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Current Job Openings
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Gen AI Developer
Location: Remote-first (Global)
Job Type: Full-time
Department: AI/Platform
As a Gen AI Developer, the role is to design, fine‑tune, and productionize LLM‑powered features that solve real user problems. Work spans prototyping with GenAI, building RAG pipelines, integrating AI agents with product workflows, and measuring impact with robust evals and telemetry. Curiosity, strong engineering fundamentals, and a bias for safe, reliable releases are essential.
Responsibilities:
Build and ship GenAI features using LLMs, prompt engineering, and fine‑tuned models (LoRA/PEFT), with automated offline/online evaluations.
Design retrieval‑augmented generation (RAG) pipelines: chunking, embeddings, hybrid search, grounding, and guardrails for factual responses.
Develop tool‑using AI agents that orchestrate multi‑step tasks via APIs, queues, and webhooks; monitor with tracing and feedback loops.
Productionize models and services with secure data handling, RBAC, and observability; partner with product and design to define success metrics.
Optimize latency, cost, and quality through caching, distillation, and prompt/model versioning; document patterns and share learnings.
Gen AI Developer
Location: Remote-first (Global)
Job Type: Full-time
Department: AI/Platform
As a Gen AI Developer, the role is to design, fine‑tune, and productionize LLM‑powered features that solve real user problems. Work spans prototyping with GenAI, building RAG pipelines, integrating AI agents with product workflows, and measuring impact with robust evals and telemetry. Curiosity, strong engineering fundamentals, and a bias for safe, reliable releases are essential.
Responsibilities:
Build and ship GenAI features using LLMs, prompt engineering, and fine‑tuned models (LoRA/PEFT), with automated offline/online evaluations.
Design retrieval‑augmented generation (RAG) pipelines: chunking, embeddings, hybrid search, grounding, and guardrails for factual responses.
Develop tool‑using AI agents that orchestrate multi‑step tasks via APIs, queues, and webhooks; monitor with tracing and feedback loops.
Productionize models and services with secure data handling, RBAC, and observability; partner with product and design to define success metrics.
Optimize latency, cost, and quality through caching, distillation, and prompt/model versioning; document patterns and share learnings.
Gen AI Developer
Location: Remote-first (Global)
Job Type: Full-time
Department: AI/Platform
As a Gen AI Developer, the role is to design, fine‑tune, and productionize LLM‑powered features that solve real user problems. Work spans prototyping with GenAI, building RAG pipelines, integrating AI agents with product workflows, and measuring impact with robust evals and telemetry. Curiosity, strong engineering fundamentals, and a bias for safe, reliable releases are essential.
Responsibilities:
Build and ship GenAI features using LLMs, prompt engineering, and fine‑tuned models (LoRA/PEFT), with automated offline/online evaluations.
Design retrieval‑augmented generation (RAG) pipelines: chunking, embeddings, hybrid search, grounding, and guardrails for factual responses.
Develop tool‑using AI agents that orchestrate multi‑step tasks via APIs, queues, and webhooks; monitor with tracing and feedback loops.
Productionize models and services with secure data handling, RBAC, and observability; partner with product and design to define success metrics.
Optimize latency, cost, and quality through caching, distillation, and prompt/model versioning; document patterns and share learnings.
Software Engineer
As a Software Engineer, the role is to design, build, and maintain scalable web and backend systems that power customer experiences. Work closely with product, design, and QA to deliver high‑quality software through code reviews, automated testing, and iterative releases. Strong ownership, clear communication, and a user‑centric mindset are key.
Responsibilities:
Translate product requirements into robust technical designs; implement services, APIs, and UIs with clean, well‑tested code.
Ensure reliability and performance with monitoring, profiling, and systematic incident resolution; contribute to architecture and roadmap decisions.
Maintain code quality via reviews, CI/CD, and documentation; reduce tech debt through refactors and sensible abstractions.
Collaborate across functions to scope, estimate, and deliver predictable releases; champion security and privacy best practices.
Improve developer experience with tooling, automation, and shared libraries; mentor peers and uphold team standards.
Software Engineer
As a Software Engineer, the role is to design, build, and maintain scalable web and backend systems that power customer experiences. Work closely with product, design, and QA to deliver high‑quality software through code reviews, automated testing, and iterative releases. Strong ownership, clear communication, and a user‑centric mindset are key.
Responsibilities:
Translate product requirements into robust technical designs; implement services, APIs, and UIs with clean, well‑tested code.
Ensure reliability and performance with monitoring, profiling, and systematic incident resolution; contribute to architecture and roadmap decisions.
Maintain code quality via reviews, CI/CD, and documentation; reduce tech debt through refactors and sensible abstractions.
