We are looking for a Senior Staff Software Engineer for Application Engineering. As a Sr Software Engineer on the Applications Engineer team, you’ll be part of a cross-functional team whose mission is to deliver real-world applications and value from the leap forward in computing enabled by quantum technology. 

We don't just build the world's best quantum computers - we apply cutting edge quantum computing tools to real world problems. Until now, our team of Applications Scientists have been building prototypes, working closely with our customers. Our new department, Applications Engineering, will ultimately help transform proven quantum computing use-cases pioneered by our scientists into production applications, making those innovations available to more people and businesses by packaging and deploying these tools as SaaS applications.

But first, we must mature existing proof-of-concept quantum code into production grade applications. As a hands-on team lead for a new engineering team, you will work on enhancing developer experience (DevEx) by applying best practices to research-grade code. You will also set up CI / CD pipelines, software packing tools, and pre/post flight tests for new algorithms as they go into deployment. Where possible you will also be in charge of optimizing and parallelizing traditional algorithms, to reduce pre-processing and post-processing workloads. You will spearhead a new team taking newly invented quantum applications, and turning them into well defined modules of reliable SaaS applications. Think of it as being a forward-deployed engineer for quantum computing applications; the strongest candidates will be generalists with wide-ranging interests. 

This team will work closely with Developer Tools, Infrastructure, and Product teams to solve problems by choosing the right technologies, and standardizing and automating those solutions. The right engineer will be comfortable working on refining chemistry simplification via Quantum Computing tool one quarter, and turning a novel quantum topology solver into a service the next - and love pair programming with scientists. They’ll bring the quantum expertise; you bring the deep and broad experience in classical programming.

Responsibilities:

• Partner with Application scientists to help deliver cutting-edge solutions for customers
• Elevate Applications code quality generally through architectural review, code review, standards, and best practices
• Improve Applications pace of delivery by creating CI / CD pipelines and other packaging automation, as well as test infrastructure and coverage
• Help productize computing code, especially improving data processing in pre-quantum and post-quantum processing steps
• Work closely with peers in Applications, Developer Tools, and Product teams to share best practices and feedback on internal tools
• Reporting to the Director of Application Engineering, help hire and mentor developers on the team

You’d be a good fit with:

• 12+ years of experience as a backend or full-stack developer, preferably in Python or Golang
• 5+ years of experience in setting up and maintaining CI / CD pipelines, in Github Actions, Jenkins, or similar tools
• 5+ years of experience in C/C++ or Rust
• 3+ years of experience in Docker, k8s, or similar containerization technologies
• A track record of shipping containerized applications that solve real business problems
• Previous work in scientific computing, quantum computing, or a related field
• Daily usage of augmented coding tools like Claude or Gemini 

You’d be a great fit with:

• 5+ years of experience in scientific computing, especially with data analysis using numpy, pandas, or similar
• Experience in HPC or performance optimization
• Experience in converting traditional algorithms into CUDA, OpenMP workflows, or similar parallelization experience
• Experience with workflow orchestration engines like Airflow, Dagster, or Temporal

Location: This position can work onsite or hybrid from our office in Bothell, WA; College Park, MD; or Boulder, CO
Travel: 5% for quarterly travel for collaboration 
Job ID:  1312

The approximate base salary range for this position is $192,979 - $252,659. The total compensation package includes base, bonus, and equity.

ABOUT THE TEAM

Anduril's Air & Missile Defense Radar team develops cutting-edge tracking algorithms and software systems that detect, track, and characterize airborne threats in real-time. We're building the next generation of tracking intelligence capabilities—automated analysis systems that understand tracking performance, identify failure modes, and continuously improve our algorithms through data-driven insights.

This role sits at the intersection of ML engineering and tracking domain expertise. You'll build end-to-end pipelines that ingest tracking algorithm telemetry, analyze correlation failures and performance anomalies, train models to automate root cause analysis, and deploy production tools that help engineers ask questions like "why didn't track X and track Y associate?" We don't just track targets; we track our tracking systems and make them smarter.

