Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! The Data Platform team at Figma builds and operates the foundational systems that power analytics, AI/ML, and data-driven decision-making across the company. We serve a diverse set of stakeholders, including AI researchers, machine learning engineers, data scientists, product engineers, and business teams that rely on data for insights and strategy. Our team owns and scales critical platforms such as the Snowflake data warehouse, ML Datalake, orchestration and pipeline infrastructure, and large-scale data ingestion and processing systems, managing all data flowing into and out of these platforms. Despite being a small team, we take on high-scale, high-impact challenges. In the coming years, we're focused on building the data infrastructure layer for Figma's AI-powered products, driving cost and performance optimizations across our data stack, scaling our ingestion and reverse ETL capabilities for new product use cases, and strengthening data quality, reliability, and compliance at every layer. If you're passionate about building scalable, high-performance data platforms that empower teams across Figma, we'd love to hear from you! This is a full-time role that can be held from one of our US hubs or remotely in the United States. What you'll do at Figma: - Design and build large-scale distributed data systems that power analytics, AI/ML, and business intelligence across Figma. - Develop batch and streaming solutions to ensure data is reliable, efficient, and scalable across the company. - Manage and evolve core platforms like Snowflake, our ML Datalake, orchestration infrastructure, and real-time ingestion systems. - Improve data reliability, consistency, and compliance, ensuring high-quality data for engineering, research, and business stakeholders. - Identify and drive cost optimization opportunities across data processing, compute infrastructure, and storage. - Collaborate with AI researchers, data scientists, product engineers, and business teams to understand data needs and build scalable solutions. - Drive technical decisions and best practices for data ingestion, orchestration, processing, and storage. - Mentor and support engineers, fostering a culture of learning and technical excellence. We’d love to hear from you if you have: - 5+ years of backend or infrastructure engineering experience, including designing and building distributed data infrastructure at scale. - Strong expertise in batch and streaming data processing technologies such as Spark, Flink, Kafka, or Airflow/Dagster. - Proven track record of impact-driven problem-solving in fast-paced environments, with a strong focus on high-quality, reliable, and performant systems. - Excellent technical communication skills, with experience collaborating across both technical and non-technical stakeholders. - Experience mentoring engineers and fostering a culture of learning and technical excellence. While it’s not required, it’s an added plus if you also have: - Familiarity with our stack, including Golang, Python, SQL, frameworks such as dbt, and technologies like Spark, Kafka, Snowflake, and Dagster. - Experience building data infrastructure for AI/ML pipelines, including model serving, feature stores, or dataset compliance. - Experience with reverse ETL, personalization platforms, or real-time event ingestion systems. - Experience with data governance, access control, and cost optimization strategies for large-scale data platforms. - The ability to navigate ambiguity, take ownership, and drive projects from inception to execution. At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you're excited about this role but your past experience doesn't align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Pay Transparency Disclosure If based in Figma’s San Francisco or New York hub offices, this role has the annual base salary range stated below. Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. The listed range is a guideline, and the range for this role may be modified. For roles that are available to be filled remotely, the pay range is localized according to employee work location by a factor of between 80% and 100% of range. Please discuss your specific work location with your recruiter for more information. Figma offers equity to employees, as well a competitive package of additional benefits, including health, dental & vision, retirement with company contribution, parental leave & reproductive or family planning support, mental health & wellness benefits, generous PTO, company recharge days, a learning & development stipend, a work from home stipend, and cell phone reimbursement. Figma also offers sales incentive pay for most sales roles and an annual bonus plan for eligible non-sales roles. Figma’s compensation and benefits are subject to change and may be modified in the future. Annual Base Salary Range: $153,000—$376,000 USD At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com. These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities. Examples of accommodations include but are not limited to: - Holding interviews in an accessible location - Enabling closed captioning on video conferencing - Ensuring all written communication be compatible with screen readers - Changing the mode or format of interviews To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding. By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice.

