As Scale's product portfolio and customer base expand, we are seeking skilled DevOps Engineers, Public Sector to be at the forefront of building out and enhancing our CI/CD pipelines. You will play a crucial role in streamlining our Software Development Life Cycle (SDLC) through collaborative efforts, moving us from a state of manual, disparate deployments to a more unified and automated system.

These engineers will gain a deep understanding of our core products' architecture and composition, enabling them to effectively deploy and manage these systems when needed. A critical aspect of this role will be seamlessly integrating various machine learning (ML) tasks and updates into our SDLC, transforming currently separate ML components into a cohesive and automated workflow. While direct ML expertise is not required, a desire to learn and integrate ML components into the lifecycle is essential.

Must have:

• At least an active TS/SCI clearance and the ability & willingness to up level to CI Poly. This is a requirement and candidates will not be considered who do not hold at least a TS/SCI clearance.

You will:

• Design, develop, and maintain robust CI/CD pipelines to automate the deployment of our lowside and highside products.
• Collaborate closely with product and engineering teams to enhance existing application code for improved compatibility and streamlined integration within automated pipelines.
• Contribute to the overall architecture and design of our deployment systems, bringing new ideas to life for increased efficiency and reliability.
• Troubleshoot and resolve complex deployment issues, ensuring minimal disruption to development cycles.
• Develop a deep understanding of our product and ML architectures to facilitate seamless integration and deployment.
• Document pipeline processes and configurations to ensure maintainability and knowledge transfer.
• Proactively incorporate security best practices into all stages of the CI/CD pipeline, building security into our development processes.
• Drive standardization and foster collaboration across different product teams to achieve a unified and efficient SDLC.

Ideally you'd have:

• 2-3 years of experience as a DevOps Engineer, DevSecOps Engineer, Software Engineer with a strong focus on CI/CD, or a similar role.
• Proven track record of building or significantly enhancing CI/CD pipelines.
• Experience configuring and adapting application code to integrate seamlessly with evolving CI/CD environments.
• Experience working fluently with standard containerization & deployment technologies like Kubernetes, Terraform, Docker, etc.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP).
• Strong proficiency in scripting and automation (e.g., Python, Bash, PowerShell).
• Familiarity with various CI/CD platforms (e.g., Jenkins, GitLab CI, GitHub Actions, Azure DevOps).
• Knowledge of software architecture, system design, and version control systems.
• Comfort with rapidly changing, fast-paced environments and a passion for finding automated solutions to complex problems.
• Basic understanding of security best practices in software development and an eagerness to integrate them.
• A hunger for learning new technologies, particularly in the realm of integrating ML into automated workflows.
• Strong problem-solving, analytical, collaboration, and communication skills.

Nice to haves:

• Experience with containerization technologies (e.g., Docker, Kubernetes).
• Exposure to machine learning lifecycles or MLOps concepts.
• Prior experience in classified environments.

Who we are

The role

The day-to-day

• Architect and implement AWS GovCloud infrastructure that meets CMMC Level 2 requirements and prepares us for certification.

• Create, manage, and execute on a roadmap for adopting, modifying, and deploying Helsing's infrastructure patterns and developer tooling. Work with the Core Infra and DevX teams to adopt or create what makes sense for our specific needs.

• Maintain Terraform and IaC tooling, manage Kubernetes workload infrastructure for builds and AI training, and co-develop Python and Rust based developer tools.

• Improve developer productivity by leveraging existing tools and creating new ones. Support DevOps and MLOps workflows, complete with CI/CD automation and CLI tooling.

• Work with vendors, partners, and service providers to integrate third-party tooling while maintaining our security and compliance requirements.

• As a senior member, you will help interview, onboard, and mentor new engineers. Over time, you will contribute to the growth and culture of the infrastructure teams.

You should apply if you

• Have deep experience with AWS, including designing and operating production infrastructure at scale. Experience with AWS GovCloud, or creating compliant AWS infrastructure, is a significant plus

• Are proficient with Kubernetes and have familiarity with Cloud-Native Technologies, like Helm, ArgoCD, or Flux

• Have strong Infrastructure as Code skills, ideally with Terraform, and can design modular, maintainable infrastructure that evolves with compliance requirements

• Can write clean, maintainable code in Python, Rust, or similar languages to build internal tooling and automation

• Communicate effectively in both technical and compliance contexts and can explain architectural decisions to engineers and security posture to auditors

• Thrive in ambiguity and take ownership of outcomes. You don't wait for perfect requirements; you actively seek context, make pragmatic decisions, and iterate based on feedback

• Value operational excellence and understand that infrastructure reliability directly impacts mission success

• Feel that building technology in service of democratic values is meaningful work worth doing

• Can obtain a security clearance (Secret or Top Secret preferred)

• Are able to work in a hybrid environment in Washington, DC, with occasional travel to customer sites or European offices

Note: We operate in an industry where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarized in a few bullet points.

Nice to Have

• Experience with defense compliance frameworks such as CMMC Level 2, FedRAMP Moderate/High, NIST 800-171, or similar security standards

• Experience with MLOps platforms and infrastructure for training and serving machine learning models

• Familiarity with container security, supply chain security, and vulnerability management in production environments

• Understanding of ITAR restrictions and experience building infrastructure that respects export control requirements

• Experience working in air-gapped or disconnected environments

• Background in distributed systems, networking protocols, or embedded computing

• Prior experience in defense, aerospace, or other highly regulated industries

Join Helsing and work with world-leading experts in their fields 

• Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns

• The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world

• Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defense industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward

• In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts

• We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practice responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about

What we offer

• A focus on outcomes, not time-tracking

• A generous compensation and benefits package (in addition to base salary) that includes, but may not be limited to, insurance coverage (medical and travel), flexible paid time off, paid holidays, and remote and/or hybrid work available depending on position. All compensation and benefits are subject to the terms and conditions of the underlying plans or programs, as applicable and as may be amended, terminated or superseded from time to time.

Helsing is an Equal Opportunity Employer. We will consider all qualified applicants without regard to race, color, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, genetics, or any other characteristic protected by applicable federal, state, or local law.

CoreWeave, the AI Hyperscaler™, acquired Weights & Biases to create the most powerful end-to-end platform to develop, deploy, and iterate AI faster. Since 2017, CoreWeave has operated a growing footprint of data centers covering every region of the US and across Europe, and was ranked as one of the TIME100 most influential companies of 2024. By bringing together CoreWeave’s industry-leading cloud infrastructure with the best-in-class tools AI practitioners know and love from Weights & Biases, we’re setting a new standard for how AI is built, trained, and scaled.

The integration of our teams and technologies is accelerating our shared mission: to empower developers with the tools and infrastructure they need to push the boundaries of what AI can do. From experiment tracking and model optimization to high-performance training clusters, agent building, and inference at scale, we’re combining forces to serve the full AI lifecycle — all in one seamless platform.

Weights & Biases has long been trusted by over 1,500 organizations — including Nvidia, Canva, Cohere, Meta, Snowflake, Square, Toyota, and Amazon — to build better models, AI agents and applications. Now, as part of CoreWeave, that impact is amplified across a broader ecosystem of AI innovators, researchers, and enterprises.

As we unite under one vision, we’re looking for bold thinkers and agile builders who are excited to shape the future of AI alongside us. If you're passionate about solving complex problems at the intersection of software, hardware, and AI, there's never been a more exciting time to join our team.

About the Role:

As an AI Solutions Engineer you will sit at the intersection of cutting-edge AI infrastructure and the world’s most ambitious AI teams. You will help customers design, deploy, and scale modern ML and GenAI systems using Weights & Biases and CoreWeave’s AI cloud. This role combines hands-on technical depth with strategic customer engagement — ideal for AI practitioners who enjoy building real systems, solving complex problems, and shaping how AI is developed and operationalized at scale.