WHAT YOU'LL DO

• Own tracking intelligence infrastructure end-to-end: Build the platform for ingesting tracking algorithm telemetry (hypotheses, scores, gains, association decisions), feature engineering performance metrics, training analysis models, and deploying them into production
• Automate tracking analysis: Develop ML models that identify correlation failures, track quality degradation, and root causes for tracking anomalies—replacing manual deep-dive investigations with scalable automated insights
• Build autotuning capabilities: Create systems that recognize incoming data characteristics and automatically adjust tracking algorithm parameters, frame rates, and model configurations for optimal performance
• Design human-in-the-loop tools: Build interfaces and query services that let engineers ask natural questions about tracking behavior and get data-driven answers backed by your models
• Exploit tracking telemetry: Instrument C++ tracking algorithms with appropriate logging (working with platform engineers), then marshal that data into consistent formats for analysis and model training
• Deploy in constrained environments: Package and deploy models for air-gapped systems with no external connectivity, following security scanning requirements where ML models are treated as data artifacts
• Manage the ML lifecycle: Handle data catalogs, ground truth labeling, model registries, versioning, and validation—ensuring models improve tracking performance in measurable ways
• Bridge domains: Translate between tracking algorithm fundamentals (Kalman filters, data association, multi-hypothesis tracking) and ML/data science techniques to build solutions that actually work
• Drive make/build decisions: Evaluate when to build custom models vs. leverage existing ML capabilities, selecting appropriate algorithm architectures for tracking intelligence problems
• Work hands-on-keyboard: This is a one-person show initially—you'll architect, code, deploy, and iterate rapidly using modern Python-based ML tooling

REQUIRED QUALIFICATIONS

• 3+ years of experience with a strong mix of ML engineering and data science—you've built models AND deployed them into production systems
• Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Experience with MLOps practices: data pipelines, feature engineering, model versioning, experiment tracking, and deployment workflows
• Familiarity with ML infrastructure tooling (MLflow, Dagster/Airflow, or similar orchestration tools)
• Understanding of tracking, estimation, or filtering algorithms (Kalman filters, data association techniques)—you need to understand what tracking algorithms output and why they make the decisions they do
• Ability to work with streaming time-series data and engineer features from algorithm telemetry
• Experience building data catalogs, managing ground truth labels, and validating model performance
• Strong software engineering fundamentals—you can build maintainable, production-quality code independently
• Comfortable working in C++ environments enough to add instrumentation/logging (no deep algorithm development required)
• Ability to obtain and maintain a U.S. Top Secret SCI security clearance

PREFERRED QUALIFICATIONS

• Experience deploying ML models in edge, embedded, or air-gapped environments with security constraints
• Background in defense, aerospace, or sensor systems
• Familiarity with containerization (Docker, Kubernetes) for model serving and deployment
• Experience with anomaly detection, root cause analysis, or automated diagnostics systems
• Knowledge of AutoML, hyperparameter tuning, or online learning techniques
• Understanding of radar systems, sensor fusion, or signal processing
• Experience building conversational or query interfaces for technical systems
• Familiarity with model registries and model-as-data artifact management
• Experience with distributed data processing (Spark, Dask) for large-scale telemetry analysis
• Formal coursework or training in MLOps, data science, or estimation theory
• Active U.S. Top Secret SCI clearance

About the opportunity

We’re looking for an experienced Analytics Engineer to help build the next generation of Contentful’s analytics foundation, powering Insight 360, our unified Snowflake-based data platform.

In this role, you’ll design and maintain robust, scalable data models that make our business data accessible and actionable across all teams. You’ll collaborate with engineers, analysts, and business stakeholders to deliver high-quality, trusted datasets that enable data-driven decision-making across the company.

Your work will sit at the heart of Contentful’s data evolution, helping us move to a modern, governed, and insight-led architecture.

What to expect?

Data Modeling & Transformation

• Design, build, and maintain efficient, scalable analytics models in dbt and Snowflake.
• Transform raw data from diverse domains (product, finance, sales, marketing, operations) into analytics-ready structures.
• Apply best practices in modularity, documentation, and testing to ensure long-term reliability.