Join our network of independent alteration professionals specializing in prom and special event dresses. What You'll Do • Alter prom dresses, homecoming dresses, pageant gowns, and quinceañera dresses (hemming, bodice/waist, straps, cups, zippers) • Work with structured formal dresses (multiple layers, sequins, beading, linings, tulle) • Conduct fittings, take precise measurements, and communicate timelines clearly • Deliver high-quality finishing and maintain an organized workspace suitable for fittings We also receive requests for wedding dresses, bridesmaid dresses, evening gowns, suits, and other formalwear. Requirements • Experience with formalwear and/or special event dress alterations • Strong garment construction knowledge + precision measuring • Machine + hand sewing proficiency • Professional communication and customer service Benefits / Highlights • Work from home (independent contractor role) • Local client requests provided through the platform • Flexibility to accept the jobs that fit your schedule • Opportunity to grow repeat clients and reviews through your profile

About the team The Dev Accelerator team builds and operates our internal developer platform: the tools, libraries, and infrastructure that let 100+ engineers design, build, test, and ship products quickly and safely. We own: - Developer CLI tooling – a “Prompt-to-Product” style CLI that scaffolds services end-to-end, environment/credentials tooling, and a wide range of internal developer scripts. - Monorepo build, lint, and CI infrastructure, including our core backend CI workflow, Bazel integration, and a custom linting framework and rule set used across the codebase. - Go ecosystem stewardship for a large suite of shared libraries and applications under our main Go module. - Python shared libraries & frameworks for core business logic, gRPC helpers, utilities, and standardized component libraries (datastores, messaging, web, etc.). - Engineering effectiveness & test infrastructure, including automated canary analysis, test data and coverage tooling, bad test dashboards, dependency analysis, and “broken main” automation. Our goal is to make the easy path the right path for product teams and AI agents building on the platform. About the role As a Senior Software Engineer, Dev Accelerator, you will: - Design and evolve the internal developer platform that underpins virtually all backend development. - Own high-leverage projects across CLI tooling, CI/CD, shared libraries, and test infrastructure. - Work across Python, Go, Bazel, Kubernetes, AWS, and CI systems to make our Golden Path fast, reliable, and intuitive. - Shape abstractions and workflows that are AI-native, consumable by both humans and AI agents. You’ll operate as a senior IC, leading multi-quarter initiatives and collaborating with product teams, infrastructure, SRE, and Security. What you’ll do - Build and evolve developer CLI tooling - Extend our primary developer CLI to scaffold new component types and services end-to-end (service manifests, container/build configs, deployment charts, build files, API definitions, starter code, alerts, runbooks). - Improve environment and credentials tooling to make local development setup fast and reliable. - Own core CI/CD and linting infrastructure - Design and maintain the backend CI workflow used by backend services. - Evolve linting, formatting, and typing tools to enforce architectural and code-quality guardrails across the monorepo. - Debug and fix CI issues that block engineers, and proactively reduce flakiness and runtime. - Steward shared Go and Python ecosystems - Own key shared Go libraries (auth, caching, clients, configuration, cryptography, logging, metrics, domain/realm, server, etc.) and their usage across many applications. - Maintain and evolve Python shared libraries and frameworks in our core libraries, gRPC helpers, utilities, and standardized components. - Strengthen test and release safety - Extend automated canary analysis with new metric types, backtesting, and safer defaults. - Build and improve test automation tooling, bad-test detection dashboards, and dependency-analysis utilities to keep main green and tests reliable. - Contribute to automation that classifies CI failures and summarizes them for engineers (including LLM-assisted workflows). - Drive platform-level design and abstractions - Design abstractions that balance simplicity for product engineers with enough power for advanced use cases. - Collaborate with PM/TPM, infrastructure, and product teams to scope and deliver multi-team initiatives (e.g., prompt-to-product workflows, typing and linting initiatives, test automation). Required technical skills Languages & frameworks - Strong experience with Python 3.x: - CLI development (e.g., Click or similar). - YAML/Jinja2-style templating. - Modern type hints and typing discipline (e.g., typing, dataclasses / attrs, Pydantic-style patterns). - Testing with pytest or similar. - Solid experience with Go: - Shared library and service development (gRPC/HTTP). - CLI patterns (e.g., Cobra/Viper or equivalents). - Testing with Go testing frameworks (e.g., Ginkgo/Gomega or the standard library). Protocols & APIs - Protobuf/gRPC: - Schema design and evolution. - Cross-language client/server generation and integration. Build systems & infrastructure - Bazel in a large monorepo: - BUILD rules and dependency management. - Working with code generation for APIs