What You’ll Do:

• Own the technical wins by building proof of value (PoVs). You’ll be partnering with Account Executives to drive discovery, solution design, and successful technical evaluations.
• Lead deep technical conversations with ML engineers, researchers, and platform teams to understand their AI/GenAI workloads and architect scalable solutions across the CoreWeave and Weights & Biases ecosystem .
• Design and deliver compelling live demos, proof-of-value engagements, and rapid prototypes showcasing experiment tracking, distributed training, agentic workflows, fine-tuning, evaluation, and inference at scale.
• Architect modern ML and GenAI systems including distributed training, RAG pipelines, multi-agent systems, LLM fine-tuning workflows, and inference solutions.
• Translate business objectives into measurable technical outcomes such as model quality improvements, cost reductions, performance gains, and faster iteration cycles.
• Serve as a trusted technical advisor, influencing architectural decisions and best practices across MLOps, LLMOps, and platform engineering.
• Partner closely with Product and Engineering to relay customer feedback, shape roadmap priorities, and improve the end-to-end AI developer experience.

Who You Are:

• 2+ years of experience in Solutions Engineering, ML Engineering, AI Engineering, or a similar customer-facing technical role.
• Bachelors degree in Computer Science, Data Science, Electrical Engineering, Statistics, Mathematics, or related field. Master in similar area of study preferred.
• Strong proficiency in Python (bonus: TypeScript/JavaScript) and experience building production-grade ML or GenAI applications.
• Hands-on experience training, fine-tuning, evaluating, debugging, and deploying deep learning or LLM-based systems.
• Experience with modern GenAI architectures including retrieval-augmented generation (RAG), tool-using agents, evaluation frameworks, and guardrails.
• Comfortable designing cloud-native architectures across AWS, GCP, or Azure, with working knowledge of containers, networking fundamentals, and security best practices.
• Able to whiteboard distributed training or inference architectures and then implement working prototypes that demonstrate real customer value.
• Excellent communication and storytelling skills, with the ability to translate complex technical concepts into business impact.
• Thrives in fast-paced environments and can manage multiple customer engagements simultaneously.

Preferred:

• Experience building and evaluating agentic applications using frameworks such as LangChain, DSPy, LlamaIndex, CrewAI, OpenClaw, or similar ecosystems. Ability to quickly adapt to new frameworks is a huge plus.
• Familiarity with vector databases, embeddings workflows, and LLM evaluation frameworks (e.g., Ragas, TruLens, DeepEval).
• Hands-on experience with Hugging Face Transformers, PEFT/LoRA/QLoRA fine-tuning, TRL, Unsloth, or similar fine-tuning stacks. Big plus if experience using RL to improve agentic systems. 
• Experience operating distributed workloads using Slurm, Ray, or Kubernetes.
• Experience with inference optimization and serving frameworks such as vLLM, TensorRT-LLM, Triton, or TGI.
• Experience in MLOps/LLMOps platform engineering or supporting enterprise AI deployments.

The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

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.

AI Office Lead - Senior AI Solutions Architect

NTT DATA is a team of more than 139,000 diverse professionals operating in more than 50 countries worldwide. Our sectors of activity include telecommunications, finance, industry, utilities, energy, public administration, and health.

Our mission? Offer technological solutions, business, strategy, development, and application maintenance while being a benchmark in consulting. Thanks to the collaboration between teams, the human quality of our people, and the fact that we do not conform to what is established, we always seek innovation that brings us closer to the future.

Our essence has led us to the forefront of technology, breaking paradigms and providing solutions that truly respond to each client's needs. Our talent has led us to be one of the top six technology companies in the world.

Because #Greattech, needs #GreatPeople, like you

NTT Data seeks high-achieving team players who quickly adapt to new challenges and entrepreneurial ventures. We are looking for a Senior AI Solutions Architect to work with our global client in Washington, D.C. This role will require an onsite presence at our client location.

Location: Washington D.C. onsite is highly preferred BUT open to candidates from Peru, Mexico, Brazil, or Chile who can work in the DC time zone**

Focus: AI Platform Architecture, Orchestration & Enterprise Integration
Experience Level: Senior (7+ years)

Role Overview

Background & Environment

• Proven experience as a senior architect or technical lead within AI, data, or digital platforms.
• Experience working in large, federated, or matrixed organizations, including public sector, healthcare, or international institutions.
• Strong background operating in environments with governance, compliance, interoperability, and cross-domain integration requirements

Key Responsibilities

Required Technical Skills & Expertise

Preferred Certifications & Training

• Scrum Master or equivalent Agile certification
• Executive-level training in AI Strategy
• NIST AI Risk Management Framework (AI RMF) training
• Microsoft Azure AI Engineer Associate
• Microsoft Azure Solutions Architect Expert
• Microsoft Copilot and Agent Administration Fundamentals

Why NTT Data?   

Empowerment and rewards are the cornerstone of our career development model. We are a young, fast-growing company, with a highly innovative and entrepreneurial spirit, because of this professional experience and growth will be unmatched. Our talent and positive attitude allow us to transform our goals into achievements, and projects into realities.

NTT Data is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. NTT Data is an Equal Opportunity Employer Male/Female/Disabled/Veteran and a VEVRAA Federal Contractor.

AI Programmer – MCP Server & Agent Systems

NTT DATA is a team of more than 139,000 diverse professionals operating in more than 50 countries worldwide. Our sectors of activity include telecommunications, finance, industry, utilities, energy, public administration, and health.

Our mission? Offer technological solutions, business, strategy, development, and application maintenance while being a benchmark in consulting. Thanks to the collaboration between teams, the human quality of our people, and the fact that we do not conform to what is established, we always seek innovation that brings us closer to the future.

Our essence has led us to the forefront of technology, breaking paradigms and providing solutions that truly respond to each client's needs. Our talent has led us to be one of the top six technology companies in the world.

Because #Greattech, needs #GreatPeople, like you

NTT Data seeks high-achieving team players who quickly adapt to new challenges and entrepreneurial ventures. We are looking for a AI Programmer to work with our global client in Washington, D.C. This role will require an onsite presence at our client location.

Experience Level: Mid–Senior (4–7+ years)
Focus: AI Agents, MCP Servers, Python Engineering
Location: Washington, D.C. (onsite preferred)
Open to candidates based in Peru, Mexico, Brazil, or Chile who can work in the DC time zone

Role Overview

The AI Programmer – MCP Server & Agent Systems is a hands-on engineering role focused on building, deploying, and maintaining MCP servers and agent-based AI services that integrate with enterprise platforms and data systems.

This role works closely with Senior AI Solutions Architects and platform teams to implement production-ready AI agents, orchestration workflows, and backend services using Python. The ideal candidate is a strong programmer with practical experience implementing AI agent patterns—not just experimenting with them.

