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Baseten · San Francisco, CA · posted 26 days ago
By AtiveSite · Published 07/11/2026 · Updated 10/11/2026 · Every figure is linked to its source below.
| Employer | Baseten |
|---|---|
| Location | San Francisco, California |
| Field | Technology › Software engineering |
| Level | not stated |
| Source category | EPD, Runtime Fabric |
| Posted | 2026-10-15 (26 days ago) |
| Work arrangement | A remote or flexible location is listed |
| Pay | Not published in the source listing |
ABOUT BASETEN
Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.
THE ROLE
Container runtimes were designed for general-purpose software workloads. AI inference is not a general-purpose workload.
Running large models at production scale exposes cracks in every layer of the container stack: runtimes unaware of GPU memory constraints, images that take minutes to pull when a model needs to scale to thousands of replicas, and isolation mechanisms that weren’t designed for the multi-tenant serving environments that production AI requires. The tools the industry has relied on for a decade weren’t built for this, and patching around those limitations at higher layers only goes so far.
Baseten owns the entire pipeline, from the moment a developer pushes a model to the moment a request gets a response. That vertical ownership means we can fix these problems at the root. The Runtime Fabric team is doing exactly that: purpose-building the container runtime and storage layers for AI inference workloads, led by some of the world’s top containerd maintainers.
As Engineering Manager for Storage Products on the Runtime Fabric team, you will lead the engineers who build and run the storage products behind that stack: the Baseten Delivery Network (BDN), which sits in the cold start path of every model on the platform, the snapshotters and filesystems that expose model weights and container images to a running container, and the storage that model deployment and training need next. The team is advancing the next generation of caching for inference workloads, from the origin tier to the GPU host. You will own reliability for the area as a measured outcome, report to the head of Runtime Fabric, and stay close to the code through design and code review. If you’ve managed systems engineers and have hands-on experience with caching, content-addressable storage, or distributed filesystems, we’d love to talk.
EXAMPLE INITIATIVES
How the Baseten Delivery Network (BDN) makes cold starts fast
Extending BDN from model weights to container image layers, training checkpoints, and deployment artifacts
Lazy-loading snapshotters and block-level delivery (EROFS, FUSE, ublk) so a container can start before the full image or model has arrived
RESPONSIBILITIES
Team Leadership and Culture
Hire and grow a team of 5 to 10 systems engineers with storage, caching, and Linux expertise
Run the team’s planning and delivery: scope work against the Runtime Fabric roadmap, set priorities, and ship on a predictable cadence
Provide regular coaching, feedback, and career development support to your direct reports
Own on-call health for the team’s services, including escalation paths and follow-through on incidents
Technical Direction
Guide the architecture and evolution of the Baseten Delivery Network: the tiered caching and weight delivery product that makes cold starts 2-3x faster and eliminates thundering herd failures during burst scaling events
Shape storage products tailored to Baseten’s workloads: extend BDN from model weights to container images, training checkpoints, and deployment artifacts, and define the interfaces that model deployment, training, and customers use to reach them
Oversee the on-host side of delivery: lazy-loading snapshotters, EROFS and overlayfs layouts, and block-level delivery (FUSE, ublk) for multi-gigabyte files
Own reliability of the delivery path as a measured outcome: end-to-end instrumentation from snapshotter initialization to first inference request, targets for cold start and cache behavior, tolerance for failing upstream registries and object stores, and safe rollout of changes to a path every customer depends on
Make the delivery path work across heterogeneous clusters and cloud providers, each with its own object store and network
Review designs and code across the area, and take on hands-on work where it unblocks the team
Champion the team’s contributions back to containerd and the snapshotter ecosystem alongside the maintainers on the Runtime Fabric team
Cross-Functional Partnership
Act as the point of contact for storage across Baseten, ensuring model deployment, training, and platform teams have the integration support they need
