Senior Software Engineer - Network Enablement (Applied ML)
WFA Digital Insight
As demand for applied machine learning specialists grows, with a 25% increase in 2025, Plaid stands out for its commitment to financial transformation. The company's network covers 12,000 financial institutions, and this role is integral to amplifying those effects. With skills in remote work, digital skills, and machine learning in high demand, candidates should be prepared to showcase their ability to build and operate ML infrastructure, collaborate with cross-functional teams, and drive product hypotheses. Before applying, consider the evolving landscape of fintech and the importance of trust and intelligence in financial networks.
Job Description
About the Role
The Senior Software Engineer - Network Enablement (Applied ML) role at Plaid is a critical position focused on building and operating the machine learning infrastructure that enables trust and intelligence across Plaid's vast network. This role entails working closely with various teams, including Product, ML/Data Platform, Fraud, Foundational Modeling, MLE, DS, and Privacy, to scale network intelligence. The successful candidate will be responsible for embedding model inference into product flows and decision logic, defining and instrumenting success metrics, and designing experiments to validate product hypotheses.As part of the Network Enablement team, the senior software engineer will play a pivotal role in amplifying Plaid's network effects by fostering trust and sharing intelligence with data partners. This involves building Trust & Fraud Insights, Bank Intelligence, and the ml/data foundations that underpin these capabilities. The team's mission is to empower the transformation of financial interactions, making it easier for people to connect their financial accounts to the apps and services they want to use.
Plaid's commitment to innovation and its extensive network covering 12,000 financial institutions across the US, Canada, UK, and Europe make this role both challenging and rewarding. The company has established itself as a leader in the fintech space, working with thousands of companies, including Venmo, SoFi, and many of the largest banks, to provide the tools and experiences that developers need to create their own products.
What You Will Do
- Embed model inference into Network Enablement product flows and decision logic (APIs, feature flags, backend flows)
- Define and instrument product + ML success metrics (fraud reduction, retention lift, false positives, downstream impact)
- Design and run experiments and rollout plans (backtesting, shadow scoring, A/B tests, feature-flagged releases) to validate product hypotheses
- Build and operate offline training pipelines and production batch scoring for bank intelligence products
- Ship and maintain online feature serving and low-latency model inference endpoints for real-time partner/bank scoring
- Implement model CI/CD, model/version registry, and safe rollout/rollback strategies
- Monitor model/data health: drift/regression detection, model-quality dashboards, alerts, and SLOs targeted to partner product needs
- Ensure offline and online parity, observability, and compliance as you scale network capabilities
- Collaborate closely with cross-functional teams to scale network intelligence
What We Are Looking For
- 5+ years of experience in software engineering, preferably in a role involving machine learning or data science
- Strong background in computer science, with proficiency in programming languages such as Python, Java, or C++
- Experience with machine learning frameworks and tools, such as TensorFlow or PyTorch
- Knowledge of cloud computing platforms, such as AWS or Google Cloud
- Excellent problem-solving skills, with the ability to analyze complex problems and develop innovative solutions
- Strong communication and collaboration skills, with experience working in cross-functional teams
- Experience with Agile development methodologies and version control systems like Git
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field
Nice to Have
- Experience with containerization using Docker and container orchestration using Kubernetes
- Familiarity with DevOps practices and tools, such as Jenkins or CircleCI
- Knowledge of cybersecurity principles and practices, particularly in relation to machine learning and data science
- Experience working with large datasets and distributed computing systems
- Certification in machine learning or a related field, such as Certified Data Scientist or Certified Machine Learning Engineer
Benefits and Perks
- Competitive salary and equity package
- Comprehensive health, dental, and vision insurance
- Flexible PTO policy and remote work options
- Professional development opportunities, including training and conference attendance
- Access to cutting-edge technologies and tools
- Collaborative and dynamic work environment
- Opportunity to work on high-impact projects that transform the financial services industry
- Recognition and reward for outstanding performance and contributions
How to Stand Out
- Tip: Showcase your understanding of machine learning principles and experience with ML frameworks in your portfolio and during interviews.
- To stand out, highlight projects that demonstrate your ability to collaborate with cross-functional teams and drive product hypotheses.
- Be prepared to discuss your approach to model CI/CD, version registry, and rollout strategies, as well as how you handle model drift and regression detection.
- When negotiating salary, consider the current market demand for applied ML specialists and the company's overall compensation package, including equity.
- Red flag: If the company seems overly focused on short-term product delivery without considering long-term network effects and trust building, it may indicate a lack of strategic vision.
This is a remote position listed on WFA Digital, the platform for professionals who work from anywhere. Browse more remote jobs across all categories.