Introduction In 2026, code review tools have become indispensable for teams working with modern software development methodologies like Agile and DevOps. As software applications grow more complex and collaboration becomes a key factor in maintaining quality, these tools streamline the process of reviewing and maintaining code. Code reviews help ensure that code adheres to industry…
The **Principal Quantum Research Scientist** is a senior individual-contributor (IC) research leader who defines and executes an applied quantum research agenda that can be translated into software capabilities, developer experiences, and differentiated product outcomes for a software or IT organization. The role blends deep expertise in quantum information science with strong software engineering judgment to create algorithms, error-mitigation techniques, simulation methods, and benchmarking frameworks that are credible in the research community and viable in real systems.
The **Lead Quantum Research Scientist** is a senior, research-heavy individual contributor (IC) who sets the technical direction for quantum algorithms and applied quantum research that can be translated into software products, developer tools, or client-facing solutions. This role bridges **foundational research** (quantum information science, algorithm design, error models) with **engineering execution** (prototype code, benchmarks, integration into quantum software stacks).
The **Principal Data Scientist** is the most senior individual-contributor (IC) data science role in the Scientist family, accountable for defining and delivering high-impact, production-grade machine learning and statistical solutions that materially improve product performance, customer outcomes, and business efficiency. This role combines deep modeling expertise with strong product and engineering judgment, setting technical direction across multiple problem spaces and mentoring the broader data science community.
The **Lead Data Scientist** is a senior, hands-on scientific and technical leader responsible for turning data into measurable product and business outcomes through high-quality modeling, experimentation, and decision intelligence. This role owns end-to-end problem framing, model development, validation, and productionization in partnership with engineering, product, and business stakeholders, while setting standards for methodology, quality, and responsible AI across the Data & Analytics function.
The **Data Scientist** turns data into reliable insights, decisions, and predictive capabilities that improve product performance, customer outcomes, and operational efficiency. In a software or IT organization, this role exists to bridge product strategy, engineering execution, and measurable business impact by applying statistical analysis, experimentation, and machine learning in a production-aware way. The business value is realized through improved conversion and retention, reduced risk and cost, better personalization, and faster learning cycles via rigorous measurement.
The **Principal Digital Twin Scientist** is a senior individual-contributor scientist who designs, validates, and operationalizes **digital twin models**—computational representations of real-world systems—by combining simulation, data assimilation, and machine learning to produce decision-grade predictions. The role sits at the intersection of **AI, physics-based modeling, and production software engineering**, and is accountable for scientific rigor, model trustworthiness, and measurable impact on product outcomes.
The **Senior Robotics Research Scientist** advances the company’s robotics intelligence capabilities by inventing, validating, and transferring novel algorithms and learning-based methods into usable software components for real-world or simulated robots. The role blends deep research rigor (hypothesis-driven experimentation, publication-quality evaluation) with engineering pragmatism (reproducible code, measurable performance, integration-ready deliverables).
The **Senior Machine Learning Scientist** is a senior individual contributor responsible for designing, validating, and productionizing machine learning solutions that materially improve product capabilities and business outcomes. The role blends deep applied ML expertise with rigorous scientific method, strong software engineering habits, and pragmatic delivery in a modern software/IT operating environment.
The **Principal Robotics Research Scientist** is a senior individual-contributor research leader responsible for inventing, validating, and transferring state-of-the-art robotics and embodied AI capabilities into production-grade software and platforms. This role defines research direction, leads high-impact technical programs, and turns novel algorithms into reliable, measurable improvements in real-world robot performance.
The **Principal Research Scientist** is a senior individual-contributor (IC) research leader in the **AI & ML** organization of a software or IT company. The role exists to **create differentiated, production-relevant AI innovations**—advancing the state of the art while translating research into capabilities that improve product quality, platform performance, customer outcomes, and business growth.
The Principal Machine Learning Scientist is a senior individual contributor (IC) who sets technical direction for machine learning (ML) and applied research efforts, turning ambiguous business and product opportunities into scalable, measurable ML capabilities. This role leads end-to-end model strategy—from problem framing and experimental design through production evaluation, monitoring, and iteration—while ensuring quality, reliability, and responsible AI practices.
The **Principal AI Research Scientist** is a senior individual-contributor research leader responsible for inventing, validating, and transferring state-of-the-art AI/ML methods into product-grade capabilities for a software or IT organization. This role combines deep technical research rigor with practical engineering judgment to ensure innovations are not only novel but also deployable, safe, and measurable in real-world systems.
The **Machine Learning Scientist** is an individual contributor (IC) role in the **Scientist** job family within the **AI & ML** department, responsible for designing, validating, and improving machine learning approaches that solve measurable product and platform problems. This role translates ambiguous business or user needs into rigorous modeling hypotheses, experiments, and model artifacts that can be productionized with ML engineering and platform teams.
The **Lead NLP Scientist** is a senior applied research and product-facing science role responsible for designing, validating, and operationalizing Natural Language Processing (NLP) and Large Language Model (LLM) capabilities that power customer-facing software features and internal AI platforms. The role blends hands-on model development with technical leadership—setting scientific direction, raising engineering rigor in experimentation, and ensuring that NLP solutions meet product, reliability, privacy, and responsible AI expectations.