Key Responsibilities

MCP Server & Agent Development

• Design, build, and maintain MCP servers that support AI agent execution, tool invocation, and orchestration
• Implement agent-based workflows using Python and modern AI frameworks
• Develop reusable services and APIs to enable agent interaction with enterprise systems and data sources

AI Agent Implementation

• Implement AI agents using frameworks such as LangChain, LangGraph, or MCP-style architectures
• Support RAG-based solutions including vector search, embeddings, and grounding strategies
• Implement guardrails, logging, and error handling for agent reliability and safety

Integration & Deployment

• Integrate AI agents with existing applications, APIs, data platforms, and workflows
• Package and deploy AI services using containerization and cloud-native patterns
• Support CI/CD pipelines for AI services and agent systems

Collaboration & Delivery

• Work closely with AI architects to implement approved designs and PoCs
• Participate in code reviews, testing, and technical documentation
• Support iteration from PoC to production under architectural and governance guidance

Required Technical Skills & Experience

Programming & Engineering

• Strong proficiency in Python
• Experience building backend services, APIs, or microservices
• Familiarity with Git-based workflows and collaborative development

AI Agents & Generative AI

• Hands-on experience implementing AI agents or orchestration workflows
• Experience with frameworks such as LangChain, LangGraph, or similar agent frameworks
• Understanding of LLM-based applications, prompt design, and tool-calling patterns

Data & AI Integration

• Experience working with vector databases, embeddings, and RAG pipelines
• Ability to integrate AI services with structured and unstructured data sources
• Familiarity with data APIs, event-driven systems, or streaming pipelines is a plus

Cloud & DevOps (Preferred)

• Experience deploying services in cloud environments (Azure preferred)
• Familiarity with Docker and cloud-native deployment patterns
• Exposure to CI/CD pipelines and basic MLOps concepts

Why NTT Data?   

Empowerment and rewards are the cornerstone of our career development model. We are a young, fast-growing company, with a highly innovative and entrepreneurial spirit, because of this professional experience and growth will be unmatched. Our talent and positive attitude allow us to transform our goals into achievements, and projects into realities.

NTT Data is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. NTT Data is an Equal Opportunity Employer Male/Female/Disabled/Veteran and a VEVRAA Federal Contractor.

Who we are

The real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy — a future where mundane repetition disappears and being known unlocks access, comfort and belonging everywhere you go. From transforming parking into a seamless drive-in, drive-out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical. The future isn’t coming; it’s here, and we need builders, innovators and problem solvers to help us create it.

Who you are

Metropolis is seeking a Senior Manager of Machine Learning Engineering within the Advanced Technologies Group to lead the technical vision and execution of our foundational systems that power our next generation of AI. You will oversee 4 critical pillars within the Machine Learning org:data engineering, annotation pipelines, ML Infrastructure and Deployment of Agentic AI solutions. You are a hands-on, senior technical leader with a broad dynamic range, capable of providing high-level strategic direction while remaining technically proficient enough to dive into the weeds with your team. Your mission is to transition state-of-art models into robust, autonomous production systems that automate complex enterprise workflows.You will partner closely with internal engineering teams and external vendors to build the scalable tools and data pipelines that define the future of recognition economy.

What you'll do

• Build and maintain scalable, compliant and auditable data infrastructure to serve computer vision and AI pricing use cases 
• Build scalable data engineering pipelines and automated annotation workflows (LLM-in-the-loop) to reduce reliance on manual labeling and accelerate model iteration
• Own the MLOps lifecycle, including distributed training infrastructure, model registries, and low-latency inference services. Ensure high availability and observability for all deployed models
• Define technical direction, lead and grow a high-performance team of data and ML infrastructure engineers to influence impactful business outcomes
• Develop foundational systems to productionize agentic AI, Large Language Models (LLMs) and Vision Language Models (VLMs) solutions for workflow automation to enhance our products
• Enable Metropolis’s move into personalization and targeted advertisement through innovative ML data pipelines and feature stores
• Collaborate with external vendors and annotation platform providers to ensure high-quality data for production models
• Partner with other ML leaders (Growth , Edge deployment) and cross-functional leaders in Hardware, Platform, and Product engineering to align development roadmaps

What we're looking for

• 10+ years of professional experience in data and machine learning engineering with proven expertise in building enterprise-scale, auditable ETL pipelines and data governance mechanisms
• 5+ years of experience in leadership and management, ideally having managed other managers
• MS or PhD in computer science and/or a quantitative discipline
• Strong experience in distributed data processing like Apache Spark, Kafka, Cloud native data storage and processing services
• 1+ years experience building data /eval pipelines and deploying agentic AI solutions (LLMs and/or VLMs)
• Experience managing technical programs, defining milestones, and communicating progress to diverse audiences
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch
• Strong proficiency with SQL and Python
• Engage effectively with external data providers and vendors
• Familiarity with computer vision systems and models (e.g. object detection, tracking, segmentation)

While not required, these are a plus:

• Manage large scale datasets and database tools for data processing
• Deploy ML services to the cloud with a focus on scalability and reliability
• Operate in innovative, high-growth environments

4 Days in Office: Metropolis values in-person collaboration to drive innovation, strengthen culture, and enhance the Member experience. Our corporate team members hold to our office-first model, which requires employees to be on-site at least four days a week, fostering organic interactions that spark creativity and connection

When you join Metropolis, you'll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. The anticipated base salary for this position is $200,000.00 USD to $250,000.00 USD annually. The actual base salary offered is determined by a number of variables, including, as appropriate, the applicant's qualifications for the position, years of relevant experience, distinctive skills, level of education attained, certifications or other professional licenses held, and the location of residence and/or place of employment. Base salary is one component of Metropolis’s total compensation package, which may also include access to or eligibility for healthcare benefits, a 401(k) plan, short-term and long-term disability coverage, basic life insurance, a lucrative stock option plan, bonus plans and more. #LI-AR1 #LI-Onsite

Intro to Position

The Lead AI Solutions Engineer, AI Labs, is a foundational role in building NPR's first Generative AI (GAI)-focused product development team. Reporting to the VP of AI Labs, this engineer will be responsible for the technical development and implementation of generative AI solutions. The core mission is to engineer systems that leverage GAI to enhance the quality and actionability of our content metadata, thereby directly supporting the organization's goals of improving content personalization and increasing editorial efficiency. This role involves hands-on work in integrating AI products into core NPR systems, ensuring solutions are scalable, align with ethical guidelines, and are built upon industry best practices. This is a critical opportunity to shape NPR's technical AI adoption and build a new, innovative function.

Responsibilities

• Engineering & Implementation: Design, develop, and deploy scalable Generative AI (GAI) solutions, focusing on enhancing content metadata quality and actionability to meet organizational objectives.
• Architecture & Technical Best Practices: Establish and champion technical best practices for Generative AI systems, including MLOps, RAG architectures, prompt engineering, and model evaluation, ensuring high-quality, maintainable code.
• Cross-functional Development: Collaborate closely with Product Managers, data scientists, software and infrastructure engineers, and relevant subject matter experts to translate product requirements and roadmaps into robust, production-ready AI systems.
• System Optimization & Performance: Implement data-driven methods, A/B testing, and performance monitoring to optimize the efficiency, scalability, and impact of AI models and pipelines.
• Technical Communication: Clearly articulate technical architectures, development progress, and engineering challenges to product and non-technical stakeholders.
• Responsible AI: Integrate ethical AI principles and compliance standards directly into the design and implementation of all AI products, in alignment with company policies.
• Technology Scouting: Research and evaluate emerging AI technologies, frameworks, and industry trends to drive continuous technical innovation within the AI Labs.

The above duties and responsibilities are not an exhaustive list of required responsibilities, duties and skills. Other duties may be assigned, and this job description is subject to change at any time.

Minimum Qualifications

• 8+ years of professional experience in software development, data science, or machine learning engineering, with a focus on building and deploying production systems.
• 2+ years of hands-on experience working with generative AI, machine learning, or data products.
• Expert proficiency in Python and experience creating functional prototypes to validate technical feasibility and user flows.
• Demonstrable understanding and experience building products that leverage Large Language Models, RAG architectures, prompting, function calling, retrieval, vector databases, embeddings, fine-tuning, and model evaluation.
• Familiarity with building and integrating MCP servers and agentic workflows.
• Background in creating QA systems for AI processes and development.
• Excellent communication skills and the ability to clearly articulate technical architectures and challenges to product and non-technical stakeholders.
• A collaborative and respectful approach to work and an ability to adapt quickly to change.
• A deep commitment to the mission of NPR and to the responsible implementation of Generative AI technologies.

