Please submit your resume in English and indicate your level of English. At Toloka, we connect smart, curious people from around the world with freelance online tasks that train and improve artificial intelligence. What we do The Toloka Annotators connects individuals with Generative AI projects from leading tech innovators. Our mission is to unlock the full potential of AI by involving real people from around the world in the development process. About the Role Annotation is what helps AI make sense of the world. As an annotator, you may be invited to take part in online projects such as rating AI-generated content, evaluating factual accuracy, or comparing responses - when projects are available. Responsibilities: - Carefully review provided data (text, images, or videos) - Label or classify content based on project guidelines - Identify and flag factually incorrect, sensitive, inappropriate, or unclear material Important note: This is project-based work. Tasks are available only when projects are active. You may be invited to one or more projects depending on your profile and current opportunities. Each project has its own compensation level based on scope and expertise required. On this project, AI trainers earn up to $23 per hour equivalent.

Please submit your resume in English and indicate your level of English. At Toloka, we connect smart, curious people from around the world with freelance online tasks that train and improve artificial intelligence. What we do The Toloka Annotators connects individuals with Generative AI projects from leading tech innovators. Our mission is to unlock the full potential of AI by involving real people from around the world in the development process. About the Role Annotation is what helps AI make sense of the world. As an annotator, you may be invited to take part in online projects such as rating AI-generated content, evaluating factual accuracy, or comparing responses - when projects are available. Responsibilities: - Carefully review provided data (text, images, or videos) - Label or classify content based on project guidelines - Identify and flag factually incorrect, sensitive, inappropriate, or unclear material Important note: This is project-based work. Tasks are available only when projects are active. You may be invited to one or more projects depending on your profile and current opportunities. Each project has its own compensation level based on scope and expertise required. On this project, AI trainers earn up to $23 per hour equivalent.

We are looking for a Principal Engineer who will sit at the technical center of our AI platform—owning architecture, standards, and production excellence across the stack. This is not a pure research role, nor a model R&D position. You will lead the productionization of GenAI systems—owning evaluation pipelines, tracing and observability, inference orchestration, safety guardrails, dataset/prompt workflows, and developer-facing tooling. You will translate ambiguous product problems into reliable, scalable platform capabilities that customers trust with production workloads. You will operate as a force multiplier—setting architectural standards, mentoring senior engineers, influencing roadmap trade-offs, and partnering directly with product and executive leadership to shape technical direction. Required Experience - 10+ years of software engineering experience with recent hands-on coding - Experience operating at Principal / Distinguished / Staff / Lead Engineer level (or equivalent while still coding) - Proven ownership of production GenAI systems (RAG, eval frameworks, inference services, safety/guardrails, tracing/observability) - Deep expertise in Python - Strong practical capability in TypeScript, React, and Node.js - Strong cloud and platform experience (AWS/GCP/Azure, Kubernetes, CI/CD, distributed systems) - Experience driving multi-team architecture decisions - Ability to communicate trade-offs clearly to senior leadership Strong Plus - RAG evaluation frameworks (ragas, custom eval harnesses) - Vector databases (Pinecone, Weaviate, Milvus, Qdrant) - OpenTelemetry instrumentation - Performance optimization of inference systems - Exposure to blockchain/tamper-evidence concepts - Own technical vision and architecture across GenAI platform capabilities - Lead end-to-end productionization of AI features (not prototypes) - Define and operate readiness criteria for AI releases (eval gates, rollout strategy, rollback mechanisms, SLOs) - Design and own core platform capabilities: - Evaluation pipelines - Tracing and observability systems - Prompt/version/dataset workflows - Guardrails and safety systems - Inference orchestration layers - Establish and track metrics for latency (p95/p99), cost efficiency, quality, and safety - Write clear architectural proposals that influence multi-team decisions - Partner with Product to translate ambiguous user pain into staged technical delivery - Raise the engineering bar through reviews, documentation, mentoring, and principled system design - Contribute hands-on across Python services, Node/TypeScript APIs, and React UI when necessary - Join at the ground floor of a GenAI infrastructure company defining a new category - Shape the technical foundation before the architecture calcifies - Work directly with senior product and engineering leadership - Build production systems that AI teams depend on daily - Fully remote - ESOP equity - Flexible hours - Generous PTO - Global offsites - Education support - Clear advancement path

We are looking for a Principal Engineer – AI/ML Platform who will own the architecture, productionization, and operational excellence of our machine learning and LLM infrastructure.This is not a research scientist role. You will define how GenAI systems are evaluated, deployed, monitored, governed, and continuously improved at scale. You will shape standards across model integration, evaluation frameworks, inference systems, safety mechanisms, telemetry instrumentation, and AI/ML workflow automation. You will operate at the intersection of AI engineering, distributed systems, and platform architecture—partnering closely with Product and Engineering leadership to ensure our AI systems are reliable, observable, safe, and economically scalable in enterprise production environments. Required Experience - 10+ years of software engineering experience with significant recent hands-on AI/ML work - Proven ownership of production AI/ML or LLM systems at scale (not research or prototypes) - Deep expertise in LLM productionization (RAG, finetuning, evaluation, guardrails, model monitoring) - Strong Python expertise - Experience with modern AI frameworks (PyTorch, TensorFlow, JAX, Scikit-learn) - Hands-on AI/MLOps experience (CI/CD for ML, deployment automation, experiment tracking, monitoring) - Experience with cloud platforms (AWS/GCP/Azure), Kubernetes, and distributed systems - Experience implementing evaluation pipelines and observability instrumentation - Demonstrated technical leadership influencing multi-team architectural direction Strong Plus - Experience with ML workflow orchestration platforms (Kubeflow, MLflow, Vertex AI, SageMaker) - Expertise in model governance, bias evaluation, compliance, and drift detection - Domain expertise in NLP, agentic systems, recommender systems, or similar applied AI areas - Open-source AI/ML contributions - Master’s or PhD in ML/AI-related field - Define and own architecture for scalable AI/ML and LLM systems, including: - Inference pipelines - Evaluation frameworks - Model lifecycle workflows - Monitoring and observability systems - Translate ambiguous business requirements into robust AI platform designs and staged delivery plans - Make strategic decisions on: - Model integrations and gateways - Retrieval-augmented generation (RAG) approaches - Evaluation methodologies - Safety and guardrail systems - Establish standards for model readiness, evaluation gates, rollout/rollback mechanisms, and drift detection - Build and deploy production-grade LLM capabilities integrated into distributed systems with clear SLOs and telemetry - Design scalable AI/MLOps and AIOps practices across training, testing, deployment, and monitoring - Improve data pipelines, feature workflows, and lineage processes supporting model evaluation and inference - Instrument tracing and model observability using OpenTelemetry and modern telemetry standards - Own evaluation pipelines tracking latency, cost, accuracy, hallucination rates, and prompt/version drift - Provide clear trade-off analyses balancing model performance, cost efficiency, safety, and maintainability - Write structured technical proposals that guide executive investment and roadmap decisions - Mentor engineers in AI productionization, experimentation discipline, and distributed systems design - Raise the engineering bar through principled reviews, documentation, and mechanism-driven standards - Shape the AI production architecture of a category-defining GenAI infrastructure company - Define how enterprise-grade AI systems are observed, evaluated, and remediated - Build mechanisms that scale beyond individual engineers - Influence roadmap and platform strategy at a formative stage - Fully remote - ESOP equity - Flexible hours - Generous PTO - Global offsites - Education support - Clear advancement opportunities