Collaborate across functions to scope, estimate, and deliver predictable releases; champion security and privacy best practices.
Improve developer experience with tooling, automation, and shared libraries; mentor peers and uphold team standards.
Software Engineer
As a Software Engineer, the role is to design, build, and maintain scalable web and backend systems that power customer experiences. Work closely with product, design, and QA to deliver high‑quality software through code reviews, automated testing, and iterative releases. Strong ownership, clear communication, and a user‑centric mindset are key.
Responsibilities:
Translate product requirements into robust technical designs; implement services, APIs, and UIs with clean, well‑tested code.
Ensure reliability and performance with monitoring, profiling, and systematic incident resolution; contribute to architecture and roadmap decisions.
Maintain code quality via reviews, CI/CD, and documentation; reduce tech debt through refactors and sensible abstractions.
Collaborate across functions to scope, estimate, and deliver predictable releases; champion security and privacy best practices.
Improve developer experience with tooling, automation, and shared libraries; mentor peers and uphold team standards.
DevOps/MLOps Engineer
Location: Remote (Global)
Job Type: Full‑time
Department: Platform Engineering
As a DevOps/MLOps Engineer, the role blends platform reliability with model operations. Build the pipelines, cloud infrastructure, and observability that let product teams ship features continuously and data teams deploy, monitor, and improve ML models safely in production. The work emphasizes automation, security, and measurable uptime and model health.
Responsibilities:
Own CI/CD and IaC: design and maintain build/test/deploy pipelines and infrastructure as code (Terraform/CloudFormation) for services and ML workloads.
Cloud reliability: implement autoscaling, blue/green/rolling deploys, monitoring/alerting, backups/DR, and SRE practices to keep systems resilient and fast.
Security and compliance: integrate DevSecOps checks, secrets management, RBAC, and vulnerability scanning across apps and data paths.
MLOps lifecycle: productionize models with versioned data/artifacts, feature stores, model registries, and automated training/deployment workflows.
Model observability: track latency, drift, bias, and accuracy; set up feedback loops, shadow tests, and safe rollbacks for new model versions.
Collaboration: partner with developers, data scientists, and product to define SLIs/SLOs, capacity plans, and release cadences; document runbooks and playbooks.
DevOps/MLOps Engineer
Location: Remote (Global)
Job Type: Full‑time
Department: Platform Engineering
As a DevOps/MLOps Engineer, the role blends platform reliability with model operations. Build the pipelines, cloud infrastructure, and observability that let product teams ship features continuously and data teams deploy, monitor, and improve ML models safely in production. The work emphasizes automation, security, and measurable uptime and model health.
Responsibilities:
Own CI/CD and IaC: design and maintain build/test/deploy pipelines and infrastructure as code (Terraform/CloudFormation) for services and ML workloads.
Cloud reliability: implement autoscaling, blue/green/rolling deploys, monitoring/alerting, backups/DR, and SRE practices to keep systems resilient and fast.
Security and compliance: integrate DevSecOps checks, secrets management, RBAC, and vulnerability scanning across apps and data paths.
MLOps lifecycle: productionize models with versioned data/artifacts, feature stores, model registries, and automated training/deployment workflows.
Model observability: track latency, drift, bias, and accuracy; set up feedback loops, shadow tests, and safe rollbacks for new model versions.
Collaboration: partner with developers, data scientists, and product to define SLIs/SLOs, capacity plans, and release cadences; document runbooks and playbooks.
DevOps/MLOps Engineer
Location: Remote (Global)
Job Type: Full‑time
Department: Platform Engineering
As a DevOps/MLOps Engineer, the role blends platform reliability with model operations. Build the pipelines, cloud infrastructure, and observability that let product teams ship features continuously and data teams deploy, monitor, and improve ML models safely in production. The work emphasizes automation, security, and measurable uptime and model health.
Responsibilities:
Own CI/CD and IaC: design and maintain build/test/deploy pipelines and infrastructure as code (Terraform/CloudFormation) for services and ML workloads.
Cloud reliability: implement autoscaling, blue/green/rolling deploys, monitoring/alerting, backups/DR, and SRE practices to keep systems resilient and fast.
Security and compliance: integrate DevSecOps checks, secrets management, RBAC, and vulnerability scanning across apps and data paths.
MLOps lifecycle: productionize models with versioned data/artifacts, feature stores, model registries, and automated training/deployment workflows.
Model observability: track latency, drift, bias, and accuracy; set up feedback loops, shadow tests, and safe rollbacks for new model versions.
Collaboration: partner with developers, data scientists, and product to define SLIs/SLOs, capacity plans, and release cadences; document runbooks and playbooks.