Preferred Qualifications

• Experience designing and building highly scalable, production-grade AI/ML pipelines and services.
• Proficiency with cloud platforms, particularly AWS and Google Cloud Platform (GCP) and MLOps tools, particularly MLFlow and Vertex AI. 
• Experience with DevOps practices, including CI/CD and enterprise infrastructure-as-code.
• Experience working in media organizations and an understanding of media delivery systems.

Work Location & Requirements

• Hybrid Permitted: This is a hybrid permitted role. Some aspects of this role require duties better performed at an NPR facility. The employee will be required to be in the office at the Washington, D.C. location at least two days a week. The employee must also reside in an NPR-supported location (D.C., MD, or VA).

Job Type

• This is a full-time, exempt position.

Compensation

Salary Range: The U.S. based anticipated salary range for this opportunity is $187,000 - $208,000 plus benefits. The range displayed reflects the minimum and maximum salaries NPR expects to provide for new hires for the position across all US locations.

NPR Benefits: NPR offers access to comprehensive benefits for employees and dependents. Regular, full-time employees scheduled to work 30 hours or more per week are eligible to enroll in NPR’s benefits options. Benefits include access to health and wellness, paid time off, and financial well-being. Plan options include medical, dental, vision, life/ accidental death and dismemberment, long-term disability, short-term disability, and voluntary retirement savings to all eligible NPR employees. 

Does this sound like you? If so, we want to hear from you.

#LI-HYBRID

Your Mission 

We are seeking a rare combination of disciplines: an experienced Sr. Compliance Engineer with deep AI Subject Matter Expertise (SME) and export compliance background to join our Governance, Risk, and Compliance (GRC) team. This role is responsible for building, implementing, and sustaining the organizational compliance posture across key regulatory and security frameworks — with a primary emphasis on RMF (NIST 800-53 Rev. 5 + Classified Overlays), CMMC Level 3, NIST 800-171 Rev. 3, EAR/ITAR cyber regulations, and — critically - the governance, risk management, and compliance controls surrounding AI/ML systems and large language models (LLMs) deployed across the enterprise. 

As AI becomes embedded in True Anomaly's operations, mission systems, and products, this role serves as the organizational authority on how AI capabilities are adopted, audited, and controlled responsibly. You will architect and operationalize compliance checkpoints and governance gates within LLM pipelines, evaluate AI vendors and platforms (including OpenAI, Anthropic Claude, and others) against classified and unclassified compliance requirements, and ensure AI-driven workflows satisfy both regulatory obligations and internal risk tolerance. 

The ideal candidate brings deep GRC knowledge, hands-on AI/LLM engineering fluency, and the ability to engage credibly with compliance assessors, government partners, and internal AI/ML engineering teams alike. 

Responsibilities 

Compliance Program Execution 

• Lead and support compliance assessment readiness across key organizational frameworks including NIST SP 800-171 Rev. 2 and 3, CMMC Level 3, NIST SP 800-53 Rev. 5, and the NIST Cybersecurity Framework (CSF). 

• Provide direction on cybersecurity readiness to address EAR and ITAR-related controls and requirements. 

• Drive CMMC readiness activities across the organization, including scoping, gap analysis, control implementation validation, evidence collection, and pre-assessment preparation. 

• Review, maintain, and mature System Security Plans (SSPs) to accurately reflect organizational control implementations, system boundaries, and operational practices — including AI/ML system boundaries and data flows. 

• Manage Plans of Actions and Milestones (POA&Ms), tracking open findings to resolution, communicating status to GRC leadership, and coordinating remediation efforts across responsible teams. 

• Conduct internal compliance audits and control effectiveness reviews to ensure ongoing adherence to applicable frameworks and to surface emerging gaps before external assessments. 

• Maintain audit-ready evidence repositories and documentation packages, ensuring traceability between controls, evidence, and framework requirements. 

AI Governance, Risk & Compliance (AI-GRC) 

• Serve as the organizational AI compliance SME — the primary authority on how AI/LLM systems (including OpenAI GPT models, Anthropic Claude, open-source models, and internally developed models) are evaluated, onboarded, and continuously governed within True Anomaly's compliance boundaries. 

• Design, implement, and maintain compliance checkpoints and enforcement gates within LLM pipelines, including:  

• Input/output filtering and content policy enforcement layers 

• Prompt injection detection and mitigation controls 

• Data classification guardrails to prevent CUI, ITAR-controlled, or classified data from flowing into non-authorized AI systems or endpoints 

• Automated audit logging of AI interactions for traceability and incident investigation 

• Model access control and role-based permissions within AI platforms 

• Conduct AI-specific risk assessments, including evaluation of AI vendor data handling practices, model training data provenance, and third-party AI API security postures against NIST AI RMF, NIST SP 800-53 AI overlays, and internal standards. 

• Develop and enforce an AI System Acceptable Use Policy and supporting standards that govern how employees and systems interact with LLMs, including permissible data inputs, output handling, human-in-the-loop requirements, and escalation procedures. 

• Evaluate proposed AI/ML use cases for regulatory risk (EAR/ITAR, CMMC, data privacy) and provide compliance go/no-go determinations with documented rationale. 

• Collaborate with AI/ML engineers and DevSecOps teams to integrate compliance gates into CI/CD pipelines and MLOps workflows, ensuring model changes and prompt changes undergo review before production deployment. 

• Maintain an AI system inventory, tracking all deployed models, APIs, integrations, and associated risk and compliance status. 

• Monitor emerging AI regulatory developments (e.g., EO 14110, NIST AI RMF, DoD AI Ethics Principles, EU AI Act implications for U.S. defense partners) and assess organizational impact. 

Cross-Functional Compliance Enablement 

• Serve as a primary GRC team resource for compliance questions, control guidance, and framework interpretation across engineering, IT, operations, legal, and security teams. 

• Partner with IT and security operations teams to verify that technical controls — including access management, logging, configuration baselines, and incident response procedures — meet CMMC and NIST requirements at an organizational level. 

• Partner with AI/ML engineers, data scientists, and product teams to embed compliance thinking into AI system design, model selection, and deployment architecture. 

• Collaborate with the Enterprise Risk Manager and broader GRC leadership to ensure compliance findings — including AI-specific risks — are reflected in the enterprise risk register and remediation priorities. 

• Support the development of compliance training and awareness materials, including AI-specific training that builds organizational understanding of responsible AI use, LLM risk, and CMMC obligations. 

• Coordinate with external assessors, third-party auditors, and government partners during assessment engagements, serving as a knowledgeable point of contact for evidence walkthroughs and control discussions. 

Qualifications 

• 7+ years of experience in IT security compliance, GRC, or a closely related discipline, with direct ownership of compliance program activities. 

• Demonstrated expertise in NIST SP 800-171, CMMC (Level 2 or 3), and NIST SP 800-53, with hands-on experience conducting gap assessments, implementing controls, and preparing organizations for external audits. 

• Extensive, hands-on experience with AI/LLM systems, including practical knowledge of platforms such as OpenAI (GPT-4/o-series), Anthropic Claude, Meta Llama, Microsoft Azure OpenAI Service, and/or comparable commercial and open-source LLM ecosystems. 

• Demonstrated ability to design, implement, and operationalize compliance controls within LLM pipelines, including guardrail layers, content filtering, audit logging hooks, and data classification enforcement. 

• Working knowledge of AI security risks, including prompt injection, jailbreaking, data exfiltration via LLM outputs, model inversion, and supply chain risks associated with third-party AI APIs. 

