Hopper

Senior Software Engineer - AI Fintech foundation

Hopper4 days ago
Location

New York - Remote

Workplace

Remote

Type

Full Time

Salary

USD 110,000 – 300,000

Level

Senior

Role

ML Engineer

Posted

Jun 29, 2026

Full TimeRemoteSenior

The role

Summary

As a Senior Software Engineer on Hopper's Fintech Foundation team, you will design, build, and evolve machine learning systems powering the pricing engine that processes billions of dollars in travel transactions across 100+ million monthly active users and major partners like Capital One and Air Canada. This role requires 5+ years of production ML systems experience, proficiency in Python, Scala, and SQL, and expertise in data modeling, distributed systems, and pricing domain knowledge to deliver real-time, scalable pricing solutions with direct impact on revenue and financial risk management.

What you'll do

Design and implement automated training pipelines: Create reusable, scalable model training pipelines that ensure consistent delivery across Hopper's diverse partner portfolio including airlines, banks, and travel agencies. Implement version control, experiment tracking, and model registry systems to maintain reproducibility and enable rapid iteration on pricing algorithms.
Build production ETL pipelines with feature engineering: Design and implement robust Extract-Transform-Load processes with sophisticated feature engineering to prepare clean, reliable data inputs for pricing models. Focus on data quality, schema validation, and pipeline monitoring to prevent model degradation from data issues.
Develop and deploy real-time ML pricing solutions: Own the complete lifecycle from model development through production deployment of real-time pricing systems. Build inference infrastructure that can handle millions of requests per second with sub-100ms latency while maintaining pricing accuracy and business logic constraints.
Monitor and optimize production ML systems: Establish comprehensive monitoring for model latency, data drift, and training-serving skew. Continuously optimize model performance, feature staleness, and system efficiency. Set up alerting and remediation workflows to catch production issues before they impact revenue or customer experience.
Run champion-challenger pricing experiments: Design and execute A/B tests on pricing and product construction levers to identify improvements and respond rapidly to market conditions. Collaborate with product and data science teams to translate experimental insights into production changes that maximize both customer value and business outcomes.
Cross-functional collaboration and technical translation: Partner with data scientists, backend engineers, and product stakeholders to translate business requirements into well-scoped technical solutions. Communicate complex ML concepts to non-technical audiences and translate business constraints into engineering specifications, ensuring alignment across distributed teams.

What we look for

Technical

Production ML systems architecture5+ years building and maintaining machine learning systems in production environments at scale. Deep experience with model deployment, serving infrastructure, monitoring frameworks, and performance optimization in real-time systems handling high transaction volumes.
Python and Scala proficiencyExpert-level programming in Python for ML model development, data processing, and system automation. Strong Scala experience for distributed computing frameworks like Spark, with demonstrated ability to write production-grade code rather than exploratory notebooks.
Advanced SQL and data modelingExpert SQL proficiency for complex queries, data warehouse design, and analytical work. Solid understanding of data normalization, dimensional modeling, and optimization techniques to support both transactional and analytical workloads in high-volume environments.
Distributed data processing frameworksHands-on experience with Apache Spark, Airflow, or similar frameworks for processing petabyte-scale datasets. Understanding of distributed computing concepts including partitioning, shuffling, and resource optimization to handle data processing at Hopper's scale.
Machine learning algorithms and domain applicationDeep understanding of ML algorithms, statistical models, and when to apply each in real-world contexts. Specific expertise in pricing models, demand forecasting, revenue optimization, or related commercial ML domains with proven ability to connect algorithm selection to business outcomes.

Education

Computer Science or related technical fieldBachelor's degree in Computer Science, Engineering, Mathematics, Physics, or equivalent practical experience. Strong foundational understanding of algorithms, data structures, and systems design principles.

Experience

Production ML systems developmentMinimum 5+ years shipping and maintaining ML systems in production. Track record of taking models from research/prototyping through reliable, scalable deployment. Experience managing model lifecycle including retraining, versioning, and deprecation.
Large-scale pricing or fintech platform experiencePreferred experience building pricing engines, yield management systems, or financial technology platforms. Understanding of how pricing decisions impact revenue, customer acquisition, and financial risk. Experience with real-time decisioning systems processing millions of transactions.
Software engineering best practicesDemonstrated ability to apply software engineering rigor to ML systems including code review, testing strategies, CI/CD pipelines, and architectural decision-making. Track record of catching bugs and performance issues before production impact through systematic attention to detail.
Cross-functional collaboration at scaleExperience working effectively across data science, product, and engineering teams in matrix organizations. Proven ability to translate between technical and business languages, manage competing priorities, and maintain clarity when requirements are ambiguous.

Skills

Required skills

PythonProduction-grade Python development for ML systems, data processing pipelines, and backend services. Expertise in libraries like pandas, scikit-learn, and TensorFlow/PyTorch for model development.
ScalaFunctional programming expertise in Scala, particularly for distributed computing and Apache Spark-based data processing at enterprise scale.
SQLAdvanced SQL for complex analytical queries, window functions, and data warehouse optimization. Ability to write efficient queries across multiple database systems.
ML model development and deploymentEnd-to-end ML systems including feature engineering, model training, hyperparameter tuning, evaluation, and production inference deployment. Understanding of model serving frameworks and containerization.
Data pipeline and ETL developmentDesign and implementation of robust data pipelines using frameworks like Apache Airflow or Spark. Experience with data validation, quality assurance, and monitoring in production systems.
System architecture and designAbility to design scalable, reliable systems. Understanding of distributed systems concepts, microservices architecture, and performance optimization for high-throughput applications.

