Member of Technical Staff

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

$175K-$220K not verified - source not recorded; timestamp not recorded

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

69% 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.

Schedule

Shift type

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Weekend work

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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

Member of Technical Staff New York, NY About Us: At Fireworks, we're building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We've been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We're an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI. Job Duties: Design, develop, and maintain large-scale backend and cloud-native infrastructure to support distributed machine learning training, inference, and data processing pipelines for generative AI platform. Architect and build scalable, resilient backend infrastructure to support distributed training, inference, and data processing pipelines. Lead technical design discussions, mentor engineers, and establish best practices for large-scale machine learning systems. Design and implement core backend services with a focus on efficiency and low latency. Drive infrastructure optimization initiatives for compute cost, storage lifecycle management, and network performance. Collaborate with machine learning, DevOps, and product teams to translate research and product requirements into robust infrastructure solutions. Evaluate and integrate cloud-native and open-source technologies such as Kubernetes, Ray, Kubeflow, and MLFlow to enhance platform reliability. Own end-to-end systems from design to deployment, emphasizing reliability, fault tolerance, and operational excellence. Minimum Education & Experience Required : Bachelor's degree or equivalent in Computer Science or related field

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