• Familiarity with NIST AI Risk Management Framework (AI RMF) and its application to enterprise and defense AI deployments. 

• Strong understanding of SSP development and maintenance, POA&M management, and audit evidence lifecycle practices in an organizational (non-product) compliance context. 

• Proven experience developing and operationalizing information security policies, standards, and procedures across a multi-disciplinary organization. 

• Strong communication skills with the ability to explain compliance requirements — including AI risk concepts — clearly to both technical practitioners and non-technical business stakeholders. 

• Highly organized, with demonstrated ability to manage multiple concurrent compliance workstreams and deadlines in a fast-paced environment. 

• Active or ability to obtain SECRET or TS/SCI security clearance. 

• Must be a U.S. citizen, lawful permanent resident, or protected individual per ITAR requirements (8 U.S.C. 1324b(a)(3)). 

Preferred Qualifications 

• Strong EAR/ITAR background as it pertains to cybersecurity, AI-generated outputs, and policy development. 

• J.D. focusing on technology law, export compliance (EAR and ITAR), AI regulation, or cyber law. 

• Experience building MLOps or AI DevSecOps pipelines with integrated compliance gates, including automated policy enforcement, prompt review workflows, or model change management processes. 

• Hands-on experience with AI safety and alignment tooling (e.g., LangChain guardrails, NeMo Guardrails, Azure Content Safety, OpenAI Moderation API, Anthropic Constitutional AI/policy layer configurations). 

• Experience evaluating AI vendor agreements and data processing agreements against DoD/CMMC/ITAR data handling requirements. 

• Familiarity with DoD AI Ethics Principles, Responsible AI (RAI) frameworks, and emerging federal AI governance requirements (e.g., EO 14110, OMB AI guidance). 

• Industry certifications such as:  

• Certified Information Systems Auditor (CISA) 

• Certified in Risk and Information Systems Control (CRISC) 

• Certified Information Systems Security Professional (CISSP) 

• CMMC Registered Practitioner (RP) or Certified Professional (CP) 

• CompTIA Security+ or equivalent 

• AWS/Azure AI or Security certifications 

• Background in startup, aerospace, defense technology, or SaaS environments operating under DoD compliance obligations. 

• Familiarity with cloud environments — particularly Azure Government, AWS GovCloud, or Azure OpenAI Government deployments — as they relate to organizational control implementation and AI boundary scoping. 

• Experience coordinating with C/3PAOs or supporting CMMC assessments. 

• Working knowledge of DFARS 252.204-7012, ITAR, and supply chain compliance obligations. 

• Familiarity with Agile/Scrum environments and hybrid project delivery models. 

Compensation 

• Base Salary: Denver - $145,000 to $195,000, Long Beach - $150,000 to $205,000, Washington, DC - $150,000 to $205,000 

• Equity + Benefits including Health, Dental, Vision, HRA/HSA options, PTO and paid holidays, 401K, Parental Leave 

Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, location, and experience. 

Additional Requirements 

• Work Location: Successful candidates will be located near Denver, CO, Long Beach, CA, or Washington D.C. While we observe a hybrid work environment, some work must be done on site. #LI-Onsite

• Work Environment: Standard office setting, working at a desk or in a production factory environment. 

• Physical Demands: May include frequent standing, sitting, walking, bending, and lifting or carrying items up to 20 lbs. 

This position will be open until it is successfully filled. 

To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR), you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. 

We value diversity of experience, knowledge, backgrounds, and perspectives and harness these qualities to create extraordinary impact. True Anomaly is committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, maternity or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. 

The work: 

• Develop, train, finetune and evaluate computer vision models in a wide range of topics, including geospatial, biometrics, 3D vision, semantic extraction, etc. 

• Deploy, maintain, and optimize ML models and data processes in a production environment  

• Develop custom Computer Vision (CV) algorithms that translate into mission value 

• Create tools to provide feedback from production ML models and data processes  

• Assist in the development and optimization of computer vision models using deep learning frameworks (e.g., PyTorch, TensorFlow). 

• Collaborate with other scientists and engineers to build and deploy models for tasks such as object detection, image segmentation, classification, and tracking

• Stay up to date with the latest research and advancements in the field of computer vision and deep learning

• Participate in data collection, preprocessing, and augmentation processes to ensure high-quality datasets

• Conduct experiments, analyze results, and contribute to the improvement of model accuracy and efficiency

• Assist in the integration of computer vision algorithms into production systems and applications 

• Document code, methodologies, and experimental results

Here’s what you need: 

• Hands-on experience with computer vision libraries (e.g., OpenCV) and deep learning frameworks (e.g., PyTorch, TensorFlow)

• Proficiency with programming languages such as Python, C/C++ or Rust 

• Strong understanding of CNNs, transformers and other advanced architectures and their applications in computer vision tasks

• Strong analytical and problem-solving skills

• Ability to work collaboratively in a team environment and take direction from senior team members

• Hands-on experience with developing computer vision models at scale from inception to business impact 

• Design and develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches 

• U.S. Citizenship (No Dual citizenship)

Bonus points if you have:  

• Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required 
• Hands-on experience with MLOps and CI/CD toolset including MLFlow, WandB, Airflow, Kubeflow, Gitlab CI or DVC
• Hands-on experience developing and deploying machine learning pipelines in AWS, Azure or GCP
• Hands on experience deploying, maintaining, testing, and optimizing ML models and data platforms in a production environment 
• Hands-on experience with other image modalities (SAR, IR, HSI, Lidar, Sonar) 

Security Clearance: 

• Active Top Secret or TS/SCI or TS/SCI with polygraph Clearance

The work: 

• Develop MLOps frameworks and workflows for a variety of domains and applications 

• Deploy, maintain, and optimize ML models and data processes in a production environment  

• Develop custom AI/ML algorithms that translate into mission value 

• Conduct experiments, evaluate model performance, and fine-tune algorithms to improve accuracy and efficiency

• Collaborate with cross-functional teams to integrate AI/ML models into products and services

Here’s what you need: 

• Hands-on experience with scripting languages such as Python, JavaScript or Rust 
• Familiar with MLOps and CI/CD toolset including MLFlow, WandB, Airflow, Kubeflow, Gitlab CI or DVC
• Hands-on experience developing and deploying machine learning pipelines in AWS, Azure or GCP 
• Design and develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches 
• Experience working with and managing processing of large datasets and computational or analytic jobs  
• US Citizenship (No Dual citizenship)

Bonus points if you have:  

• Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required  

Security Clearance: 

• Active Top Secret or TS/SCI or TS/SCI with polygraph Clearance

The work: 

• Develop MLOps frameworks and workflows for a variety of domains and applications 

• Deploy, maintain, and optimize ML models and data processes in a production environment  

• Develop custom AI/ML algorithms that translate into mission value 

• Conduct experiments, evaluate model performance, and fine-tune algorithms to improve accuracy and efficiency

• Collaborate with cross-functional teams to integrate AI/ML models into products and services

Here’s what you need: 

• Hands-on experience with scripting languages such as Python, JavaScript or Rust 

• Hands-on experience with developing machine learning models at scale from inception to business impact 

• Hands-on experience with any major deep learning framework and libraries (TensorFlow, PyTorch, HuggingFace) 

• Hands-on experience with MLOps and CI/CD toolset including MLFlow, WandB, Airflow, Kubeflow, Gitlab CI or DVC 

• Hands-on experience developing and deploying machine learning pipelines in AWS, Azure or GCP 

• Design and develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches 

• Experience deploying, maintaining, testing, and optimizing ML models and data platforms in a production environment/s

• Experience working with and managing processing of large datasets and computational or analytic jobs  or in academic setting

• Experience monitoring and triaging issues with data processes and tools   

• US Citizenship (No Dual citizenship)