Nice to have

Pricing and revenue optimizationDomain expertise in dynamic pricing, revenue management, yield optimization, or similar financial applications. Understanding of business metrics and how technical decisions impact financial outcomes.
Apache Spark and distributed computingAdvanced expertise in Apache Spark for large-scale data processing, including performance tuning and optimization for batch and streaming workloads.
Data warehousing and analytics platformsExperience with modern data warehouses like Snowflake, BigQuery, or Redshift. Understanding of analytics engineering and how to build data infrastructure for both ML and business analytics.
Kubernetes and containerizationExperience with Docker, Kubernetes, and cloud-native deployment patterns for ML systems. Understanding of service orchestration and infrastructure-as-code principles.
A/B testing and experimental designExpertise in designing and analyzing A/B tests, multi-armed bandit algorithms, and statistical inference. Experience translating experiment results into actionable product decisions.
Real-time ML systemsExperience building low-latency inference systems, online learning, and streaming data processing. Understanding of real-time requirements in high-throughput transaction processing.
FinTech product knowledgeUnderstanding of financial services, insurance products, or travel fintech products. Familiarity with compliance, risk management, and customer experience in regulated environments.

Compensation & benefits

Salary

USD 110,000 – 300,000 (annual)

Stock options

Available

Benefits

Competitive equity package

Pre-IPO equity with significant upside potential as a well-funded startup backed by major institutional investors and banks. With over $750 million raised and B2B division (HTS) representing 75% of business, equity holders benefit from proven growth trajectory and clear path to future liquidity events.

Unlimited paid time off

Truly unlimited PTO policy reflecting trust-based culture. Combined with generous parental leave (above industry standards) and flexible work arrangements, supporting work-life balance and personal wellbeing.

Comprehensive health and wellness coverage

100% employer-paid Medical, Dental, and Vision coverage for employees. Additional access to Health Reimbursement Account (HRA), Dependent Care Account (DCA), Flexible Spending Account (FSA), and Disability and Life insurance protecting your financial security.

Retirement planning support

Access to 401(k) plan with employer support for long-term financial planning and retirement savings.

Travel and work flexibility stipends

Carrot Cash travel stipend for personal travel benefits reflecting Hopper's travel mission. Combined with work-from-home stipend and on-demand access to FlexDesk co-working spaces, enabling flexible work arrangements whether remote or office-based.

Entrepreneurial culture with high impact

Small, dynamic teams where pushing limits and taking calculated risks is embedded in everyday business. Direct influence on strategic decisions affecting 100+ million users and billions in annual travel transactions. Open communication channels with management and company leadership ensure your technical perspective shapes product direction.

Interview process

  1. 1
    Initial screening and technical discussion — Recruiter screen focusing on background verification, compensation expectations, and high-level technical experience overview. Follow-up technical conversation with team members to discuss production ML systems experience, specific projects, and problem-solving approach in past roles.
  2. 2
    Take-home technical assessment — Practical coding assessment focusing on ML systems design, data pipeline architecture, or pricing algorithm optimization. Typically involves Python/Scala coding and SQL queries, assessed on both correctness and production-readiness of approach.
  3. 3
    System design and architecture interview — Deep-dive discussion on designing large-scale ML systems. Topics include data pipeline architecture, model serving infrastructure, monitoring strategies, and handling real-time requirements at Hopper's scale. Focus on trade-offs, scalability considerations, and operational excellence.
  4. 4
    Domain expertise and business context discussion — Conversation exploring understanding of pricing strategies, revenue optimization, financial risk management, and how technical ML decisions impact business outcomes. Discussion of past experiences with similar domains and how you approach translating business requirements into technical specifications.
  5. 5
    Cross-functional collaboration round — Interviews with data science, product, and engineering team members to assess communication across disciplines, ability to translate between technical and business languages, and collaborative problem-solving in ambiguous situations.
  6. 6
    Team fit and final discussion — Conversation with hiring manager or engineering lead covering team dynamics, learning opportunities, growth potential, and alignment on career goals. Discussion of specific projects you would work on and how your background prepares you for the role's challenges.
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Hopper

Hopper

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Hopper is a travel booking app and online marketplace, leveraging data-driven technology to predict prices and help users book hotels, flights, and car rentals at the best rates.

Montreal, QC, CanadaFounded 2006hopper.com

Tech Stack

Languages
PythonScalaSQL
Frameworks
Apache SparkApache Airflowscikit-learnTensorFlow or PyTorchPandas
Databases
SQL data warehouses (Snowflake, BigQuery, Redshift)Distributed databasesFeature stores
Tools
MLflow or Weights & BiasesDocker and container registriesKubernetesGit and version controlMonitoring and observability tools (Datadog, Prometheus)
Other
Model serving platformsReal-time feature computationStatistical testing and causal inference
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