Optimize requirements for the underlying storage platforms with the infrastructure team: object storage, local NVMe and block storage, and network bandwidth per cluster, so that capacity and provider decisions account for what the delivery path needs
Manage the team’s dependencies in both directions: sequence work that model deployment, training, and infrastructure teams block on, and negotiate interfaces and timelines with the teams and upstream projects the storage path depends on
Work with the head of Runtime Fabric and product stakeholders to prioritize storage investments based on customer impact and infrastructure reliability
Communicate team progress, technical trade-offs, and incidents clearly to the team and to leadership
REQUIREMENTS
Experience managing a team of engineers in a systems, infrastructure, or storage context, including hiring and performance management
Hands-on background in distributed storage systems, content-addressable storage, or large-scale caching infrastructure
Strong systems programming background in Go, Rust, and/or C/C++, with the ability to engage credibly in code reviews and architectural discussions
Working knowledge of the Linux storage stack: filesystems, the page cache, block devices, and mounts
Understanding of how container images are structured, stored, and delivered at scale
Strong written and verbal communication skills, with the ability to influence without authority across teams
NICE TO HAVE
Contributions to containerd/containerd, snapshotter projects (stargz, soci, nydus, overlaybd), distribution/distribution, or closely related open-source projects
Familiarity with lazy-loading snapshotters or peer-to-peer image distribution
Experience with EROFS, FUSE, ublk, or io_uring
Experience building or operating CDN, object storage, or large-scale caching infrastructure
Experience running storage services on Kubernetes
BENEFITS
Competitive compensation, including meaningful equity
(U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents
Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
Paid parental leave
Fertility and family-building stipend through Carrot
(U.S. only) Company-facilitated 401(k)
Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.
At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Field: Technology Sub-niche: Software engineering All jobs at Baseten Jobs in California Jobs in San Francisco, CA San Francisco city guide California state guide San Francisco County economy
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This is a — role in the Technology field (Software engineering) at Baseten.
We could not match this job title to one occupation in the official wage statistics, so no pay estimate is shown. Pay is not in the source listing; ask the employer for the range and compare in the 240 job niches.
Estimate for a wage earner: 2026 federal brackets and standard deduction (IRS), Social Security 6.2% up to $184,500, Medicare 1.45% (+0.9% above $200,000 single / $250,000 married), and a state rate you set; set your own state tax rate. It ignores credits, pre-tax deductions, local taxes and benefits. Sources: IRS: tax year 2026 inflation adjustments · SSA wage base per Federal Register notice 2025-19763.
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The job is in San Francisco, CA. Below is what the official statistics say about living and working there.
How San Francisco compares. Among the 145 largest places in California, San Francisco is #4 by population, #139 for homeownership (#1 = highest), #20 for remote work (#1 = most), #25 for college degrees (#1 = most). Its county, San Francisco County, is #3 of 58 counties for average pay ($174,103).
San Francisco has 830,235 residents (ACS 2020–2024), about 2.1% of the population of California. It covers 47 square miles of land, which works out to roughly 17,784 people per square mile. The median age is 40.0, 6% older than the state median (40.0 against 37.9).
The median gross rent is $2,476 a month (rent plus utilities), 22% above the state median ($2,476 against $2,036). Over a year that is 21.1% of the median household income. US housing guidance treats housing as affordable when it takes 30% of income or less, so the typical household is under that line. The median home value is $1,394,500, which is 9.9 times the median household income ($140,970); a ratio above 5 usually means buying is out of reach without savings or a second income. 38.2% of households own their home, 32% below the state average (38.2% against 55.9%). A low ownership share points to a rental-heavy market with more apartments and shorter stays.