Bonus points if you have:  

• Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required  

Security Clearance: 

• Active Top Secret or TS/SCI or TS/SCI with polygraph Clearance

The work: 

• Develop MLOps frameworks and workflows for a variety of domains and applications 

• Deploy, maintain, and optimize ML models and data processes in a production environment  

• Develop custom AI/ML algorithms that translate into mission value 

• Conduct experiments, evaluate model performance, and fine-tune algorithms to improve accuracy and efficiency

• Collaborate with cross-functional teams to integrate AI/ML models into products and services

Here’s what you need: 

• Hands-on experience with scripting languages such as Python, Javascript or Rust 

• Hands-on experience with developing machine learning models at scale from inception to business impact 

• Hands-on experience with any major deep learning framework and libraries (Tensorflow, PyTorch, HuggingFace) 

• Hands-on experience with MLOps and CI/CD toolset including MLFlow, WandB, Airflow, Kubeflow, Gitlab CI or DVC 

• Hands-on experience developing and deploying machine learning pipelines in AWS, Azure or GCP 

• Design and develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches 

• Experience deploying, maintaining, testing, and optimizing ML models and data platforms in a production environment  

• Experience working with and managing processing of large datasets and computational or analytic jobs  

• Experience monitoring and triaging issues with data processes and tools   

• Must be a U.S. Citizen (No Dual citizenship)

Bonus points if you have:  

• Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required  

Compensation: $180,000-$210,000

Team and Position Details:

As a Data Warehouse Technical Architect with deep site reliability engineering (SRE) experience, you will be reporting to the Chief Technology Officer and will be dedicated to architectural excellence and technical leadership for Virtru's digital privacy products. The ideal candidate will have a natural passion for digital privacy, data architecture, and delivering exceptional technical solutions. You'll have the opportunity to work with and mentor a stellar team of passionate engineers who are collaborative, cohesive, and dedicated to producing high-quality results. We encourage curiosity and have a culture that encourages growth. As a Technical Architect on this team, you will be on the cutting edge of Zero-Trust - the future of data and service protection!

This is a player/coach role with primary focus on architecture, design, and technical health, while also contributing to critical technical implementations. You will partner closely with Engineering Managers and other technical architects to provide dedicated architecture and expertise to design, build, and operate our mission-critical data infrastructure at scale. 

As a Data Warehouse Technical Architect, your responsibilities will include:

Software Architecture & Design:

• Lead system design and architecture reviews for scaled data warehouses and engineer and designing and deploying web scale applications
• Deep experience in SRE and deploying application at scale to clouds. Strong foundation in SRE principles and practices
• Create and maintain architectural documentation, including authoring and reviewing ADRs (Architecture Decision Records), to ensure alignment with overall software architecture
• Data lakehouse design, data modeling, and integration patterns for deploy databases and data as a service, at scale
• Provide technical direction for scaling complex features and initiatives involving data storage, processing and deployments
• Familiarity with machine learning infrastructure and MLOps

Technical Health & Quality:

• Monitor and improve technical debt across all engineering teams, related to data storage, processing and site reliability
• Partner with Engineering Managers to define key technical health metrics and streamline technical onboarding processes
• Drive reliability, performance, and security best practices for data storage, processing and SRE
• Break down complex technical tasks into clear, manageable deliverables for the team
• Ensure sustainable engineering practices and code quality standards
• Serve as a technical guide for team members, helping them understand data access and storage and deployment architecture, and development practices
• Ensure teams are delivering solutions to satisfy requirements while aligning to current and future architectural designs

Technical Leadership & Mentorship:

• Coach and mentor engineers on data storage, warehouse, processing, and SRE systems architecture, and best practices
• Lead by example through high-quality code contributions to critical or complex components
• Share knowledge of hyperscale data warehouse/lakehouses, data models, and emerging technologies with the team
• Assist SRE teams with improving stability, automation, and scalability
• Conduct code reviews with focus on architectural alignment and quality
• Support hiring processes and technical assessments for engineering candidates

Collaboration & Strategic Partnership:

• Partner with Engineering Managers through regular 1:1s and team planning sessions
• Collaborate with product management, UX, and other architects to design and deliver new software solutions
• Work with other Technical Architects to identify and resolve cross-team dependencies and technical challenges
• Ensure technical roadmap alignment with product and business priorities
• Participate in and contribute to platform-wide architectural initiatives

Execution:

• Take ownership of the entire lifecycle and toolchain of all data components from design through deployment
• Contribute to critical technical implementations, balancing hands-on coding with architectural responsibilities
• Work collaboratively in a fast-paced environment touching a variety of projects and products
• Build software that will meaningfully impact how people approach data privacy

Skills that will help you thrive in this role:

Technical Expertise:

• Strong  knowledge of cloud data warehouses (Snowflake, BigQuery, Redshift, or Synapse)
• Strong experience with lakehouse platforms (Databricks, Delta Lake, Apache Iceberg, or Hudi)
• Deep understanding of data modeling techniques (Kimball, Data Vault, or dimensional modeling)
• Experience with columnar storage formats (Parquet, ORC)
• Strong knowledge of batch and stream processing architectures
• Experience with workflow orchestration tools (Airflow, Prefect, Dagster, or similar)
• Understanding of CDC (Change Data Capture) patterns and tools
• Strong foundation in SRE principles and practices
• Experience with infrastructure as code (Terraform, CloudFormation, or Pulumi)
• Proficiency in containerization and orchestration (Docker, Kubernetes)
• Knowledge of monitoring and observability tools (Datadog, Prometheus, Grafana, or similar)
• Experience with CI/CD pipelines and DevOps practices

Architectural & Leadership Skills:

• Demonstrated experience leading architectural design and technical direction for engineering teams
• Proven ability to influence without authority and drive technical decisions across teams
• Strong technical mentoring and coaching experience
• Experience creating architectural documentation, design documents, and technical specifications
• Track record of improving technical health metrics and reducing technical debt
• Experience with cross-team collaboration and dependency management

Education & Experience:

• Bachelor's degree in Computer Science, Engineering, or related technical discipline (or equivalent experience)
• At least 7 years of data warehouse, database, or data processing experience
• Proven experience in architectural or technical leadership roles

Communication & Collaboration:

• Excellent ability to communicate clearly and concisely, both verbally and in writing
• Strong presentation skills and ability to articulate complex technical concepts to diverse audiences
• Experience with stakeholder management and aligning technical decisions with business context
• Motivated by delivering customer value, team impact, and technical excellence

Preferred Qualifications:

• Experience architecting Microsoft 365 add-ins with focus on security, performance, and user experience
• Knowledge of data privacy, encryption, and zero-trust security models
• Familiarity with CI/CD pipelines and modern DevOps practices
• Contributions to open-source projects or technical community involvement

Virtruvian qualities that will set you up for success:

• Thinking outside of the box to respectfully challenge your teammates and managers in the pursuit of excellence
• Strong sense of urgency with an action-oriented mindset
• Able to collaborate and adapt to shifting priorities as business needs evolve
• Comfortable with asynchronous communication including slack, email, zoom, etc.

Perks & Benefits:

At Virtru, we believe people do their best work when their wellbeing is put first. This is why we make your wellbeing our priority with a thoughtful and holistic program that encompasses Occupational, Mental, Social, Physical, and Environmental Wellness by offering benefits such as…

• A Flexible PTO policy — we strongly encourage you to take time off (in addition to 14 holidays) to ensure that you are getting the proper time needed to unplug and recharge. 
• A $1,500 annual Learning & Development Stipend focused on providing you the resources to continually learn and professionally grow.
• Frequent company-sponsored team celebrations that provide ample opportunities to connect with teammates and be social!
• Access to an Employee Assistance Program
• Access to Headspace, a mental health app tailored to your specific needs.
• A flat 3% contribution to your retirement account
• A high degree of flexibility — Have an appointment, errand, or family emergency to take care of? Hop to it! We give you the time and space to take care of you and your own first.

