Forward Deployed Machine Learning Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

$180K-$270K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

93% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Growth-stage From the posting source checked Jun 20, 2026

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Forward Deployed Machine Learning Engineer San Francisco (USA) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX - foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role You'll live at the intersection of cutting-edge research and brutal production reality. Your customers won't just want FLUX to work-they'll need it optimized for their specific hardware, fine-tuned for their unique use cases, and integrated into systems that weren't designed for diffusion models in the first place. What You'll Work On - Ensures FLUX models perform optimally in customer environments-whether that's on-premise GPU clusters or BFL-hosted infrastructure-balancing the eternal tension between latency and output quality - Architects deep product integrations that go far beyond "here's an API endpoint"-helping customers with everything from model hosting and deployment to inference optimization techniques that haven't made it into textbooks yet - Customizes our foundation models for visual media to solve problems customers couldn't articulate until you helped them understand what's possible - Sits in technical deep-dives with customers to diagnose performance bottlenecks, then translates those findings into solutions (and sometimes into research questions for our core team)

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