Interested in this AI/ML Engineer role at FORA?
Apply Now →Skills & Technologies
About This Role
About Fora
--------------
Fora is the modern travel agency, empowering anyone with a passion for travel to build a thriving advisory business. We're modernizing the $100B\+ travel agency industry by combining powerful technology, data, and community to enable thousands of entrepreneurs to build on our platform. Our business\-in\-a\-box platform gives travel entrepreneurs everything they need to launch and scale, from cutting\-edge tools and personalized training to a vibrant community and exclusive industry partnerships. At the heart of it all is our mission: 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 2025, we announced our $60 million Series B and C investment rounds, led by Thrive Capital and Insight Partners, with participation by previous investors including Forerunner and Heartcore Capital. We've also been recognized as a LinkedIn Top Startup 2024, Fast Company's Most Innovative Companies 2025 and 2023, and Built In 2025 Best Places to Work.
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.
About The Role
------------------
We’re looking for a seasoned Senior / Staff Backend Engineer to help build Fora’s fast growing suite of AI\-driven products and infrastructure. Our ideal candidate is someone who follows AI closely and can tell which models and frameworks are worth using and which won’t last; 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.
Most travel agents can’t scale past $1M / year in business before they need to make their first hire. By giving advisors a day\-one assistant to delegate entire workflows to, we believe they should be able to solo\-operate $100M / year businesses. Our current focus is on building Via, an AI co\-pilot built into our advisor portal. Via takes on time\-consuming tasks so advisors can move faster, stay more organized, and glean insights into their business that they couldn't ever before, freeing them to do what they love: building client relationships, curating unforgettable trips, and growing their business.
This role tackles challenges across the AI stack including: Building and expanding our suite of MCP tools; tuning our agent harness and orchestration layer for quality, cost, and speed; and building evaluations and observability that let us ship changes to a non\-deterministic system with confidence; and much much more.
Key Responsibilities
------------------------
- 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
- Continue improving the reliability of a non\-deterministic system by designing evals, regression suites, tracing, and logging event metrics so we can measure quality, catch tool\-call drift, and ship prompt, model, and tool changes with less risk
- Focus on performance, latency, architecture, and clean code – advisors feel every millisecond of a streamed response and every wrong tool call
- 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
- Help establish the patterns, primitives, and best practices for Applied AI at Fora as the teams surface area grows
Requirements
----------------
- 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
- 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
- Ability to be a team player, with strong communication and collaboration skills
- An entrepreneurial mindset and comfort with ambiguity and fast iteration
Strongly Preferred
----------------------
- Hands\-on experience shipping LLM\-powered features in production: MCP tools, agentic loops, structured outputs, streaming (SSE), prompt design, retrieval, evals
- 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)
- Daily use of AI coding tools as part of how you build
- Experience with async task and streaming infrastructure (Celery, RabbitMQ, or similar)
- AWS experience
- Kubernetes experience
Compensation
----------------
Compensation for this role varies based on experience, with an indicative range of $155K–$225K \+ equity. Final compensation will depend on the level at which the candidate is hired, as we’re considering multiple levels for this role.
- Unlimited vacation
- Health Insurance (including an option completely covered by Fora HQ)
- Dental \& Vision Insurance
- Wellhub Memberships
- 401k plan with company match
- Commuter Benefits
- Supplemental Life Insurance
- Stock Options
This role is based in New York City with a hybrid WFH \& office schedule (Monday, Tuesday and Thursday are our Tribeca in\-office days, with flexibility for Wednesday and Friday at your preference).
Our Values
--------------
We’re forging our own path
Fora has always been about driving change within the industry. We’re not interested in maintaining the status quo.
We’re stronger together
Community is our cornerstone and collective power is our strength. We believe we can all go further when we operate together, using our combined leverage to unlock better opportunities and outcomes for our advisors, partners, and travelers.
We believe in technology
We believe technology is an answer to some of the most fundamental challenges the travel industry faces. We believe advancements in AI, bold investments in our platforms, and a world\-class data infrastructure will transform the work of our advisors and our partners, while creating better travel experiences for travelers.
We’re here to serve
We operate in service of our community and believe that when they’re empowered to focus on what they do best, we all win. It’s why we relentlessly advocate for our advisors and prioritize their best interest every step of the way.
We mean business
Fora is equal parts fun, meaningful work and serious travel business. We’re unlocking opportunities for thousands of travel entrepreneurs, delivering a stream of high\-quality guests at scale for our partners, and providing a superior travel experience for our travelers. It’s a better equation for the future of our industry.
WORK AUTHORIZATION
Authorization to work in the United States is required for full\-time roles based in our New York City office. Fora is unable to sponsor or assist with U.S. work authorization. Roles based outside of the United States are not subject to this requirement.
EQUAL OPPORTUNITY
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.
Salary Context
This $155K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At FORA, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($190K) sits 5% above the category median. Disclosed range: $155K to $225K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
FORA AI Hiring
FORA has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in New York, NY, US. Compensation range: $225K - $225K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.