In addition to wellbeing, Virtru places a strong emphasis on diversity, equity, inclusion, and belonging. Our DE&I Council is dedicated to fostering an inclusive workplace and making the psychological safety of each and every one of our teammates a top priority. 

Additional perks include: 

• Competitive compensation
• Generous parental, medical, and bereavement policies
• Uncapped commissions for Sales roles
• 401K contribution and stock options
• Full medical, dental, and vision benefits
• New Hire Swag and IT Welcome boxes
• Structured semi-annual 360° performance reviews

Virtru is committed to building an inclusive environment for people of all backgrounds and everyone is encouraged to apply. Virtru is an Equal Opportunity Employer and does not discriminate on the basis of race, color, gender, sexual orientation, gender identity or expression, religion, disability, national origin, protected veteran status, age, or any other status protected by applicable national, federal, state, or local law.

About the role: As an Engineering Fellow, you will apply your technical skills to support high-impact research problems. Fellows will contribute across the project lifecycle — from processing diverse data sources and designing dynamic visualizations, to deploying sophisticated models and building cloud infrastructure. This is a hands-on role at the intersection of applied research and practical engineering, with opportunities to explore novel methods, test ideas quickly, and generate insights. 

Fellows specialize in one of three concentrations based on interest and past experience: Software Engineering, Data Engineering, or Machine Learning. 

In this role, you will:

• Collaborate with engineers on real projects, including client-facing products and in-house tooling;
• Assist with researching experiment design and automation, particularly as it relates to abuse detection or red teaming of AI systems;
• Ideate / brainstorm new research approaches to known and novel problems in the Trust & Safety and AI Security fields; and
• Support other critical initiatives. 

Software Engineering concentration responsibilities may include:

• Implementing cloud infrastructure for deploying machine learning models;
• Writing high-coverage test suites for complex codebases; and
• Guiding project development with software engineering best practices, including version control, continuous integration, and design patterns. 

Data Engineering concentration responsibilities may include:

• Sourcing, curating, and processing diverse data sources across domains and modalities, including automated collection of internet-scale datasets; 
• Designing data architecture schemata, implementing with production-grade data storage tools, and interfacing via custom APIs; and 
• Developing front-end dashboards and other visualizations.  

Machine Learning concentration responsibilities may include:

• Training, validating, evaluating, and deploying cutting-edge machine learning algorithms including classifiers, LLMs, and computer vision models;
• Building agentic systems for automated prompting, red-teaming, research, and rapid experimentation; 
• Supporting projects with specialized knowledge of frontier model architectures and cutting-edge technology. 

We’re looking for someone who: 

• Brings curiosity and creativity to ambiguous research problems, with a bias toward experimentation and rapid iteration; 
• Thrives in collaborative, interdisciplinary environments; is resourceful, proactive, and adaptable;
• Is comfortable communicating technical ideas clearly to both technical and non-technical audiences; and 
• Is excited about contributing to real-world applications and exploring new methods that push beyond standard benchmarks. 

Requirements:

• Strong academic background and quantitative foundation demonstrated through applied coursework, research, or hands-on-experience
• Strong Python background
• Clear communicator of technical concepts for non-technical audiences

Nice to have:

• Familiarity with Google Cloud Platform (or similar), including storage and database services (e.g., Cloud Storage, CloudSQL, Cloud Spanner), workflow orchestration (e.g., Cloud Composer/Airflow, Cloud Run, Pub/Sub), and ML services (e.g., Vertex AI, Compute Engine)
• Experience managing full lifecycle projects from design to deployment

Software Engineering: 

• • Experience designing and building end-to-end backend systems, from architecture and data modeling to deployment, scaling, and monitoring
  • Proficiency in backend programming languages such as Python, Java, Kotlin, Node.js, or Go and experience building secure systems, APIs, and microservices
  • Knowledge of security best practices, including authentication methods (OAuth, JWT), encryption, and secure API development; knowledge of common attack vectors (SQL injection, privilege escalation, DDoS) and effective mitigation strategies

Data Engineering: 

• • Experience with web scraping/crawling (e.g., Beautiful Soup, Selenium, Scrapy)

Machine Learning Engineering: 

• • Computer vision skills (OCR, image classification, deep fake detection)
  • Familiarity with multimodal learning (text-image or text-audio) or cross-domain model evaluation
  • Exposure to MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.)
  • Understanding of modern retrieval-augmented generation (RAG), AI agent frameworks, and context-aware orchestration (e.g., LangChain, LlamaIndex, OpenAI Agents, or AutoGen) for building intelligent applications

Benefits:

• Flexible start / end dates
• Remote work (based in the continental U.S.) 
• Flexible schedule, up to 20 hours per week (negotiable)
• Hourly pay commensurate with experience and qualifications
• $30 per hour for undergraduate students 
• $35 per hour for graduate students
• $50 per hour for advanced PhD students
• $60 per hour for postdocs or non-tenured positions
• $125 per hour for tenure-track academics

CSQ227R88

PLEASE NOTE:
Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ227R175

PLEASE NOTE:
Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. The position is based in the Washington, D.C., Maryland, or Virginia metropolitan area and includes periodic on‑site work and client collaboration. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Resident Solutions Architect (RSA) on our Professional Services team, you will work with customers on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• Handle a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues
• Collaborate with the Databricks Technical, Project Manager, Architect and Customer teams to ensure the technical components of the engagement are delivered to meet customer's needs
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues

What we look for:

• US Top Secret Clearance Required this position
• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines
• Documentation and white-boarding skills
• Experience working with clients and managing conflicts
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects
• Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent experience through work experience
• Ability to travel up to 30% when needed

CSQ127R319

As a Resident Solutions Architect (RSA) on our Professional Services team, you will work with customers on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• Handle a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues
• Collaborate with the Databricks Technical, Project Manager, Architect and Customer teams to ensure the technical components of the engagement are delivered to meet customer's needs
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines
• Documentation and white-boarding skills
• Experience working with clients and managing conflicts
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects
• Ability to travel up to 30% when needed

CSQ127R318

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ127R318

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ227R87

Candidates located anywhere within the Central or Eastern Time Zones are also eligible for consideration for this role.

As a Senior Big Data Solutions Architect (Sr Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap hands-on projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 9+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Apache Spark™ runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps
• Capable of design and deployment of highly performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Experience in building scalable streaming and batch solutions using cloud-native components
• Travel to customers up to 20% of the time
• Nice to have: Databricks Certification

About the Role:

As a member of the Product and Engineering team at PitchBook, you will be part of a team of big thinkers, innovators, and problem solvers who strive to deepen the positive impact we have on our customers and our company every day. We value curiosity and the drive to find better ways of doing things. We thrive on customer empathy, which remains our focus when creating excellent customer experiences through product innovation.

We know that greatness is achieved through collaboration and diverse points of view, so we work closely with partners around the globe. As a team, we assume positive intent in each other’s words and actions, value constructive discussions, and foster a respectful working environment built on integrity, growth, and business value. We invest heavily in our people, who are eager to learn and constantly improve. Join our team and grow with us!

As an Engineering Manager, Machine Learning (ML) Operations in the Technology & Engineering division, you will be responsible for leading and managing PitchBook’s MLOps team. The team is responsible for enabling PitchBook’s Machine Learning teams and practitioners by providing tools and golden paths that optimize all aspects of the Machine Learning Development Life Cycle (MLDLC). Your team’s work will support projects in a variety of domains, including Generative AI (GenAI), Large Language Models (LLMs), Natural Language Processing (NLP), Classification, and Regression. Your role will be critical in driving AI (Artificial Intelligence) innovations across the organization.

