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About This Role
### Description
*Position at SoFi*
Employee Applicant Privacy Notice
Who we are:
Shape a brighter financial future with us.
Together with our members, we’re changing the way people think about and interact with personal finance.
We’re a next\-generation financial services company and national bank using innovative, mobile\-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.
The Role:
We are looking for an experienced Senior Staff Software Engineer to join our Builder Tools engineering organization with a mission to enable SoFi engineers to elegantly solve problems. In this role, you will have the opportunity to directly impact, influence and lead the direction and architecture of our AI\-powered software testing experience, and elevate product reliability through testing infrastructure innovations and practices. You will get the chance to lead, define, and take on complex and interesting problems as part of a fast\-paced, highly collaborative organization. The ideal candidate will be a mentor, technical leader and a team player who is hands\-on and comfortable driving solutions from initial architecture to implementation and adoption with a strong sense of ownership and drive for delivery.
What You’ll Do:
- Technical leadership \- Provide thought leadership for the technical strategy, design, implementation, delivery and operations for AI powered agentic testing (autonomous test generation, execution, failure remediation), and foundational test infrastructure (environment, data generation, multi tenancy).
- Innovate \- Collaborate with cross\-functional teams to drive innovation in testing enablement infrastructure, experience and tooling.
- Exemplary Practitioner \-Be a subject matter expert in the testing domain, including outcome KPIs and metrics, and operational excellence.
- Mentor \- Collaborate with engineers in the team, provide mentorship, and domain expertise to enhance the overall technical capabilities of the team..
- Continuous Improvement \- Contribute to creating a culture of continuous learning, data\-driven decisions and improvements. Proactivelyidentify and manage risks.
- Collaborate – Build strong working relationships with coworkers and cross\-organizational teams.
- Influence \- Influence and scale the adoption of platforms, tools and best practices across the engineering organization.
What You’ll Need:
- Experience \- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field.
- 8\+ years software development experience.
- Experience developing in a cloud environment (e.g., AWS), using containers (e.g., Docker, Kubernetes), cloud\-native technologies, service meshes (e.g., Istio, Envoy), CI/CD pipelines and automated testing.
- Expertise \- Deep knowledge of testing practices for micro\-services (e.g., multi tenancy, ephemeral test environment, test user and data generation techniques). Deep awareness of testing tools (e.g., Locust, Artillery), frameworks (e.g., Kotest, Mockito) and techniques (e.g., fuzz, property)
- 2\+ years of experience using or developing AI tools (e.g., Claude Code, Prompts, Cursor), AI infrastructure (e.g., MCP, RAGs, Vector dbs), agent frameworks (e.g. Agent SDK, Langchain, Langfuse).
- Design \- Strong understanding of software design principles, and distributed systems architecture.
- Problem solving \- Strong problem solving and programming fundamentals (algorithms, data structures).
- Coding Skills \- Proven coding skills (e.g., Java, Kotlin, Python) delivering large scale systems with infrastructure automation (e.g., Terraform).
- Project Ownership \- Ability to own, manage and deliver projects from scoping through launch. Experience working with Agile development processes.
- Strong Interpersonal skills \- Excellent written and verbal communication skills. Demonstrated ability to collaborate well with technical and non\-technical members, and proven skills to operate effectively in a cross\-functional team.
Preferred Qualifications:
- Experience with security, compliance, and risk management in cloud environments.
- Experience with monitoring and logging (e.g. Datadog, Elastic, Coralogix).
- Experience with container orchestration (e.g., Docker, Kubernetes) and networking
Compensation and Benefits
The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.
To view all of our comprehensive and competitive benefits, visit our Benefits at SoFipage!
##### Pay range: $172,800\.00 \- $297,000\.00
Payment frequency: Annual
This role is also eligible for a bonus, long term incentives and competitive benefits. More information about our employee benefits can be found in the link above.
SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.
##### The Company hires the best qualified candidate for the job, without regard to protected characteristics.
##### Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
##### New York applicants: Notice of Employee Rights
##### SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email [email protected].
##### Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time.
Internal Employees
If you are a current employee, do not apply here \- please navigate to our Internal Job Board in Greenhouse to apply to our open roles.
Salary Context
This $172K-$297K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At SoFi, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($234K) sits 31% above the category median. Disclosed range: $172K to $297K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
SoFi AI Hiring
SoFi has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Positions span Seattle, WA, US, Jacksonville, FL, US. Compensation range: $264K - $341K.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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