Pipeline Ownership

• Take ownership of analytics engineering projects end-to-end: from scoping requirements through implementation and monitoring.
• Develop reusable, maintainable data transformations that enable self-serve analytics in Tableau and other BI tools.

Enablement & Collaboration

• Partner with analysts, data scientists, and business teams to define and deliver trusted data assets.
• Collaborate with data platform engineers to optimize performance, governance, and cost efficiency in Snowflake.
• Contribute to the shared data catalog and drive adoption of best practices across analytics and engineering teams.

What you need to be successful?

Required

• Experience: 3+ years in analytics engineering, data engineering, or BI development in a modern data stack environment.
• Data Modeling: Proven expertise with SQL to build modular and well-documented data models in dbt. Understanding of various data modeling techniques.
• Warehouse Experience: Deep knowledge of Snowflake (warehouses, roles, virtual warehouses, performance tuning).
• Collaboration: Strong ability to work cross-functionally with analysts, engineers, and business stakeholders to translate needs into technical solutions.
• Data Ops: Experience applying version control, testing, and observability practices in dbt or similar frameworks.

Preferred

• Visualization: Experience structuring data for BI tools such as Tableau, focusing on scalability and performance.
• Ingestion & Orchestration: Familiarity with Airflow, or similar tools for data ingestion and orchestration.
• Reverse ETL: Working knowledge of tools like Hightouch for operationalizing insights back into business systems (e.g., Salesforce, Gainsight).
• Governance: Understanding of data cataloging, lineage, and documentation using tools like Atlan

What's in it for you?

• Join an ambitious tech company reshaping the way people build digital experiences
• Full-time employees receive Stock Options for the opportunity to share in the success of our company
• Comprehensive healthcare package covering 100% of monthly health premiums for employees and 85% of costs for your dependents. 
• Fertility and family building benefits, including a lifetime reimbursable wallet to support your growing family.
• We value Work-Life balance and You Time! A generous amount of paid time off, including vacation days, sick days, compassion days for loss,  education days, and volunteer days
• Company paid parental leave to care for and focus on your growing family 
• Use your personal annual education budget to improve your skills and grow in your career
• Enjoy a full range of virtual and in-person events, including workshops, guest speakers, and fun team activities, supporting learning and networking exchange beyond the usual work duties 
• An annual wellbeing stipend to care for your physical, financial, or emotional health
• A monthly communication stipend and phone hardware upgrade reimbursement.
• New hire office equipment stipend for hybrid or distributed employees. Get the gear you need to work at your best.

This role will need to be conducted in a state in which we are currently registered to do business. In addition, this position is not eligible for visa sponsorship. Applicants must be authorized to work without the need for visa sponsorship by the start date of employment.

This position is eligible for equity awards in accordance with the terms of Contentful’s equity plans.

The application deadline is 05/21/2026

Colorado Salary Statement: The salary range displayed is specifically for those potential hires who will work or reside in the state of Colorado if selected for the role. Any offered salary is determined based on internal equity, internal salary ranges, market data/ranges, applicant's skills and prior relevant experience, certain degrees and certifications (e.g. JD/technology), for example.

Colorado Salary: $119,000- $161,000

About the opportunity

We are looking for a Data Scientist who will turn data into insights and build models that drive meaningful business outcomes. You will work closely with cross-functional stakeholders to understand problems, design analytical solutions, and deploy data products that scale.

What to expect?

• Collect, clean, and analyze structured and unstructured data.
• Develop predictive models, statistical analyses, and machine learning algorithms.
• Build dashboards, reports, and data visualizations to communicate insights.
• Collaborate with engineering to deploy models into production environments.
• Perform exploratory data analysis (EDA) to identify trends and opportunities.
• Conduct A/B testing and design experiments to measure feature performance.
• Document methodologies and ensure reproducibility of analytical work.
• Contribute to end-to-end machine learning workflows, including model training,  validation, and deployment, working with established tools and patterns and with support from engineering.
• Monitor model performance and data quality, and update models as needed.

What do you need to be successful?