and clients. - Containers & orchestration: - Docker image builds. - Kubernetes concepts (Helm-style values, service deployments, readiness/liveness/health checks). - Cloud infrastructure (AWS preferred): - Experience with a meaningful subset of: object storage, relational databases (e.g., Postgres), key–value/document stores, search, streaming/ingest services, Kafka, Redis, IAM. CI/CD & DevOps - Modern CI systems (e.g., Git-based CI/CD platforms): - Authoring non-trivial pipelines (matrix builds, reusable workflows, secrets/permissions). - Infrastructure-as-Code mindset: - Comfortable working with service manifests (YAML), Terraform/Terragrunt-like patterns, or internal equivalents. - Linting & static analysis: - Hands-on experience configuring and tuning linting and typing tools (e.g., pylint, ruff, mypy, golangci-lint). - Experience writing or extending custom lint rules is a strong plus. Strongly preferred - Monorepo experience - Worked in a large, shared codebase with complex dependency graphs and shared frameworks. - Familiar with dependency graph analysis and strategies to keep builds/tests fast. - Developer experience (DX) tooling - Built or maintained CLI tools, scaffolding systems, or internal frameworks used by other engineers. - Thoughtful about ergonomics, documentation, and guardrails. - Testing & progressive delivery - Exposure to canary analysis / progressive rollout systems (e.g., Prometheus/PromQL, Grafana, automated deployment checks). - Experience with test data management, integration/E2E test infrastructure, or bad-test detection. - Messaging & streaming - Experience with Kafka (topic design, producers/consumers, observability, error handling). - LLM/AI integration - Experience or strong interest in using LLMs to improve developer workflows (e.g., failure summarization, smart code generation, AI-native CLIs). How you work We’re looking for someone who: - Designs scalable developer abstractions - Has built tools that many engineers depend on, and can point to where those abstractions worked (or didn’t) and why. - Operates across the full platform stack - Comfortable jumping between Python CLIs, Go libraries, Bazel rules, YAML manifests, CI workflows, and Kubernetes deployments. - Reduces complexity for others - Measures success by how much simpler and safer life becomes for product engineers and AI agents using the platform. - Thinks in terms of leverage - Prefers building guardrails, automation, and self-service over manual ops work. - Writes clear documentation - Treats usage and developer documentation as part of the product surface, not an afterthought. - Collaborates across teams - Communicates clearly with product teams, infra, SRE, and security; comfortable gathering feedback via Slack, ticketing systems, and design docs and iterating quickly. If you’re excited about building the tools and infrastructure that hundreds of engineers (and AI agents) will use every day, we’d love to talk. #LI-AJ1 Actual compensation will be determined based on several non-discriminatory factors including skills, experience, qualifications, and geographic location. In addition to base salary, this role may be eligible for bonus or incentive compensation, equity, and a comprehensive benefits package. Base salary range: $179,800—$258,500 USD Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here. If you would like more information on your EEO rights under the law, please click here.

About the team The Dev Accelerator team builds and operates our internal developer platform: the tools, libraries, and infrastructure that let 100+ engineers design, build, test, and ship products quickly and safely. We own: - Developer CLI tooling – a “Prompt-to-Product” style CLI that scaffolds services end-to-end, environment/credentials tooling, and a wide range of internal developer scripts. - Monorepo build, lint, and CI infrastructure, including our core backend CI workflow, Bazel integration, and a custom linting framework and rule set used across the codebase. - Go ecosystem stewardship for a large suite of shared libraries and applications under our main Go module. - Python shared libraries & frameworks for core business logic, gRPC helpers, utilities, and standardized component libraries (datastores, messaging, web, etc.). - Engineering effectiveness & test infrastructure, including automated canary analysis, test data and coverage tooling, bad test dashboards, dependency analysis, and “broken main” automation. Our goal is to make the easy path the right path for product teams and AI agents building on the platform. About the role As a Software Engineer 2, Dev Accelerator, you will: - Design and evolve the internal developer platform that underpins virtually all backend development. - Own high-leverage projects across CLI tooling, CI/CD, shared libraries, and test infrastructure. - Work across Python, Go, Bazel, Kubernetes, AWS, and CI systems to make our Golden Path fast, reliable, and intuitive. - Shape abstractions and workflows that are AI-native, consumable by both humans and AI agents. You’ll operate as an IC, leading multi-quarter initiatives and collaborating with product teams, infrastructure, SRE, and Security. What you’ll do - Build and evolve developer CLI tooling - Extend our primary developer CLI to scaffold new component types and services end-to-end (service manifests, container/build configs, deployment charts, build files, API definitions, starter