Fora

Senior/Staff Backend Engineer, Applied AI

New York City · Staff+ · Part-time

No sponsorship$180k-$225kDetected 1 day ago

PythonDjangoFlaskAWSKubernetesRabbitMQLLMsAI OrchestrationCommunicationCollaboration

About the role

has built, shipped, triaged, and optimized something that uses LLMs (in production or on the side)
and has experience working in ambiguity, on top of APIs and SDKs that change every few weeks.

Partner with the team's Product Manager and Designer to define and prioritize Fora's Applied AI roadmap, turning ambiguous goals into shippable agent capabilities
Help design and build our MCP tooling - exposing Fora's data and service layers through permission-aware MCP tools that agents can call on an advisor's behalf, with the proper guardrails in place so that we only ever act on the right data at the right time
Build and harden our agent orchestration - tool-calling loops, streaming responses, multi-step workflows, retries and guardrails, and the contracts between Fora's advisor portal and the AI service
Collaborate with engineers across the company to define end-to-end solutions that span the AI services, the core Django platform, and our portal frontend
Take features from 0 → 1, owning an entire solution from design to implementation to delivery and beyond
High API and distributed-systems design skills, with strong fundamentals in relational databases and ORMs
Genuine curiosity and drive to go deep and stay up to date on applied AI

Bachelor's Degree in Computer Science, or equivalent practical experience
5+ years experience in backend web development
3+ years experience with Python, using Django / Flask / Alternative
Ability to be a team player, with strong communication and collaboration skills
Familiarity with the modern agent stack: the Model Context Protocol (MCP), LLM provider SDKs (e.g., Anthropic, OpenAI), and agent / eval frameworks
Experience designing evaluation and observability for non-deterministic AI systems (offline evals, LLM-as-judge, tracing, prompt / version tracking)
Experience with async task and streaming infrastructure (Celery, RabbitMQ, or similar)

Our ideal candidate is someone who follows AI closely and can tell which models and frameworks are worth using and which won't last

Compensation for this role varies based on experience, with an indicative range of $180K–$225K + equity.

to help the next generation of travel entrepreneurs turn their love for travel into a fulfilling career, whether full-time or part-time.
We believe that everyone, from seasoned professionals to first-time advisors, can build something both profitable and personal.
Founded in 2021 by seasoned entrepreneurs, Fora has grown steadily since, expanding to a team of 200+ full-time employees based in downtown New York City.
In 2026, we announced our $60 million Series D investment round, led by Forerunner and Tactile Ventures, with continued participation from existing investors Thrive Capital, Insight Partners, and Heartcore Capital.
We've also been recognized as a LinkedIn Top Startup 2024, Fast Company's Most Innovative Companies 2025 and 2023, and we were most recently recognized as one of the TIME100 Most Influential Companies 2026.
We're building the first truly unified platform for all travel needs-leveraging the best of human expertise and technology to transform how people plan and book travel.

Fora is committed to an equitable hiring process and an inclusive work environment.
BIPOC and traditionally underrepresented candidates are strongly encouraged to apply.
We will not discriminate and will take action to ensure against discrimination in employment, recruitment, advertisements for employment, compensation, termination, upgrading, promotions, and other conditions of employment against any employee or job applicant on the bases of race, color, gender, national origin, age, religion, creed, disability, veteran's status, sexual orientation, gender identity, gender expression or any other characteristic protected by law.

This listing is sourced directly from Fora's careers page and normalized into a canonical job model.