Required

• Bachelor’s or Master’s in Data Science, Computer Science, Statistics, Mathematics, or related field.
• Strong proficiency in Python (pandas, scikit-learn, NumPy).
• Experience using SQL to work with analytical databases and data warehouses.
• Solid understanding of statistical modeling, model evaluation, and experimental methods.
• Familiarity with machine learning techniques (regression, classification, clustering, etc.).
• Hands-on experience building and running machine learning models in production or production-like environments.
• Experience building dashboards with tools like Tableau, or similar.
• Strong analytical and problem-solving skills.
• Ability to balance analytical rigor with practical delivery.
• Excellent communication skills with the ability to translate data insights into business actions.

Preferred    

• Familiarity with workflow orchestration or model lifecycle tools (e.g., Airflow, MLflow) at a practical, working level.
• Implement practical model deployment approaches (e.g., batch inference, scheduled retraining) using existing team tools and patterns.
• Experience with cloud platforms (AWS, or similar).
• Knowledge of ML Ops tools (MLflow, Airflow, or similar).
• Exposure to big data technologies (Redshift, Snowflake).
• Experience working in agile environments.

What's in it for you?

• Join an ambitious tech company reshaping the way people build digital experiences
• Full-time employees receive Stock Options for the opportunity to share in the success of our company
• Comprehensive healthcare package covering 100% of monthly health premiums for employees and 85% of costs for your dependents. 
• Fertility and family building benefits, including a lifetime reimbursable wallet to support your growing family.
• We value Work-Life balance and You Time! A generous amount of paid time off, including vacation days, sick days, compassion days for loss,  education days, and volunteer days
• Company paid parental leave to care for and focus on your growing family 
• Use your personal annual education budget to improve your skills and grow in your career
• Enjoy a full range of virtual and in-person events, including workshops, guest speakers, and fun team activities, supporting learning and networking exchange beyond the usual work duties 
• An annual wellbeing stipend to care for your physical, financial, or emotional health
• A monthly communication stipend and phone hardware upgrade reimbursement.
• New hire office equipment stipend for hybrid or distributed employees. Get the gear you need to work at your best.

This role will need to be conducted in a state in which we are currently registered to do business. In addition, this position is not eligible for visa sponsorship. Applicants must be authorized to work without the need for visa sponsorship by the start date of employment.

This position is eligible for equity awards in accordance with the terms of Contentful’s equity plans.

The application deadline is 05/21/2026

Colorado Salary Statement: The salary range displayed is specifically for those potential hires who will work or reside in the state of Colorado if selected for the role. Any offered salary is determined based on internal equity, internal salary ranges, market data/ranges, applicant's skills and prior relevant experience, certain degrees and certifications (e.g. JD/technology), for example.

Colorado Salary: $136,000-$184,000

About the opportunity

We are seeking a Data Engineer to help design, build, and scale modern data solutions that power analytics, operational reporting, and business decision-making across the company. You will work closely with our data, analytics, and business teams to develop high-quality data pipelines, models, and integrations that deliver clean, reliable, and actionable data.

You will join a small, cross-geography engineering team operating with a product mindset - delivering iterative value, partnering closely with business and analytics stakeholders, and owning both build and run responsibilities of a mission-critical platform.

What to expect?

• Design, build, and maintain scalable data pipelines and transformations across multiple systems and sources.
• Develop high-quality data models that support analytics, reporting, and operational use cases.
• Partner with analytics, product, and business stakeholders to understand data needs and translate them into technical solutions.
• Implement strong data quality, validation, and monitoring processes to ensure reliability and trust in the data.
• Optimize data storage, processing, and performance within cloud data warehousing environments.
• Contribute to the development and evolution of our modern data platform, including tooling, standards, and best practices.
• Support data platform operations—troubleshooting issues, improving reliability, and ensuring SLAs are met.
• Collaborate with cross-functional partners on governance, documentation, definitions, and data stewardship.
• Implement CI/CD practices and Infrastructure-as-Code (IaC) to automate deployment, testing, and environment management.
• Participate in code reviews, design discussions, and operational on-call rotations as needed.