code, alerts, runbooks). - Improve environment and credentials tooling to make local development setup fast and reliable. - Own core CI/CD and linting infrastructure - Design and maintain the backend CI workflow used by backend services. - Evolve linting, formatting, and typing tools to enforce architectural and code-quality guardrails across the monorepo. - Debug and fix CI issues that block engineers, and proactively reduce flakiness and runtime. - Steward shared Go and Python ecosystems - Own key shared Go libraries (auth, caching, clients, configuration, cryptography, logging, metrics, domain/realm, server, etc.) and their usage across many applications. - Maintain and evolve Python shared libraries and frameworks in our core libraries, gRPC helpers, utilities, and standardized components. - Strengthen test and release safety - Extend automated canary analysis with new metric types, backtesting, and safer defaults. - Build and improve test automation tooling, bad-test detection dashboards, and dependency-analysis utilities to keep main green and tests reliable. - Contribute to automation that classifies CI failures and summarizes them for engineers (including LLM-assisted workflows). - Drive platform-level design and abstractions - Design abstractions that balance simplicity for product engineers with enough power for advanced use cases. - Collaborate with PM/TPM, infrastructure, and product teams to scope and deliver multi-team initiatives (e.g., prompt-to-product workflows, typing and linting initiatives, test automation). Required technical skills Languages & frameworks - Strong experience with Python 3.x: - CLI development (e.g., Click or similar). - YAML/Jinja2-style templating. - Modern type hints and typing discipline (e.g., typing, dataclasses / attrs, Pydantic-style patterns). - Testing with pytest or similar. - Solid experience with Go: - Shared library and service development (gRPC/HTTP). - CLI patterns (e.g., Cobra/Viper or equivalents). - Testing with Go testing frameworks (e.g., Ginkgo/Gomega or the standard library). Protocols & APIs - Protobuf/gRPC: - Schema design and evolution. - Cross-language client/server generation and integration. Build systems & infrastructure - Bazel in a large monorepo: - BUILD rules and dependency management. - Working with code generation for APIs and clients. - Containers & orchestration: - Docker image builds. - Kubernetes concepts (Helm-style values, service deployments, readiness/liveness/health checks). - Cloud infrastructure (AWS preferred): - Experience with a meaningful subset of: object storage, relational databases (e.g., Postgres), key–value/document stores, search, streaming/ingest services, Kafka, Redis, IAM. CI/CD & DevOps - Modern CI systems (e.g., Git-based CI/CD platforms): - Authoring non-trivial pipelines (matrix builds, reusable workflows, secrets/permissions). - Infrastructure-as-Code mindset: - Comfortable working with service manifests (YAML), Terraform/Terragrunt-like patterns, or internal equivalents. - Linting & static analysis: - Hands-on experience configuring and tuning linting and typing tools (e.g., pylint, ruff, mypy, golangci-lint). - Experience writing or extending custom lint rules is a strong plus. Strongly preferred - Monorepo experience - Worked in a large, shared codebase with complex dependency graphs and shared frameworks. - Familiar with dependency graph analysis and strategies to keep builds/tests fast. - Developer experience (DX) tooling - Built or maintained CLI tools, scaffolding systems, or internal frameworks used by other engineers. - Thoughtful about ergonomics, documentation, and guardrails. - Testing & progressive delivery - Exposure to canary analysis / progressive rollout systems (e.g., Prometheus/PromQL, Grafana, automated deployment checks). - Experience with test data management, integration/E2E test infrastructure, or bad-test detection. - Messaging & streaming - Experience with Kafka (topic design, producers/consumers, observability, error handling). - LLM/AI integration - Experience or strong interest in using LLMs to improve developer workflows (e.g., failure summarization, smart code generation, AI-native CLIs). How you work We’re looking for someone who: - Designs scalable developer abstractions - Has built tools that many engineers depend on, and can point to where those abstractions worked (or didn’t) and why. - Operates across the full platform stack - Comfortable jumping between Python CLIs, Go libraries, Bazel rules, YAML manifests, CI workflows, and Kubernetes deployments. - Reduces complexity for others - Measures success by how much simpler and safer life becomes for product engineers and AI agents using the platform. - Thinks in terms of leverage - Prefers building guardrails, automation, and self-service over manual ops work. - Writes clear documentation - Treats usage and developer documentation as part of the product surface, not an afterthought. - Collaborates across teams - Communicates clearly with product teams, infra, SRE, and security; comfortable gathering feedback via Slack, ticketing systems, and design docs and iterating quickly. If you’re excited about building the tools and infrastructure that hundreds of engineers (and AI agents) will use every day, we’d love to talk. #LI-AJ1 Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here. If you would like more information on your EEO rights under the law, please click here.