Description
Senior Software Engineer - AI Products
Location: San Francisco
Department: Engineering
Location Type: HYBRID
Employment Type: FULL_TIME
About Gem
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.
About the Role
We're hiring senior and staff engineers to join our AI product engineering team. Here's what makes this different: at Gem, software engineers don't just build around models, they work directly on them. You'll fine-tune LLMs and embedding models, rethink our search architecture, clean and optimize data flows, and make calls on what systems and infrastructure we use. This team provides high ownership with the opportunity to experiment, prototype and then ship to tproduction. Nothing is off the table if it makes our search faster and more accurate.
This is hands-on work from end to end. You'll integrate directly with LLMs and rerankers, experiment with new models as they launch, build evaluation systems to measure what actually matters, and own the entire stack from Snowflake data pipelines to embedding queries to the UI someone sees in their browser.
Recruiting is an industry ripe for AI transformation, the features you build will directly help companies discover and hire exceptional talent, impacting their success. Unlike generic ML tooling, the work you do here changes how teams scale and how people land meaningful jobs.
What You’ll Build
- Model work: fine-tuning LLMs and embedding models for recruiting queries, testing new providers as they launch, building systems to evaluate what actually improves search quality
- Search at scale: making semantic search instant across 800M+ profiles, integrating rerankers to surface better candidates, designing the feedback loops that help search get smarter
- Data infrastructure: owning pipelines in Snowflake that feed our models, cleaning and structuring candidate data, building the systems that let us experiment quickly without breaking production
- Shipping full-stack features: writing the code from prompt engineering to UI, creating interfaces that make complex search feel simple, iterating based on what recruiters actually tell us
What You Bring
About You
- You have 5+ years of industry experience as a software engineer, building user-facing products.
- You enjoy mentoring, working cross-functionally, and working directly with customers.
- You move with velocity and invest in improving your craft.
- You prioritize technical quality, building reliable and scalable systems.
- You are product-oriented and you don’t hesitate in making decisions.
- You are energized by collaborating in-office with your peers in a hybrid model.
Extra Credit:
- You've built agents or worked on eval infrastructure
- You know your way around vector databases (Pinecone, Weaviate, Qdrant) or search systems (Elasticsearch)
- You've worked with RAG architectures or ML observability
- You've worked at an early-stage startup where you had to figure things out yourself
- Background in information retrieval, ranking, NLP, or recommendation systems
- Experience with data pipelines (Snowflake, dbt), model deployment, or monitoring models in production
- Comfortable in Python and working with ML frameworks like PyTorch or TensorFlow
How We Work
We've removed most development friction:
- Local dev with Vite boots instantly with hot-reload
- CI runs in ~10 minutes, deploys go straight to production
- We ship fast and iterate based on customer feedback
Our engineering culture:
- Reasonable hours
- Weekly team events and happy hours
- Regular hackathons for experimental features
- Team from Meta, Uber, and Dropbox
This role offers the chance to work on a small, world-class team (ex-Meta, Uber, Dropbox) building real products in a fast-moving, user-focused environment. You’ll have high ownership, lots of say in how we build, and the opportunity to shape both technology and team norms as we scale. If you’re excited by the idea of shipping something impactful, iterating quickly, and seeing your work change how companies hire – we’d love to talk to you.
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
Role Details
- 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.
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