What do you need to be successful?

Required

• 3–6+ years of experience in Data Engineering or a similar technical role.
• Strong SQL skills and experience working with cloud data warehouses (e.g., Snowflake, Redshift, BigQuery).
• Experience building and maintaining ETL/ELT pipelines using tools such as dbt, Airflow, or similar frameworks.
• Proficiency in Python or another scripting language.
• Strong understanding of data modeling, data structures, and modern data architecture patterns.
• Experience with CI/CD workflows, version control (Git), and Infrastructure-as-Code tools (Terraform a plus).
• Familiarity with data governance, quality, lineage, and cataloging concepts and tools.
• Demonstrated ability to collaborate with analysts, engineers, and business partners in a cross-functional environment.
• Excellent communication skills and a product-focused mindset.

Preferred

• Experience with monitoring/observability tools (e.g., Monte Carlo, Datadog).
• Experience with BI tools such as Tableau or Looker.
• Background in distributed systems, APIs, or event-based architectures.
• Exposure to data security, compliance, or privacy frameworks.
• Experience utilizing AI for development.

What we're looking for

• A builder who enjoys solving complex data challenges with clean, reliable, scalable solutions.
• A collaborator who thrives in cross-functional environments and communicates clearly.
• A problem-solver who cares about quality, consistency, and long-term maintainability.
• Someone who wants to make a measurable impact by empowering data-driven decision-making across the company.

What's in it for you?

• Join an ambitious tech company reshaping the way people build digital experiences
• Full-time employees receive Stock Options for the opportunity to share in the success of our company
• Comprehensive healthcare package covering 100% of monthly health premiums for employees and 85% of costs for your dependents. 
• Fertility and family building benefits, including a lifetime reimbursable wallet to support your growing family.
• We value Work-Life balance and You Time! A generous amount of paid time off, including vacation days, sick days, compassion days for loss,  education days, and volunteer days
• Company paid parental leave to care for and focus on your growing family 
• Use your personal annual education budget to improve your skills and grow in your career
• Enjoy a full range of virtual and in-person events, including workshops, guest speakers, and fun team activities, supporting learning and networking exchange beyond the usual work duties 
• An annual wellbeing stipend to care for your physical, financial, or emotional health
• A monthly communication stipend and phone hardware upgrade reimbursement.
• New hire office equipment stipend for hybrid or distributed employees. Get the gear you need to work at your best.

This role will need to be conducted in a state in which we are currently registered to do business. In addition, this position is not eligible for visa sponsorship. Applicants must be authorized to work without the need for visa sponsorship by the start date of employment.

This position is eligible for equity awards in accordance with the terms of Contentful’s equity plans.

The application deadline is 05/23/26

Colorado Salary Statement: The salary range displayed is specifically for those potential hires who will work or reside in the state of Colorado if selected for the role. Any offered salary is determined based on internal equity, internal salary ranges, market data/ranges, applicant's skills and prior relevant experience, certain degrees and certifications (e.g. JD/technology), for example.

Colorado Salary Range: $119,000-$161,000

Shopmonkey's vision is to help every shop thrive by equipping them with the tools they need to run and grow their business. Our cloud based all-in-one shop management software takes owners and technicians from quote to cashing out a satisfied customer. Our software has a modern and intuitive UI and our backend is powered by the latest technologies so our clients can focus on the things they do best. As a Senior ML Engineer at Shopmonkey, you will be a part of a globally distributed engineering team working closely with your product and design counterparts. You will have the chance to work on the frontier of ML modeling, data pipelines, MLOps, and agentic AI systems to meet real-world auto shop needs. Shopmonkey has the structured data, workflows, and operational maturity to deliver ML powered systems that are not only intelligent but trusted and useful. You’ll move fast to bring ML systems from discovery all the way through production, helping to shape the future of the automotive care experience. For any Bay area based candidates, this would be hybrid with 2-3 days/week office at our Morgan Hill, CA office for collaboration. 

