The **Junior Computer Vision Engineer** builds, evaluates, and helps deploy computer vision (CV) models and supporting software components that enable products to “see” and understand images and video. This role contributes to the end-to-end CV lifecycle—data preparation, experimentation, model training, evaluation, and integration—under guidance from senior engineers or applied scientists.
The **Junior Autonomous Systems Engineer** builds and validates software components that enable machines or software agents to perceive their environment, make decisions, and act safely with minimal human intervention. This role contributes to an autonomy stack (e.g., perception, localization, planning, control, or orchestration) and supports the engineering practices required to deliver reliable autonomous behavior in production-like environments.
The **Junior Applied AI Engineer** is an early-career individual contributor who helps design, build, test, and ship machine learning–enabled features into production software systems under the guidance of senior engineers and applied scientists. The role focuses on **applied implementation**: turning validated modeling approaches into reliable, observable services, pipelines, and product experiences.
A **Junior AI Safety Engineer** supports the safe, reliable, and policy-compliant development and deployment of machine learning (ML) and generative AI (GenAI) systems by implementing safety evaluations, mitigations, and monitoring controls within engineering workflows. The role focuses on practical engineering work: building and running test harnesses, creating safety checks in CI/CD, helping triage safety incidents, and partnering with senior safety engineers, applied scientists, and product teams to reduce harmful or non-compliant model behaviors.
A Junior AI Platform Engineer builds, operates, and improves the internal platform capabilities that enable data scientists and ML engineers to train, evaluate, deploy, and monitor machine learning models reliably. This role focuses on implementing well-defined platform components (CI/CD, model packaging, infrastructure-as-code modules, deployment templates, observability hooks) and supporting day-to-day platform operations under the guidance of senior engineers.
The Junior AI Evaluation Engineer designs, runs, and maintains repeatable evaluation processes that measure the quality, safety, and reliability of AI/ML systems—especially modern LLM-enabled features—before and after release. The role focuses on turning ambiguous “is it good?” questions into measurable metrics, representative test sets, and automated evaluation pipelines that product and engineering teams can trust.
The **Junior AI Engineer** is an early-career individual contributor in the **AI & ML** department who helps design, build, test, and support machine learning (ML) and AI components that ship inside software products and internal platforms. The role focuses on implementing well-scoped model improvements, data/feature preparation, experimentation, and production hardening under the guidance of senior AI/ML engineers and data scientists.
The **Junior AI Agent Engineer** designs, implements, and iterates on AI “agents” that can plan, call tools, retrieve knowledge, and complete workflows reliably within a software product or internal platform. This role focuses on building **production-grade agent behavior** (prompting, tool interfaces, retrieval pipelines, evaluation harnesses, and guardrails) under the guidance of senior engineers and applied ML leads.
The **Generative AI Engineer** designs, builds, and operates production-grade generative AI capabilities—typically large language model (LLM) applications, retrieval-augmented generation (RAG) systems, and agentic workflows—integrated into customer-facing products and internal platforms. The role balances applied ML engineering with software engineering rigor, focusing on reliability, security, cost efficiency, evaluation, and measurable business outcomes rather than experimentation alone.
The Federated Learning Engineer designs, builds, and operates privacy-preserving machine learning systems that train models across distributed data sources without centralizing sensitive data. This role exists in software and IT organizations that need to learn from data located on user devices, customer environments, partner organizations, or regulated data stores where direct pooling is constrained by privacy, security, contractual, or residency requirements. The business value is enabling higher-quality models, broader data coverage, and faster model iteration while reducing privacy risk and improving compliance posture.
The **Edge AI Engineer** designs, optimizes, and deploys machine learning inference capabilities to run reliably on **resource-constrained edge environments** such as mobile devices, embedded systems, IoT gateways, industrial PCs, retail kiosks, and on-prem appliances. The role bridges applied ML engineering and systems engineering: it turns trained models into **production-grade, measurable, secure, and maintainable** edge inference solutions.
The **Distinguished Responsible AI Engineer** is a top-tier individual contributor who defines and operationalizes responsible AI (RAI) engineering standards across AI/ML products and platforms, ensuring models are **safe, fair, reliable, privacy-preserving, secure, and compliant** throughout their lifecycle. The role combines deep engineering expertise with governance design, advanced evaluation methods, and cross-functional leadership to enable the organization to scale AI capabilities without unacceptable risk.
The **Distinguished Machine Learning Engineer** is a top-tier individual contributor (IC) responsible for setting the technical direction and engineering standards for production-grade machine learning (ML) systems across an organization. This role designs and evolves the end-to-end ML engineering ecosystem—spanning data/feature pipelines, model development, deployment, observability, reliability, and governance—while delivering material business outcomes through scalable, secure, and maintainable ML capabilities.
The **Distinguished LLM Engineer** is a top-tier individual contributor (IC) role responsible for architecting, proving, and operationalizing large language model (LLM) capabilities that measurably improve product value, developer velocity, and business outcomes. This role combines deep hands-on engineering with organization-wide technical leadership—setting standards for model quality, evaluation, safety, performance, and cost efficiency across LLM-powered systems.
The **Distinguished Generative AI Engineer** is a top-tier individual contributor responsible for designing, scaling, and governing generative AI capabilities that become durable, reusable assets across a software company or IT organization. This role blends deep technical execution with enterprise architecture influence—turning rapidly evolving GenAI techniques into reliable, secure, cost-effective products and platforms.