The San Francisco-Oakland-Fremont, CA metropolitan area has 2,373,920 jobs in the BLS wage survey, and the median pay across all occupations is $74,260 a year, 46% above the national median ($74,260 against $50,980). The biggest occupation groups are office and administrative support (226,320 jobs, median $62,120); management (223,100 jobs, median $176,340); business and financial operations (207,030 jobs, median $112,960). The area is most specialised in computer and mathematical (1.9 times) and life, physical, and social science (1.8 times), where the share of workers is well above the national share. The best-paying groups with at least 5,000 jobs are management ($176,340) and computer and mathematical ($167,770). In 2026M09 the unemployment rate of San Francisco County was 4.0%. 69.6% of residents aged 16 and over are in the labour force. The city lies in San Francisco County; its jobs, pay by industry and GDP are on the county page.
Among workers, 28.6% drive alone, 18.7% use public transit, 9.3% walk, 31.0% work from home. 21.6% of workers commute 45 minutes or more, 11% above the state average (21.6% against 19.4%). Remote work is more common than in the state as a whole, which widens the set of employers a resident can work for without moving.
60.3% of adults aged 25 and over hold a bachelor's degree or higher, 63% above the state share (60.3% against 37.1%). 34.0% of residents were born outside the United States, 26% above the state share (34.0% against 27.0%). The poverty rate is 11.2%, 7% below the state rate (11.2% against 12.0%).
The warmest month is Sep, averaging 59°F (14.9°C), and the coolest is Apr at 53°F (11.6°C). About 515 mm (20 inches) of precipitation falls in a year; Dec is the wettest month (112 mm) and Jul the driest (2 mm). These values come from NASA satellite-based climatology on a grid of about 50 km, so they describe the wider area, not a weather station in the city.
Closest cities in this guide: Daly City (31 miles, rent 3% higher); Pacifica (31 miles, rent 25% higher); San Bruno (34 miles, rent 12% higher); Millbrae (36 miles, rent 32% higher). Rent differences are against San Francisco; use the Compare tab to put any two cities side by side.
Full guide to San Francisco →California guideAll jobs in San Francisco
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Places within 50 miles (straight line) of San Francisco, CA that have a guide, with what living there costs. No pay estimate is shown because the title did not match one occupation.
| Place ⇅ | Distance from the job city ⇅ | Median rent ⇅ | Median home value ⇅ | Median household income ⇅ | Commute 45+ min ⇅ | Rent as % of occupation pay ⇅ | Open jobs there ⇅ |
|---|---|---|---|---|---|---|---|
| San Francisco, CA | 0 mi | $2,476 | $1,394,500 | $140,970 | 21.6% | n/a | Jobs |
| Daly City, CA | 31 mi | $2,551 | $1,115,000 | $123,547 | 19.6% | n/a | Jobs |
| Pacifica, CA | 31 mi | $3,098 | $1,249,800 | $160,523 | 15.5% | n/a | — |
| San Bruno, CA | 34 mi | $2,773 | $1,207,500 | $141,272 | 12.2% | n/a | — |
| Millbrae, CA | 36 mi | $3,272 | $1,934,900 | $163,476 | 21.8% | n/a | Jobs |
| Brisbane, CA | 36 mi | $2,574 | $1,232,200 | $156,169 | 29.8% | n/a | Jobs |
| South San Francisco, CA | 36 mi | $2,802 | $1,188,800 | $136,578 | 15.2% | n/a | Jobs |
| Half Moon Bay, CA | 37 mi | $2,433 | $1,549,500 | $157,884 | 26.7% | n/a | — |