What you will do:

• Design, build, and ship production-ready ML models across a range of problem spaces: regression, classification, clustering, ranking, and recommendation systems.
• Conduct end-to-end development of ML systems: data gathering, experimentation, feature engineering, model training, evaluation, deployment, and monitoring.
• Define and track model performance metrics, run A/B tests, and iterate based on real-world feedback.
• Help design and implement shared feature stores so that reusable features can serve multiple models consistently in both batch and real-time contexts.
• Work within a modern MLOps environment to ensure scalable and reliable deployment of models.
• Contribute to training infrastructure, model versioning, and CI/CD pipelines for ML workflows.
• Work closely with data scientists and data engineers to develop data driven solutions that are high impact for businesses.
• Translate complex ML workflows into digestible updates for cross-functional stakeholders.
• Contribute to backlog velocity by owning appropriate tickets and delivering high-impact work in a collaborative, fast-paced environment.
• Implement NLP and LLM-powered components for sentiment analysis, real-time conversation evaluation, and behavior optimization.
• Contribute to analytics and predictive features such as no-show prediction and sentiment dashboards.
• Help build and ship AI agents that help automate key auto-shop business processes.

We are looking for people who have:

• Minimum of 5+ years of industry experience in applied machine learning; advanced degrees (Master’s or PhD) may offset years of experience.
• Proven experience shipping models into production (not just proof-of-concepts or notebooks).
• Proficiency in Python; experience with ML frameworks like PyTorch or Tensorflow.
• Strong foundations in classical ML/DL. Including some of the following: regression, classification, clustering, ranking, feature engineering, model evaluation, and experimentation.
• Bachelor’s degree in a STEM field, or equivalent practical experience.
• Strong collaboration and communication skills—comfortable working with PMs, designers, engineers and other cross functional team members.

Nice to have:

Candidates should have some experience in one or more of the following areas.

• Understanding of MLOps principles: model versioning, orchestration, evaluation, monitoring, model serving, and CI/CD for ML. 
• Understanding of MLOps, and experience with modern tooling like MLFlow, DVC, Airflow, etc.
• Experience with LLMs and NLP frameworks (e.g., TensorFlow, Hugging Face, LangChain).
• Experience with/interest in LLM workflows and agentic workflows
• Cloud infrastructure experience, (e.g. GCP, AWS).
• Familiarity with vector databases (e.g. Pinecone, pgvector) and embedding-based retrieval or similarity search.
• Strong SQL skills for working with large-scale data.
• Experience designing or contributing to feature stores (e.g. Feast, VertexAI Feature Store, Tecton) for shared, reusable feature pipelines.

Bonus Points:

• Prior experience working at a high growth startup.
• Experience building consumer-facing agents in vertical SaaS, in the automotive industry (business or consumers).
• Background in data processing or real-time analytics.
• Experience with Snowflake or other large-scale data warehouse solutions.

In the United States, the range is typically a salary of $165,000 to $200,000 + bonus + equity + benefits. The range provided is Shopmonkey’s reasonable estimate of the base compensation for this role. The actual amount will be based on job-related and non-discriminatory factors such as location, experience, training, skills, and abilities. Consult with your Recruiter during the initial call to determine a more targeted range based on these job-related factors. In addition to this base compensation company stock options and benefits as outlined below are included.

About your role

We are looking for a forward-thinking Data Engineering Intern to join our team for Summer 2026. This isn't just a role about moving data from point A to point B; it’s about rethinking how those pipelines are built. You will sit at the intersection of core infrastructure and AI, building robust ETL processes while leveraging AI technologies to automate the very workflows you create.

Your mission is dual-purpose: you will support our Business Intelligence efforts and directly influence our product roadmap by turning raw data into actionable insights through advanced dashboards and automated workflows.

