Design and implement production-grade AI features to enhance recruiting efficiency, utilizing AI/ML technologies for intelligent candidate search and insights extraction.
About Gem
About the Role
What You'll Do
What You'll Need
Nice to Haves
How We Work
Benefits
Role Details
Gem is the only AI-first all-in-one recruiting platform. It brings together your ATS, CRM, sourcing, scheduling, and analytics — plus 800+ million profiles to source from — with AI built into every workflow. By eliminating the headaches of juggling multiple tools, Gem helps customers boost recruiter productivity by up to 5x while saving 30-50% on technology costs. Over 1,000 organizations — from startups to industry leaders like Zillow, DoorDash, and Asana — trust Gem to fuel their growth. With an industry-leading 4.8/5 rating on G2, Gem is the platform recruiters actually love to use. Gem has raised $148M from renown investors including Accel, Greylock, ICONIQ, Sapphire, and Meritech.
With Gem, you can experience the power of a truly connected recruiting platform — one consistent interface, unified data, smarter AI recommendations, and simplified permissions. Our customers achieve remarkable results.
Just as we strive to help our customers find great talent, we also invest in our own people and culture. We are proud of the culture we’ve built and have recently been recognized as:
- Forbes America's Best Startup Employers 2026
- Great Place to Work Certified, 2024
- Fortune Best Workplaces for Millennials, 2023
We work in our San Francisco office three days per week. In-person collaboration is core to how we ship quickly and build great products together.
We're looking for a senior or staff AI/ML Engineer to design and ship production-grade AI features that transform how companies hire. You'll be one of the first AI/ML engineers at Gem, shaping both the technology and how we build. You'll work on challenges like building intelligent search systems that surface perfect candidates from massive talent pools, creating AI tools that help recruiters craft personalized outreach, and extracting insights from candidate profiles and recruiting activity. You'll own your work end-to-end, from prototype to production, and iterate quickly based on real recruiter feedback.
Recruiting is an industry ripe for AI transformation. Unlike chatbots or summarization tools, our AI directly impacts whether companies succeed or fail by finding them exceptional talent.
- Build multi-step reasoning systems that understand nuanced recruiter queries like 'senior ML engineers who've worked at YC companies and know PyTorch' – currently processing 800M+ profiles
- Develop intelligent search that understands recruiter intent and surfaces the right candidates instantly
- Ship rapidly and iterate by moving from prototype to production, measuring performance, and refining based on live product data and user feedback
- Collaborate closely with product managers, designers, and engineers in our SF office to create cohesive AI-driven experiences
- Share ML expertise with teammates and help establish best practices for model development and evaluation
What You'll Need
Core technical skills:
- Experience applying AI/ML in production environments, especially with LLMs
- Hands-on knowledge of retrieval and search systems, including query understanding, ranking, and relevance optimization
- Familiarity with vector databases and embedding models for AI applications
- Understanding of retrieval architectures and their implementation
- Pragmatic approach to AI. You find the right balance between cutting-edge techniques and scalable, maintainable solutions
How you work:
- Problem-solving: You approach problems holistically, starting with clear understanding of context. You navigate ambiguity well, decompose complex problems into clean solutions, and think critically about real-world impact
- Shipping quality products: You partner closely with product and design to craft experiences that feel effortless. You understand the balance between craft, speed, and business impact
- Communication and collaboration: You explain technical concepts clearly to both engineers and non-technical stakeholders. You engage thoughtfully in disagreements and compromise when needed
- Velocity and ownership: You're excited by quick experimentation and continuous improvement. You take pride in shipping features from start to finish
- User focus: You care about business impact and prioritize accordingly. You think critically about how your work shapes real people's daily workflows
Nice to Haves
- Prior startup experience (especially 0→1 product)
- Prompt and context engineering skills or experience with LLM fine-tuning
- Background in information retrieval, NLP, or recommendation systems
- Experience implementing feedback loops to improve search quality over time
- Track record of shipping AI features that balance product craftsmanship with technical rigor
How We Work
We've intentionally invested in removing development friction:
- Local development with Vite boots instantly with hot-reload
- CI finishes in about 10 minutes, and commits deploy to production immediately
- We ship quickly and iterate based on customer feedback
We take pride in having an unusually good engineering culture:
- Reasonable working hours
- Weekly team activities and happy hours
- Regular hackathons where we build experimental features
- Work with a world class team from Meta, Uber, and Dropbox
Benefits
- 10-year stock option exercise window
- Flexible Time Off and 16 paid holidays (including company wellness days)
- Best-in-class medical, dental, and vision coverage
- $1,200 annual learning and development stipend
- 16 weeks paid parental leave for all parents, plus $1,500 new-parent perk and flexible return-to-work options
- Location: This role is based 3 days per week out of our San Francisco HQ and is not eligible for full-time remote work.
- Compensation: The annual cash compensation range for this position is $190,000–$260,000 based on level in addition to equity & benefits.
The range displayed on this job posting reflects the minimum and maximum compensation. Factors including location, level, job-related knowledge, skills, and experience will determine compensation.
Gem is an equal opportunity employer. We celebrate our inclusive work environment and encourage folks of all backgrounds and perspectives to apply. At Gem, we’re committed to having an inclusive and transparent environment where every voice is heard and acknowledged. We embrace our differences, and know that our diverse team is a strength that drives our success. Gem is committed to developing a barrier-free recruitment process and work environment. If you require any accommodation, please email us at [email protected] and we’ll work with you to meet your accessibility needs.
Gem’s Candidate Privacy Notice
By clicking “Submit Application”, you acknowledge and agree that you have read and understand Gem’s Candidate Privacy Statement, including the information provided on how Gem processes your personal data and your related rights as set forth therein.
#LI-MF1
Gem San Francisco, California, USA Office
San Francisco, CA, United States
Similar Jobs
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.
Top Skills:
C++Distributed Ml PipelinesGenerative ModelsImitation LearningLarge-Scale Training InfrastructureMotion PlanningOnboard Real-Time SystemsPythonReinforcement LearningSimulation EnvironmentsTrajectory Planning
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
Lead design, build, and operate scalable observability systems for distributed cloud and on-vehicle environments. Contribute code and reviews, create technical designs, build tooling to collect observability signals, run PoCs for new tools, partner with engineering teams, define SLIs/SLOs, guide roadmap decisions, and mentor engineers to improve developer productivity and reliability.
Top Skills:
AWSAzureDockerGCPGoGrafanaIstioKubernetesObservability PipelinesPrometheusSlisSlosSshTerraformTsdbsUnix/Linux
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Design, train, evaluate, and productionize LLM-based NLU and agentic AI systems. Improve model quality, latency, reliability, and safety; fine-tune models, implement agent architectures, and collaborate across teams to deploy scalable, privacy-preserving conversational AI.
Top Skills:
DpoGoGraph Of ThoughtsHybrid Vector DatabasesLarge Language ModelsMac Development EnvironmentMultimodal Foundation ModelsPythonRlaifRlhfTree Of Thoughts
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
