Our mission is to bring clarity and control to the world’s most complex codebases. AI is accelerating code creation, but the infrastructure to understand, oversee, and evolve that code hasn’t kept pace. Sourcegraph gives engineering organizations full visibility across their systems, precise context for their agents, and the ability to execute coordinated code changes at scale. As agentic development becomes the dominant engineering paradigm, we provide the context layer teams need to take control of their codebase.

With Code Search, Deep Search, MCP, and Agentic Batch Changes, we deliver on that mission today – giving engineering teams and their AI tools the cross-repo context to navigate massive codebases with confidence, and the ability to make changes across hundreds of repositories at once. Companies like Stripe, Reddit, and Leidos rely on Sourcegraph to ship faster and with higher quality. We’re backed by a16z, Sequoia, and Redpoint, and proud to operate a globally distributed team.

Hours & location:

While we hire almost anywhere in the world, we have a preference for someone to reside in the following locations for this role: USA. Location: Remote – Worldwide.

Why this job is exciting:

Sourcegraph is at the forefront of building AI tools to solve the biggest problems in the software industry. The Code Understanding team owns the surfaces where that intelligence meets the developer: Deep Search, our agentic, multi-step answer engine across an enterprise’s entire lineup of codebases, Query Assist, turning natural language queries into Sourcegraph query syntax, Smart Hovers, concisely summarizing symbols right where devs need it, guided diff review, and the APIs that both developers and agents use. Most of what makes those surfaces tick is agent engineering: a blend of software engineering, machine learning, and statistics. As the staff ML and agent engineer on Code Understanding, you’ll be the technical owner of it: setting the team’s direction for models, evaluations, and agent systems. This is a staff-level role: we’re hiring a technical leader, not just a strong individual contributor.

Concretely, you’ll own work like:

• Agentic systems. You’ll design and harden the multi-step, tool-using agent loops behind current and new agentic experiences
• Pragmatic use of evaluations. Crafting agentic products means needing to tell when a change actually helped
• Models: selection, upgrading, and training. You’ll decide which models we run where, drive upgrades, and fine-tune our own when that’s the right choice
• Retrieval and context engineering. You’ll push on how we ground models in a customer’s code – retrieval, ranking, context windows, and citations
• Cost and latency. Every surface has a per-user economic budget. You’ll treat cost and latency as product features

You’ll do this on a small, senior-leaning team that ships quickly and owns a lot of product surface.

Within one month, you will:

• Get the Code Understanding products and their model/agent pipelines running end-to-end locally
• Build a clear picture of where the AI engineering pain is and the product surfaces most constrained
• Get to know the team and our customers, and start forming your own opinions
• Join the team’s on-call support rotation

Within three months, you will:

• Own a meaningful agentic slice of the product end-to-end, driving it from problem-framing through rollout
• Establish how the team ships model and prompt changes responsibly: the evals, dashboards, and guardrails
• Begin up-leveling teammates in building with models by pairing with them and reviewing their code

Within six months, you will:

• Be the recognized technical authority for agent engineering and agentic systems on Code Understanding
• Have measurably moved the products: better answer quality, lower cost/latency, or new agentic capabilities
• Be setting the direction of the team’s roadmap where it intersects agents, bringing conviction

About you:

You are a staff engineer and technical leader with hard-won skills across production machine learning, evaluation, and agent systems. This high-leverage role relies on your ability to make sound model and evaluation decisions. You operate at staff scope: you own the most ambiguous, highest-risk problems in your domain.

• You have personally owned a production model lifecycle. You have trained or fine-tuned at least one model and taken it from dataset construction through evaluation
• You build agents, fluently and opinionatedly. You’ve designed multi-step agentic systems and made them reliable, observable, and cost-bounded
• You have strong evaluation judgment. You build representative datasets, meaningful baselines, useful error taxonomies, and release criteria
• You treat cost and latency as product constraints. You make measured quality, latency, and cost tradeoffs
• You operate autonomously on ambiguous problems. Given a rough product idea, a few customer quotes, and a Slack thread, you come back with a plan
• You contribute beyond your domain. As a senior IC, you go into whatever part of the codebase a problem requires
• You up-level the people around you. You mentor by pairing on hard problems, providing substantive design and code reviews
• You’re customer and product-driven. You’re comfortable on customer calls and in feedback threads, you turn raw signals into requirements
• You’re pragmatic, not a perfectionist. You ship the smallest correct thing and prefer robust solutions over complicated ones

On the engineering fundamentals:

• You’re a strong software engineer who can ship production services
• You’re comfortable across our stack – Go on the backend, TypeScript on the frontend, GraphQL, PostgreSQL
• You’re fluent with agentic coding tools, and you understand and own every line it submits
• You’re comfortable in an async-first, multi-service, fast-paced remote environment

Nice-to-haves:

• You’ve shipped an LLM-powered or agentic developer-facing product you can speak about opinionatedly
• You’ve fine-tuned, distilled, or trained models to meet cost, latency, or quality targets in production
• Experience with retrieval, ranking, embeddings, or search relevance
• Experience working directly with enterprise customers and translating their needs into a product
• Experience mentoring or up-leveling engineers, especially raising a team’s agent engineering fluency

Level:

This job is an IC4. You can read more about our job leveling philosophy in our Handbook.

Compensation:

We pay above-market salaries because we want to hire exceptional people who can focus on building great products. As an open and transparent company, our compensation philosophy and pay bands are visible to every Sourcegraph teammate, and we strive to make our approach equitable. Your base salary is determined by the IC4 pay band for your location zone (1-4). The starting salary for the IC4 pay band in each zone is:

• Zone 2: $176,000 USD
• Zone 3: $132,000 USD
• Zone 4: $88,000 USD

In addition to competitive cash compensation, we offer meaningful equity and great perks and benefits.

Interview Process:

We expect the interview process to take 5.5 hours in total. Introduction Stage: Recruiter Screen (30m), Hiring Manager Screen (30m). Team Interview Stage: Resume Deep Dive (60m), System Design (60m), Harden a Code Research Agent Interview (60m), Cross-functional team collaboration / Values (60m). Final Interview Stage: Leadership (30m). We check references and conduct your background check. You are welcome to request additional conversations with anyone you would like to meet.

Sourcegraph is an equal opportunity workplace; we welcome people from all backgrounds. Sourcegraph participates in E-Verify for U.S. Employees.

Source: Sourcegraph. Apply here: https://job-boards.greenhouse.io/sourcegraph91/jobs/6185008004