A Day in the Life

• Build & Orchestrate: Design, develop, and maintain ETL pipelines to ingest data into our Snowflake warehouse using Python, SQL, and Airflow.
• AI-Driven Automation: Implement AI-powered solutions to streamline engineering tasks, including:
• Automating code generation and documentation.
• Building AI-driven data quality checks and anomaly detection.
• Developing "self-healing" pipelines that can identify and alert on ingestion errors.
• Insight Generation: Use Jupyter Notebooks and Streamlit to analyze data and build internal tools that help our product team make data-driven decisions.
• Visualization: Create high-impact dashboards in Tableau that translate complex data into a clear narrative for stakeholders.
• Agile Collaboration: Participate in daily Scrum huddles, manage tasks via Jira, and work closely with product owners and QA to promote code to production.
• Cloud Infrastructure: Interact with cloud services via CLI and manage containerized environments using Docker and Kubernetes.

What you’ll need

• Academic Status: Currently pursuing an undergraduate degree with a targeted graduation date in 2026 or early 2027, or pursuing a degree in Computer Science, Data Science, or a related quantitative field.
• Programming Mastery: Expertise in Python and SQL
• Infrastructure Mindset: A strong understanding of Data Warehousing (Snowflake) and ETL orchestration (Airflow).
• DevOps Curiosity: Familiarity with CLI, Docker, and Kubernetes for managing cloud-based environments.
• Analytical Toolkit: Experience with Jupyter Notebooks, Tableau, or Streamlit.
• Problem Solver: A proactive approach to using AI/LLMs to automate repetitive tasks and improve system reliability.

The Summer "Win"

By the end of your 10-week internship, you will have designed and deployed a functional ETL pipeline that feeds into a custom Insights Dashboard. Your work won't just sit in a folder, it will be used to influence our official product roadmap.

Interview Process

• Recruiter Phone Screen
• Technical Assessment 
• Hiring Manager Interview
• Team Interview

Job Description  

We automate the most tedious parts of the data stack—so data arrives in the warehouse ready to query, transform, and power insight. No maintenance, no manual effort, just trusted data delivered at scale.

Today, thousands of the world’s most data-driven companies—spanning Fortune 500s to high-growth startups—rely on Fivetran to move billions of records daily and execute millions of transformations through our platform. With ever-increasing demand for data in AI and analytics workflows, we’re building the infrastructure to power the next generation of intelligent business.

About the Role

As a Product Manager on our Enterprise Platform team, you’ll shape the foundation that makes Fivetran scalable, secure, and extensible for thousands of enterprise customers. 

You’ll lead key initiatives across the platform–enterprise-grade security and governance, user and workspace configuration, and observability tooling—ensuring customers can confidently configure, govern, and scale Fivetran usage.

This is a high-impact, high-visibility role, perfect for product leaders who thrive on technical challenges, tight feedback loops, and opportunities to shape long-term platform strategy. You’ll balance usability and self-service with enterprise-grade requirements like scale, security, and governance.

This is a full-time position based out of our Denver, CO office. Our hybrid work model offers a blend of remote flexibility and in-person collaboration, including two days in the office each week to connect and build as a team.

What You’ll Do

• Define and drive the vision, strategy, and roadmap for key platform features

• Partner closely with engineering and design to define and deliver robust, intuitive functionality that meets the needs of enterprise customers.

• Understand customer needs by engaging directly with users, analyzing feedback and usage data, and collaborating with support, sales, and solutions engineering.

• Drive cross-functional alignment and go-to-market readiness for major product launches.

• Stay on the pulse of trends in developer platforms, data infrastructure, and enterprise tooling—with an eye toward AI-first opportunities.

Skills We’re Looking For

• Product management experience in B2B SaaS or cloud platforms.

• A strong technical foundation—comfortable working on APIs, backend systems, developer tools, or security platforms.

• A track record of owning complex initiatives and delivering high-impact outcomes for technical users.

• Exceptional collaboration and communication skills across product, engineering, and go-to-market teams.

• Analytical mindset and comfort with ambiguity in a fast-paced environment.

• Ability to thrive in a fast-paced, high-growth environment.
   

Bonus Skills

• Familiarity with modern data infrastructure and tooling (e.g., ELT/ETL, dbt, Airflow, Dagster, Snowflake, Databricks).

• Experience with enterprise security and compliance standards (e.g., SSO, RBAC, audit logging, SOC2).

• Prior work on developer tools, SDKs, or extensibility platforms.

• Hands-on experience with cloud platforms (AWS, Azure, GCP).

