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Senior / Staff Software Engineer — AI / ML
Snorkel AI · New York City, NY (Hybrid); San Francisco, CA (Hybrid)
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What Snorkel AI asks for
From Snorkel AI's Senior / Staff Software Engineer — AI / ML ad, checked 5 Nov 2026.
Production ML and software ownership
At least 5 years of experience building and owning production machine learning or software systems from the first prototype to final release, including managing non-deterministic behaviors.
Statistics and experimentation
Practical grounding in experiment design, hypothesis testing, sampling, and using confidence intervals to interpret results rigorously.
LLM evaluation and routing
Improving evaluation efficiency for agentic workloads through model routing, adaptive sampling, and building judge systems to ensure quality and cost control.
Fine-tuning and serving models
Fine-tuning open-weight models using parameter-efficient methods like LoRA and serving them in production when they meet quality standards.
Python and system design
High proficiency in Python and core engineering practices including system design, code reviews, and testing.
The ad asks for 5 or more years building production ML or software systems. Based in New York City or San Francisco in a hybrid capacity.
How the example answers the ad
Four of the five requirements above, each matched to a line in the example.
LLM evaluation and routing
“Designed an automated evaluation system using LLM-as-a-judge with statistically grounded sampling to improve validation accuracy by 22 percent.”
Building an automated system using LLM-as-a-judge and sampling methods shows the evaluation efficiency component of LLM evaluation and routing.
Fine-tuning and serving models
“Fine-tuned Llama-3 models via QLoRA, achieving a 15 percent improvement in domain-specific reasoning benchmarks compared to base models.”
Fine-tuning Llama-3 models using QLoRA to improve reasoning benchmarks shows the parameter-efficient methods part of fine-tuning and serving models.
Production ML and software ownership
“Led the transition from prototype to production for a high-traffic LLM feature, resulting in a 45 percent reduction in latency for 5 million daily requests.”
Handling 5 million daily requests for an LLM feature shows production software ownership. Transitioning the system from a prototype to full release addresses the required lifecycle experience.
Statistics and experimentation
“Applied rigorous experiment design with confidence intervals to validate the impact of feature changes, leading to a 5 percent increase in fraud detection precision.”
Using confidence intervals for fraud detection validation shows the required grounding in statistics. The mention of experiment design for feature changes covers the practical experimentation part of the role.
The whole example as text
Daniel Kim · Staff ML Engineer
Summary
Staff Machine Learning Engineer with 7 years of experience in production systems and large-scale data modeling. Led the development of end-to-end LLM pipelines that reduced inference latency by 45 percent while maintaining 98 percent accuracy. Implemented adaptive model routing and automated evaluation frameworks to lower cloud operational costs by 30 percent. Engineered high-throughput Python systems for distributed fine-tuning and deployment of open-weight models using LoRA techniques.
Experience
Staff ML Engineer · Anthropic
San Francisco, CA · 2024 - Present
- Led the transition from prototype to production for a high-traffic LLM feature, resulting in a 45 percent reduction in latency for 5 million daily requests.
- Designed an automated evaluation system using LLM-as-a-judge with statistically grounded sampling to improve validation accuracy by 22 percent.
- Fine-tuned Llama-3 models via QLoRA, achieving a 15 percent improvement in domain-specific reasoning benchmarks compared to base models.
Senior ML Engineer · Stripe
South San Francisco, CA · 2022 - 2024
- Architected a distributed training framework using PyTorch that increased model iteration speed by 40 percent for risk classification teams.
- Applied rigorous experiment design with confidence intervals to validate the impact of feature changes, leading to a 5 percent increase in fraud detection precision.
ML Engineer · Pinterest
San Francisco, CA · 2020 - 2021
- Optimized ranking algorithms for user discovery, increasing content engagement by 12 percent across mobile platforms.
- Developed custom Python data loaders that reduced memory usage by 30 percent for massive recommendation datasets.
Junior ML Engineer · Adobe
San Jose, CA · 2019 - 2020
- Automated feature engineering tasks using Python scripting, decreasing data preparation time by 20 hours per week for senior staff.
- Assisted in the deployment of image classification models to production environments with containerization via Docker.
Education
MS, Computer Science · Stanford University
Stanford, CA · 2017 - 2019
- Completed a master's thesis focused on distributed optimization techniques for deep neural networks.
- Maintained a 3.9 GPA across rigorous advanced coursework in machine learning and algorithmic complexity.
BS, Electrical Engineering · University of California, Berkeley
Berkeley, CA · 2013 - 2017
- Graduated with High Honors for exceptional performance in signal processing and software design.
- Led the university robotics club in developing vision-based navigation systems for competitive autonomous vehicles.
Skills
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Frequently asked questions
Is the example from an employee?
No. It is an AI-written example for this ad, made to show how a resume can answer it.
Is this role remote?
No. The ad lists the role as hybrid in New York City or San Francisco.
What is the focus of the engineering work?
The work focuses on machine learning and LLM problems rather than infrastructure plumbing, specifically involving agent evaluation, model routing, and fine-tuning.
Where do I apply?
On the official Snorkel AI posting, through the job link on this page.
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