design documents and decision records for other engineers to build from
• Select and optimize relational, document, key-value, and in-memory data stores based on workload and access patterns
• Diagnose execution plans, index costs, lock contention, connection pooling, replica lag, partitioning, document aggregation, shard keys, hot partitions, cache behavior, and dual-write issues
• Diagnose latency across the full request path, including ORM-generated traffic, synchronous and ETL latency, and third-party integrations
• Own data-layer capacity planning and cost per request alongside latency
• Introduce design and query reviews for hot paths and data-layer changes
• Define standards for migrations, index justification, and work that must not run synchronously
• Instrument data access in application traces
• Contribute CI regression gates and production-realistic load testing
• Investigate performance for the largest customer accounts and per-tenant behavior
• Join the escalation on-call rota once established

Principal Backend Engineer, Performance, Database

• Design scientific coding tasks consisting of one main physics problem and at least 3 logically connected sub-problems
• Implement verified golden solutions in Python with complete unit test coverage
• Develop discriminative test cases that distinguish correct from incorrect AI-generated outputs
• Perform quality control checks using the Turing Central Task Platform (CTP), including Tier 1 structure checks and Tier 2 quality rubrics
• Revise scientific tasks and solutions based on QC feedback
• Optimize tasks to meet Pass@K evaluation criteria across multiple LLM judges, including GPT, Gemini, and Nemotron
• Validate scientific correctness, well-posedness, determinism, and expected outputs
• Maintain high first-submission quality and minimize rework
• Participate in reviews, feedback sessions, and project standups during required overlap hours

Physics, Python Specialist

• Design scientific coding tasks consisting of one main chemistry problem and at least 3 logically connected, progressively structured sub-problems.
• Develop verified golden solutions in Python with complete unit test coverage.
• Create discriminative test cases that distinguish correct from incorrect AI-generated outputs.
• Execute quality control validation through the Central Task Platform (CTP), including Tier 1 structural checks and Tier 2 quality rubrics.
• Iterate on task specifications, implementations, and test cases based on QC feedback.
• Optimize tasks to meet Pass@K evaluation criteria across multiple LLM judges, including GPT, Gemini, and Nemotron.
• Ensure scientific correctness, determinism, well-posedness, and adherence to project quality standards.
• Maintain high first-submission quality and minimize rework, targeting consistent L1 approval.
• Participate in review meetings, feedback sessions, and project standups during required overlap hours.

Scientific Chemistry, Python, AI Evaluation

• Author mathematical coding tasks containing one main problem and at least 3 logically connected sub-problems that progressively build toward the main solution
• Implement verified golden solutions in Python with complete unit test coverage
• Design discriminative test cases that distinguish correct from incorrect AI-generated outputs
• Perform quality control validation using the Central Task Platform (CTP), including Tier 1 structure checks and Tier 2 quality rubrics
• Refine problem specifications, solutions, and test cases based on QC feedback
• Optimize tasks to meet Pass@K evaluation criteria across multiple LLM judges, including GPT, Gemini, and Nemotron
• Ensure mathematical correctness, well-posedness, determinism, and compliance with project quality standards
• Maintain high first-submission quality and minimize rework, targeting consistent L1 approval
• Participate in review sessions, feedback meetings, and project standups during required overlap hours

AI Content Specialist, Mathematics, Python

• Design scientific coding tasks consisting of one main chemistry problem and at least 3 logically connected, progressively structured sub-problems
• Develop verified golden solutions in Python with complete unit test coverage
• Create discriminative test cases that distinguish correct from incorrect AI-generated outputs
• Execute quality control validation through the Central Task Platform (CTP), including Tier 1 structural checks and Tier 2 quality rubrics
• Iterate on task specifications, implementations, and test cases based on QC feedback
• Optimize tasks to meet Pass@K evaluation criteria across multiple LLM judges, including GPT, Gemini, and Nemotron
• Ensure scientific correctness, determinism, well-posedness, and adherence to project quality standards
• Maintain high first-submission quality and minimize rework, targeting consistent L1 approval
• Participate in review meetings, feedback sessions, and project standups during required overlap hours