Sr. Software Engineer, Data & AI Platform

BetterWorks Systems, Inc.•Menlo Park, CA

2h•Remote

About The Position

Betterworks is HR software to align, develop, and activate your workforce for business growth. Organizations are able to replace outdated, ineffective, universally loathed annual review processes with powerful Continuous Performance Management programs that help managers be better at the conversations, coaching, and development necessary to inspire and motivate the entire workforce to achieve an organization's top priorities today and be ready for tomorrow's challenges. This is a fully remote position. Join our Data & AI Platform team as a Sr. Software Engineer and play a key role in shaping the future of our AI platform. As a Sr. Software Engineer, Data & AI Platform at Betterworks, you'll have a significant impact, collaborating closely with design, engineering, and product teams to build and scale a world-class Data and Generative AI platform. We're looking for someone who possesses deep expertise in platform engineering, a passion for crafting robust and scalable data and AI infrastructure, and the ability to contribute to a high-performing team.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
5-8 years of professional experience in software development, with a strong focus on backend or platform engineering.
Deep expertise in Python (must have) and other modern languages Typescript or Go, and experience building complex, scalable microservice-based or event-driven backend systems.
Proven hands-on experience with AWS services is essential, especially for large-scale, high-performance workloads.
Strong understanding of backend architecture patterns and best practices for building scalable and maintainable applications (Microservices, Integrations, Observability).
Experience or strong interest in Generative AI, Operations (MLOps), and developing components like RAG, Inference services, and Evaluation pipelines.
Familiarity with event-driven architecture (EDA), message queues and asynchronous processing (Kafka, RabbitMQ).
Experience with backend frameworks and technologies: FastAPI, NestJS, Postgres etc.
Proficiency in writing unit, integration, and end-to-end tests for platform applications.
Excellent problem-solving and debugging skills for complex distributed systems.
Strong communication and collaboration skills, with an ability to lead technical discussions and projects.
Self-motivation to explore the new AI technologies from experimentation to production at scale.

Nice To Haves

Champion best practices in platform engineering, including CI/CD, testing, and observability for data and AI-specific workloads. Experience with GitHub Actions is a plus.
Experience using AI-powered development tools (GitHub Copilot, MCP servers, AI IDEs) to enhance productivity and code quality is a plus.
Knowledge of advanced Gen AI concepts including , embedding, Vector DBs, Retrieval-Augmented Generation (RAG), Fine-tuning, Inference optimization, evaluation, guardrails, Model Context Protocol (MCP) clients/servers and Agentic AI is a significant plus.

Responsibilities

Design, develop, and enhance core features of our data and AI platform, focusing on robust, scalable, and resilient cloud distributed services and architecture.
Champion best practices in platform engineering, including CI/CD, testing, and observability for data and AI-specific workloads. Experience with GitHub Actions is a plus.
Proactively identify and troubleshoot performance bottlenecks and infrastructure challenges within the AI platform, ensuring optimal application performance and reliability.
Contribute to the evolution of our platform architecture, exploring and evaluating new technologies and approaches in Data pipelines, self-hosting, and self-managing Generative AI, LLMOps, and scalable distributed systems.
Possess deep experience with Observability tools for logging, tracing, monitoring, alertings and dashboards like New Relic, Prometheus, and Grafana.
Leverage deep expertise in containerization and orchestration technologies, specifically Kubernetes and Docker, to manage and scale the AI infrastructure.
Collaborate closely with product managers, data scientists, and engineering stakeholders to translate Generative AI and platform requirements into detailed technical specifications.
Mentor and guide junior engineers, fostering a collaborative and supportive team environment focused on platform excellence.
Stay up-to-date with emerging technologies and trends in Data & AI platform development and Generative AI, sharing your knowledge with the team.
Knowledge of advanced Gen AI concepts including , embedding, Vector DBs, Retrieval-Augmented Generation (RAG), Fine-tuning, Inference optimization, evaluation, guardrails, Model Context Protocol (MCP) clients/servers and Agentic AI is a significant plus.