Top Software Engineer Jobs in Portland, ME

Build and operate large-scale data pipelines that collect, process, and optimize audio datasets for AI model training. The role involves discovering data sources, improving data quality, throughput, and cost efficiency, partnering with AI scientists, and owning engineering projects end to end. Strong backend software engineering, Python, Bash, Linux, production systems, and data-processing experience are required, with GCP, Terraform, web crawling, and audio or ML dataset familiarity preferred.

Build and operate large-scale AI data pipelines, discover audio data sources, optimize data-processing systems for quality, throughput, and cost, and collaborate with AI scientists on training datasets. The role requires backend software engineering, production systems experience, independent problem-solving, and ownership of projects from discovery through continuous improvement. Nice-to-have experience includes web crawling, distributed processing, cloud infrastructure, Terraform, dataset quality, and audio processing.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable ingestion and processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed, ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement, with emphasis on backend development, production systems, data acquisition, and scalable infrastructure.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing processing systems for quality, throughput, and cost, and collaborating with AI scientists. Own engineering projects from discovery through continuous improvement while working independently in an ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires strong backend engineering, production systems experience, Python and Bash proficiency, and independent ownership of projects. Experience with web crawling, distributed processing, GCP, Terraform, machine-learning datasets, or audio processing is advantageous.

Build scalable data pipelines and processing systems for AI model training at petabyte scale. Responsibilities include discovering audio data sources, improving dataset quality and processing efficiency, optimizing throughput and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. The role involves discovering data sources, optimizing data processing for quality, throughput, and cost, partnering with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and scale full-stack software for employer and Employee Assistance Program products, including employer portals, patient and provider workflows, eligibility, authorization, and billing. Lead technical projects, design secure distributed systems, mentor engineers, contribute to engineering culture, and use modern technologies and AI-assisted development tools. Collaborate cross-functionally to expand access to mental healthcare.

Build and scale full-stack software for an AI-native cybersecurity platform. Responsibilities include developing backend services and APIs, creating frontend applications, collaborating on product features, writing tested code, participating in reviews, troubleshooting across the stack, and improving engineering processes. The role requires 3–5 years of software engineering experience and experience with backend, frontend, databases, and cloud technologies. Docker, Kubernetes, CI/CD, AI/ML, and startup experience are preferred.

Leads architecture and hands-on development of scalable backend systems and APIs supporting real-time applications. Owns the engineering lifecycle, writes high-performance code, establishes testing and reliability practices, resolves production issues, guides technical roadmaps, collaborates cross-functionally, and mentors engineers. The role requires expertise in object-oriented backend development, distributed cloud systems, automated testing, system monitoring, and technical leadership.

Design and deliver Android system applications for Toast POS devices, including device policy enforcement, setup, firmware and app updates, background services, and fleet deployment. Lead technical design, cross-functional planning, Android and hardware upgrades, automated testing, production issue investigations, and staged rollouts. Mentor engineers and collaborate with product, hardware, QA, and software teams to build secure, reliable device-management systems.

Design, build, deploy, and operate large-scale, highly available distributed systems for Twilio SendGrid. Improve scalability and security, implement reliable components, add monitoring, automate processes, and collaborate with product and engineering to deliver customer-focused features.

Join as the founding full-stack engineer to extend a TypeScript React/Next.js prototype, design product features, integrate AI/OCR, write tests and infrastructure, talk to users, and iterate quickly to drive growth.

Build and maintain high-performance, resilient SDKs across multiple programming languages and platforms. Develop features, improve reliability, collaborate with engineering and product teams, maintain open-source repositories, and influence technical and product decisions. The role requires strong software engineering fundamentals, polyglot development ability, library-building experience, production-quality coding, and expertise in unit, integration, load testing, and benchmarking.

Work directly with the founder to design and build core product features: integrations that pull license data, automation to file renewals/applications, data cleaning with AI, dashboard and alerting UX, and establish engineering practices for testing, deployments, and security. Term contractor role with potential conversion to full-time based on performance.

Build, test, review, deploy, monitor, debug, and maintain full-stack software used in K-12 classrooms. Work primarily with React, TypeScript, Node.js, and AWS while improving complex codebases and service design. Participate in incident response, refactoring, code reviews, pairing, mentorship, and cross-functional collaboration with product, design, and education experts. Use AI tools thoughtfully and prioritize reliable, accessible, and equitable learning experiences.

Build Tailscale’s internal AI-enablement tools, workflows, guardrails, and documentation. Partner with engineering leadership and teams to define the AI roadmap, evaluate coding agents and models, improve adoption, and measure effects on velocity and quality. The role involves developing internal developer-facing automation, teaching and influencing engineers, identifying high-leverage AI opportunities, and promoting safe use of AI-generated code in a distributed engineering organization.

Lead end-to-end development of reliable care-agent tooling and software services integrating contact-center platforms with backend systems. Own architecture, performance, security, production operations, and technical direction while partnering with product, care operations, QA, and engineering teams. Translate operational workflows into scalable solutions, mentor engineers, and improve tools using agent feedback, operational metrics, and AI-assisted development practices.

Build and scale backend systems, APIs, infrastructure, and applications for multimodal sensing and AI intelligence platforms. Responsibilities include processing sensor, audio, video, and environmental data; supporting data pipelines and model inference; ensuring reliability, scalability, and data integrity; and contributing to engineering standards and long-term technical strategy.

Lead technical vision and strategy for Affirm’s Cards backend engineering organization. Architect scalable and reliable platforms, guide cross-team initiatives, champion AI adoption and engineering excellence, influence technical roadmaps, and mentor engineers through Staff level. The role requires strong architectural expertise, organizational leadership, project management, communication, and cross-functional collaboration.

Develop and debug production-grade full-stack applications across front-end and back-end systems. Build features, APIs, databases, and data models; troubleshoot end-to-end issues; analyze unfamiliar codebases; translate implementations across programming languages; and review AI-generated and developer-written code for correctness, security, scalability, performance, and maintainability.

Develop and evaluate coding tasks for AI systems, including bug fixes, feature development, refactoring, optimization, reproducible environments, reference solutions, automated testing, and deterministic evaluation. The role also involves debugging complex codebases, reviewing contributions, documenting technical decisions, and refining evaluation criteria. Candidates should have strong software engineering fundamentals, backend or API development experience, open-source contributions, and proficiency in at least one listed programming language.

Lead technical execution and architecture for the Loyalty product. Own roadmap, design distributed services and APIs, ensure reliability/observability, integrate with ZMP and AthenaOS, build and coach engineering teams, and drive cross-team delivery of scalable multi-tenant loyalty capabilities.

Own and deliver backend lakehouse infrastructure goals, lead engineers through complex problems, and build highly available distributed data systems. Collaborate with product, design, analytics, and engineering stakeholders; define scalable technical solutions; improve code quality and engineering standards; monitor operational health; support on-call responsibilities; and mentor team members.