• Prior experience in a startup or fast-paced environment.

#LI-HYBRID #LI-KM3

About the Opportunity

We are seeking a Senior Software Engineer to design, build, deploy, monitor, and optimize production-ready ML services in regulated healthcare. You will work hands-on to package, test, orchestrate, deploy, and maintain ML models, improve workflows, and implement CI/CD and automated testing to ensure reliability, performance, and faster delivery of business value.

Responsibilities

• Design, build, and deploy scalable AI/ML services with clear service boundaries, making pragmatic trade-offs across performance, maintainability, security, and long-term extensibility.

• Own full lifecycle delivery of complex features from architectural planning and API design to implementation and post-release observability.

• Define and enforce robust verification and validation for AI services including unit, integration, and end-to-end testing to ensure reliability of code in production and compliance with our Quality Management System and regulatory standards. 

• Integrate CI/CD and MLOps practices for automated model builds, testing, and deployment.

• Translate product requirements into well-defined technical designs proactively shaping implementation details.

• Contribute to the necessary technical documentation required for regulatory submissions.

• Collaborate cross functionally to reduce knowledge silos and ensure continuity in critical systems through cross-training and documentation. 

Requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field

• 7+ years of software engineering experience, with expertise in AI production systems (Python, PyTorch) and data services (SQL, Postgres, NoSQL, Redis or similar).

• Experience with unit, integration testing for ML models, including data validation, correctness checks, and reproducibility.

• Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms(SageMaker, Databricks, Unity Catalog, Snowflake/Snowpark) to build scalable ML data pipelines and model workflows.

• Strong collaboration skills to work effectively with ML scientists, engineers, and regulatory teams.

• Comfortable operating in ambiguity and driving clarity across product, engineering, and business stakeholders.

This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges:

• Base Salary: $153,000 - $179,000
• TTC: $176,000 - $206,000

*Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.

About the Opportunity

We are seeking a senior machine learning software engineer to design, build, deploy, monitor, and optimize production-ready ML services in regulated healthcare. You will work hands-on to package, test, orchestrate, deploy, and maintain ML models, improve workflows, and implement CI/CD and automated testing to ensure reliability, performance, and faster delivery of business value.

Responsibilities

• Collaborate with AI scientists to package and deploy ML models, ensuring reproducibility, versioning, and compliance.
• Build and maintain model serving infrastructure including monitoring, drift detection, automated retraining, and logging.
• Implement unit, integration, and system-level testing for ML models, covering data validation, model correctness, and deployment workflows.
• Develop and operate end-to-end ML pipelines: ingestion → preprocessing → feature engineering → evaluation → deployment → monitoring.
• Integrate CI/CD and MLOps practices for automated model builds, testing, and deployment.
• Identify and resolve workflow inefficiencies or gaps between research and production.
• Recommend and integrate frameworks, libraries, and infrastructure to improve pipeline efficiency, maintainability, and observability.
• Collaborate cross-functionally to ensure compliance with regulatory requirements (FDA/HIPAA) in production ML workflows.
  
  

Requirements

• 7+ years of experience in software engineering for ML production or ML platform delivery.
• Hands-on experience deploying ML models via APIs, batch pipelines, or streaming inference.
• Proficiency in Python (required), Java, or similar, with software engineering best practices for ML workflows.
• Experience with unit, integration, and pipeline-level testing for ML models, including data validation, correctness checks, and reproducibility.
• Familiarity with cloud platforms (AWS preferred: SageMaker, S3, EC2) and reproducible ML pipelines.
• Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms(SageMaker, Databricks, Unity Catalog, Snowflake/Snowpark) to build scalable ML data pipelines and model workflows.
• Strong collaboration skills to work effectively with AI scientists, software engineers, and regulatory teams.

The base salary range for this role varies by location and is aligned to market benchmarks.

• Candidates located in higher-cost labor markets, including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range.
• Final compensation is determined based on location, experience, skills, and internal equity.

This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges:

• Base Salary: $153,000 - $179,000
• TTC: $176,000 - $206,000

*Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.