The **Distinguished Applied AI Engineer** is a top-tier individual contributor (IC) who designs, proves, and scales applied AI capabilities that materially move company outcomes—product performance, revenue, customer retention, reliability, and cost efficiency—while raising the engineering and scientific bar across the organization. This role bridges advanced machine learning with production-grade software engineering, turning ambiguous business goals into repeatable AI systems that ship safely, operate reliably, and improve over time.
The **Distinguished AI Engineer** is a top-tier individual contributor (IC) engineering role responsible for **enterprise-scale technical direction and delivery of AI/ML systems** that materially shape the company’s products, platforms, and operating model. This role combines deep hands-on engineering capability with cross-organization technical leadership to ensure AI solutions are **reliable, secure, cost-effective, governable, and production-grade**.
A **Computer Vision Engineer** designs, trains, evaluates, and deploys vision-based machine learning systems that interpret images and video to power product capabilities (e.g., detection, segmentation, tracking, OCR, image understanding, and multimodal experiences). The role combines applied ML engineering with strong software practices to move models from experimentation into reliable, scalable production.
The **Autonomous Systems Safety Engineer** ensures that autonomy-enabled products (e.g., robotic platforms, autonomous agents, autonomy SDKs, or decision-making services) are designed, verified, and operated with **demonstrable, auditable safety assurances** appropriate to their operational context. This role translates safety intent into actionable engineering requirements, verification evidence, runtime guardrails, and release gates—especially where machine learning and probabilistic behavior complicate traditional assurance methods.
The **Autonomous Systems Engineer** designs, builds, and operationalizes software components that enable systems to perceive, decide, and act with minimal human intervention—reliably, safely, and measurably. In a software company or IT organization, this role typically sits within **AI & ML Engineering** and bridges ML models with real-time systems engineering to deliver autonomy capabilities into products, platforms, or internal operational tooling.
The **Associate Robotics Software Engineer** designs, implements, tests, and supports software components that enable robots and robotic systems to perceive, plan, and act reliably in real-world environments. This role sits at the intersection of **robotics engineering** and **AI/ML-enabled autonomy**, typically contributing to production-grade codebases (often ROS/ROS 2-based), simulation workflows, and on-robot integration under the guidance of senior engineers.
The **Associate Responsible AI Engineer** helps ensure that AI-enabled products and platforms are designed, built, evaluated, and operated in ways that are **safe, fair, privacy-preserving, transparent, and compliant**. This role combines practical software engineering with applied responsible AI methods—implementing evaluation pipelines, integrating guardrails into ML/LLM systems, and supporting governance evidence for releases.
The **Associate Recommendation Systems Engineer** designs, builds, evaluates, and operationalizes components of recommendation systems that personalize user experiences (e.g., “recommended for you,” “similar items,” ranking feeds, related content, and next-best-action suggestions). At the associate level, the role focuses on implementing well-scoped features, models, and data pipelines under guidance from senior engineers, while developing strong fundamentals in machine learning for ranking and retrieval, experimentation, and production ML practices.
The **Associate RAG Engineer** builds and improves retrieval‑augmented generation (RAG) capabilities that connect large language models (LLMs) to trusted enterprise knowledge (documents, tickets, product data, policies) to produce accurate, grounded answers. This role focuses on implementing retrieval pipelines, preparing and indexing content, evaluating answer quality, and supporting productionization under guidance of senior engineers.
The **Associate NLP Engineer** builds, evaluates, and improves natural language processing (NLP) capabilities that power user-facing features and internal AI workflows (e.g., classification, extraction, semantic search, summarization, conversational experiences, and retrieval-augmented generation). The role focuses on implementing well-defined solutions under guidance, turning research or prototype concepts into reliable components that can be tested, shipped, and monitored in production.
The **Associate MLOps Engineer** supports the reliable deployment, monitoring, and ongoing operations of machine learning (ML) models and ML-enabled services in production. This role focuses on implementing and maintaining the “last mile” systems that connect data science work to secure, observable, and scalable runtime environments—typically through CI/CD automation, containerization, orchestration, and standardized ML lifecycle tooling.
The **Associate Machine Learning Engineer** builds, tests, and operationalizes machine learning components that power software products and internal platforms. This role sits at the intersection of software engineering and applied machine learning, contributing production-ready code, reproducible experiments, and reliable model deployment workflows under the guidance of senior ML engineers and data science leaders.
The **Associate LLM Engineer** builds and improves application features powered by large language models (LLMs), focusing on safe, reliable, and measurable behavior in production. This role contributes to LLM-enabled services such as retrieval-augmented generation (RAG), summarization, classification, extraction, agentic workflows, and conversational interfaces—typically under the guidance of more senior LLM/ML engineers.
The Associate Knowledge Graph Engineer designs, builds, and maintains foundational knowledge graph assets—schemas, pipelines, entity resolution logic, and query interfaces—that connect enterprise data into a semantically consistent graph for AI and ML use cases. This role focuses on delivering reliable graph-ready datasets, improving graph data quality, and enabling downstream applications such as semantic search, recommendations, analytics, and emerging LLM-powered experiences.
The **Associate Generative AI Engineer** builds and improves production-grade generative AI capabilities—typically LLM-powered features such as search augmentation (RAG), summarization, chat assistants, classification/extraction, and agent-like workflows—under the guidance of senior engineers and ML leaders. The role focuses on implementing well-scoped components (prompting, retrieval pipelines, evaluation harnesses, API integration, guardrails, and observability) that make generative AI features reliable, secure, and cost-effective in real software products.