The Lead Machine Learning Scientist is a senior individual-contributor (IC) scientific leader responsible for turning ambiguous product and business problems into measurable machine learning (ML) outcomes, and for guiding the design, development, validation, and iteration of ML models that operate reliably in production. This role blends deep applied ML expertise with technical leadership: setting scientific direction for a problem area, raising the technical bar across the team, and ensuring model quality, safety, and business impact.
The **Associate Responsible AI Scientist** supports the design, evaluation, and continuous improvement of machine learning (ML) and generative AI systems to ensure they are **fair, reliable, transparent, privacy-preserving, secure, and aligned with company policy and applicable regulation**. This is an early-career applied science role that combines **measurement (metrics and testing), technical analysis (data/model behaviors), and governance-ready documentation** to help teams ship AI features responsibly.
The **Associate Machine Learning Scientist** is an early-career individual contributor who helps design, prototype, evaluate, and incrementally improve machine learning (ML) models that power product features, internal platforms, or analytics capabilities. The role focuses on **problem framing, experimentation, model development, and measurement**, with increasing responsibility for reproducible research and production-aware modeling practices.
The Senior Project Manager leads complex, cross-functional software and IT initiatives from initiation through delivery, ensuring outcomes are achieved on time, within agreed scope, and with transparent risk and dependency management. This role operates as the “delivery integrator” across engineering, product, security, operations, and business stakeholders—turning strategic intent into an executable plan and measurable results.
The Senior Delivery Manager is accountable for reliably delivering complex software and IT initiatives from commitment through release and adoption, balancing scope, schedule, quality, risk, and stakeholder outcomes. This role orchestrates cross-functional teams (engineering, QA, product, platform/DevOps, security, and operations) to meet business objectives while improving delivery predictability and execution maturity.
The Release Manager is accountable for planning, coordinating, and governing the end-to-end release of software changes into production (and other controlled environments) in a predictable, low-risk, and business-aligned way. This role orchestrates the “last mile” of delivery across engineering, QA, infrastructure, security, and business stakeholders—ensuring that releases are ready, authorized, communicated, executed, and validated.
The Delivery Manager is accountable for turning approved product and technology work into predictable, high-quality outcomes by orchestrating people, process, and delivery governance across one or more cross-functional teams. This role ensures delivery commitments are realistic, risks are surfaced early, dependencies are actively managed, and stakeholders receive timely, evidence-based updates on progress, scope, and trade-offs.
A **Technical Program Manager (TPM)** drives end-to-end delivery of complex, cross-functional technology programs that span multiple engineering teams and business stakeholders. The role blends **program management rigor** (planning, risk management, governance, dependency orchestration) with enough **technical depth** to understand architecture, delivery constraints, and operational realities.
The **Senior Technical Program Manager (Senior TPM)** is accountable for planning, orchestrating, and delivering complex, cross-functional technical programs that span multiple engineering teams, systems, and stakeholders. The role blends rigorous program management with technical fluency to manage dependencies, risks, and trade-offs across architecture, security, reliability, and delivery timelines.
The **Senior Program Manager** is accountable for planning, orchestrating, and delivering complex, cross-functional programs that span multiple teams, workstreams, and systems within a software or IT organization. This role converts strategic intent into executable plans, ensures delivery predictability, manages dependencies and risk, and drives alignment across engineering, product, security, operations, and business stakeholders.
The **Senior Engineering Program Manager (Senior EPM)** orchestrates complex, multi-team engineering programs to deliver measurable business outcomes—on time, with predictable scope, quality, and risk management. This role ensures engineering execution aligns with product strategy, platform reliability, and operational constraints by building program structure, decision cadence, and transparent reporting across stakeholders.
The Program Manager is accountable for delivering a coordinated set of interrelated initiatives (a program) that together achieve measurable business outcomes across technology, product, and operations. This role plans and drives execution across multiple teams, aligns stakeholders on scope and priorities, manages dependencies and risks, and ensures delivery predictability and quality.
The **Principal Technical Program Manager (Principal TPM)** is a senior, highly autonomous individual contributor who leads **large-scale, technically complex, cross-organizational programs** that are critical to product delivery, platform reliability, security posture, and/or strategic modernization. This role exists to connect strategy to execution across multiple engineering and business teams, ensuring that the most important initiatives land predictably, safely, and with measurable business outcomes.
The Principal Program Manager is a senior individual-contributor (IC) program leader responsible for orchestrating complex, cross-functional initiatives that span multiple engineering teams, product areas, and operational functions. This role converts strategic objectives into executable program plans, manages dependencies and risk, and ensures delivery outcomes are achieved with predictable timelines and clear accountability.
The **Principal Engineering Program Manager (EPM)** is a senior, high-impact individual contributor who plans, orchestrates, and delivers complex, cross-team engineering programs that are critical to product delivery, platform reliability, security posture, and operational scalability. This role translates strategic engineering priorities into executable, measurable programs, aligning multiple engineering teams and stakeholders to deliver outcomes on time, with predictable quality and controlled risk.