| Burlingame, CA | 38 mi | $2,785 | $2,000,001 | $174,000 | 23.1% | n/a | Jobs |
| Richmond, CA | 40 mi | $1,960 | $668,200 | $95,391 | 28.8% | n/a | — |
| San Mateo, CA | 41 mi | $3,077 | $1,618,700 | $153,504 | 15.8% | n/a | Jobs |
| Berkeley, CA | 41 mi | $2,133 | $1,413,900 | $108,092 | 20.8% | n/a | Jobs |
| Alameda, CA | 42 mi | $2,474 | $1,235,700 | $137,697 | 27.3% | n/a | Jobs |
| Belmont, CA | 43 mi | $2,875 | $2,000,001 | $205,297 | 18.6% | n/a | Jobs |
| Oakland, CA | 44 mi | $1,979 | $929,900 | $101,600 | 21.9% | n/a | Jobs |
| Foster City, CA | 44 mi | $3,501 | $1,838,200 | $199,143 | 20.6% | n/a | Jobs |
| San Carlos, CA | 45 mi | $2,854 | $2,000,001 | $237,470 | 14.9% | n/a | Jobs |
| Redwood City, CA | 47 mi | $2,968 | $1,801,700 | $157,814 | 10.2% | n/a | Jobs |
| San Leandro, CA | 48 mi | $2,186 | $844,200 | $101,420 | 23.7% | n/a | — |
| Atherton, CA | 49 mi | $3,501 | $2,000,001 | $250,001 | 7.0% | n/a | — |
We could not match this title to one occupation, so here are the other sub-niches in Technology, where your skills may carry over:
Cybersecurity (760) Data, analytics and AI (3724) Cloud, IT and infrastructure (1161) Product management (935)
How far San Francisco, CA is from the 20 largest US cities, in straight-line miles (not driving distance). Click a column to sort.
| From ⇅ | Straight-line distance (miles) ⇅ | Population of that city ⇅ |
|---|---|---|
| San Francisco, CA | 0 mi | 830,235 |
| San Jose, CA | 73 mi | 990,138 |
| Los Angeles, CA | 364 mi | 3,857,263 |
| San Diego, CA | 475 mi | 1,389,526 |
| Phoenix, AZ | 678 mi | 1,642,323 |
| Seattle, WA | 684 mi | 754,195 |
| Denver, CO | 986 mi | 718,877 |
| Oklahoma City, OK | 1,420 mi | 697,125 |
| Fort Worth, TX | 1,484 mi | 963,194 |
| Dallas, TX | 1,515 mi | 1,307,930 |
| San Antonio, TX | 1,516 mi | 1,479,835 |
| Austin, TX | 1,530 mi | 979,539 |
| Houston, TX | 1,673 mi | 2,328,253 |
| Chicago, IL | 1,885 mi | 2,711,226 |
| Indianapolis, IN | 1,979 mi | 885,860 |
| Columbus, OH | 2,142 mi | 914,802 |
| Charlotte, NC | 2,332 mi | 903,844 |
| Jacksonville, FL | 2,403 mi | 977,670 |
| Philadelphia, PA | 2,550 mi | 1,579,706 |
| New York, NY | 2,602 mi | 8,483,844 |
| City ⇅ | Straight-line distance ⇅ | Open jobs ⇅ |
|---|---|---|
| Alameda, CA | 42 mi | 37 |
| Belmont, CA | 43 mi | 2 |
| Redwood City, CA | 47 mi | 47 |
| Palo Alto, CA | 54 mi | 330 |
| Sunnyvale, CA | 60 mi | 185 |
| Fremont, CA | 62 mi | 8 |
| Pleasanton, CA | 63 mi | 7 |
| Milpitas, CA | 66 mi | 15 |
| Campbell, CA | 67 mi | 7 |
| San Jose, CA | 73 mi | 898 |
| Capitola, CA | 79 mi | 1 |
| Sacramento, CA | 103 mi | 56 |
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A private-sector role in technology (software engineering) listed 26 days ago. The full description is on this page and on the employer's page.
San Francisco, CA. A remote or flexible location is listed; confirm with the employer which states are accepted.
Use the "View this job and apply on the employer's page" button. AtiveSite does not receive applications.
The listing does not publish pay, and we could not match the title to one official occupation.
Median rent in San Francisco is $2,476 a month and median household income $140,970 (ACS 2020–2024).
It was posted on 2026-10-15 (26 days ago). Jobs older than 90 days are not shown.
Yes: browse software engineering jobs in California.
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