Primary Job Responsibilities:

• Lead the MLOps team direction and execution (operations, processes, practices, and standards), working closely with engineering leadership and product management to craft roadmaps, define KPIs, and achieve success criteria
• Ensure effective communication and coordination across geographically dispersed teams. Oversee the enablement of scalable solutions that meet high standards of reliability and efficiency
• Champion the adoption and integration of ML best practices at PitchBook, fostering a culture of innovation and experimentation to drive the development of high-quality AI products
• Serve as a force multiplier by removing roadblocks, implementing process improvements, providing frequent and actionable feedback to team members, and building practices for ideation and innovation
• Bridge the gap between business/product needs and execution, including building and delivering on group-level objectives and key results, identifying resource needs, and building execution plans for initiatives
• Ensure MLOps roadmap items are delivered on time and have exceptional quality
• Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBook’s AI capabilities
• Describe technical content in intuitive ways for a variety of audiences, adapting communication from highly technical deep dives with engineers to non-technical dialogue with executive stakeholders
• Establish and drive a culture founded on creating belonging, psychological safety, candor, connection, cooperation, and fun
• Understand how to apply agile, lean, and principles of fast flow to team efficiency and productivity 
• Support the vision and values of the company through role modeling and encouraging desired behaviors 
• Participate in various company initiatives and projects as requested 

Skills and Qualifications:

• Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, Data Science, or a related field
• 3+ years of experience in an engineering leadership role, managing globally distributed teams
• 6+ years of experience in hands-on development of Machine Learning algorithms
• 6+ years of experience in hands-on deployment of Machine Learning services
• 6+ years of experience supporting the entire MLDLC, including post-deployment operations such as monitoring and maintenance
• 6+ years of experience with Amazon Web Services (AWS) and/or Google Cloud Platform (GCP)
• Experience with at least 70%: PyTorch, Tensorflow, LangChain, scikit-learn, Redis, Elasticsearch, Amazon SageMaker, Google Vertex AI, Weights & Biases, FastAPI, Prometheus, Grafana, Apache Kafka, Apache Airflow, MLflow, and KubeFlow
• Ability to break large, complex problems into well-defined steps, ensuring iterative development and continuous improvement
• Experience in cloud-native delivery with a deep practical understanding of containerization technologies such as Kubernetes and Docker, and the ability to manage these across different regions
• Proficiency in GitOps and creation/management of CI/CD pipelines
• Demonstrated experience building and using SQL/NoSQL databases
• Demonstrated experience with Python (Java is a plus) and other relevant programming languages and tools
• Excellent problem-solving skills with a focus on innovation, efficiency, and scalability in a global context
• Strong communication and collaboration skills, with the ability to engage effectively with internal customers across various cultures and regions
• Ability to be a team player who can also work independently
• Experience working across multiple development teams is a plus
• Proficiency with the Microsoft Office suite including in-depth knowledge of Outlook, Word, and Excel with the ability to pick up new systems and software easily 
• Must be authorized to work in the United States without the need for visa sponsorship now or in the future

Benefits + Compensation at PitchBook:

Physical Health            

• Comprehensive health benefits
• Additional medical wellness incentives 
• STD, LTD, AD&D, and life insurance

Emotional Health 

• Paid sabbatical program after four years
• Paid family and paternity leave 
• Annual educational stipend
• Ability to apply for tuition reimbursement
• CFA exam stipend 
• Robust training programs on industry and soft skills 
• Employee assistance program
• Generous allotment of vacation days, sick days, and volunteer days 

Social Health 

• Matching gifts program
• Employee resource groups
• Subsidized emergency childcare  
• Dependent Care FSA
• Company-wide events
• Employee referral bonus program  
• Quarterly team building events

Financial Health 

• 401k match
• Shared ownership employee stock program 
• Monthly transportation stipend  

*Please be aware the above PitchBook benefit and perk offerings are subject to corresponding plan and policy documents and may change during the course of your employment.

Compensation

• Annual base salary: $240,000-$300,000
• Target annual bonus percentage: 15%

Working Conditions:

At the heart of our company is a belief in the power of in-person collaboration. Being together in the office fuels our creativity, strengthens our connections, and drives the innovation that sets us apart. Our culture is built on spontaneous moments—those hallway conversations, whiteboard brainstorms, and shared celebrations in each of our global offices—that simply can’t be replicated remotely. This role is expected to be in the office 5 days a week. 

The job conditions for this position are in a standard office setting. Employees in this position use PC and phone on an on-going basis throughout the day. Limited corporate travel may be required to remote offices or other business meetings and events.

We are excited to get to know you and your background. Concerned that you might not meet every requirement? We encourage you to still apply as you might be the right candidate for the role or other roles at PitchBook.

#LI-TK1

#LI-Onsite

Software Engineer ML Ops, Pricing (Senior)

Seattle, WA

About the Team & Role

The Pricing team is the engine behind Opendoor’s ability to price homes with speed, scale, and confidence. We build the core platform that turns data, models, and business logic into the prices that power our entire business. Our services and data infrastructure are mission-critical to pricing decisions and automation, and they must be fast, accurate, and resilient—because even small improvements can drive major business impact.

We’re looking for a senior-level Software Engineer to join our Pricing & ML team, leading the design and evolution of the platform and tooling that productionize the machine learning models behind our pricing engine. This role is ideal for an engineer who enjoys working close to data and models, has meaningful experience with ML workflows, and wants to shape technical direction as well as ship high-impact systems. Our models are pragmatic and straightforward—we prioritize value, reliability, and iteration speed over complex research systems.

In this role, you’ll work side-by-side with backend software engineers, data scientists, ML engineers, product managers, and partner engineering and operations teams to turn prototypes and ideas into robust, scalable, and observable production systems. You’ll own high-impact initiatives end-to-end, mentor other engineers, and have significant influence over how our pricing platform evolves and how we shape the future of real estate.

What You’ll Do

• Lead the design and implementation of services, tooling, and workflows that enable reliable training, deployment, and monitoring of pricing and ML models
• Work closely with researchers and analysts to convert model prototypes into clean, testable, production-ready Python code and systems
• Own and operate model pipelines end-to-end — including data ingestion, training, validation, versioning, deployment, and monitoring
• Design and maintain workflows that support the full ML lifecycle: experimentation, training, evaluation, deployment, and iteration
• Develop and optimize data access patterns and SQL queries over large, complex datasets
• Implement robust automation for key ML lifecycle workflows (e.g., scheduled retraining, rollbacks, A/B tests, canary releases)
• Drive improvements in reliability, observability, performance, and cost-efficiency across ML pipelines and model-serving environments
• Proactively address real-world challenges like data drift, model decay, and changing market conditions in the real estate domain
• Contribute to and help define shared ML infrastructure, patterns, and best practices across the Pricing & ML team
• Lead code reviews and technical design discussions; mentor and support other engineers on ML-adjacent work
• Participate in and help improve on-call and incident response processes for ML systems

What You’ll Need

• 8+ years of experience in software engineering or 6 Years with a Masters or ML engineering, including substantial work with ML-adjacent or production ML workflows
• Strong proficiency in Python, with a track record of writing maintainable, modular, and well-tested production code
• Solid experience working with SQL (queries, joins, indexing, and performance optimization)
• Proven experience owning and operating data pipelines and/or model training/serving pipelines in production or high-stakes environments
• Deep familiarity with the end-to-end ML lifecycle (training, evaluation, deployment, monitoring, and iteration)
• Demonstrated ability to make and communicate technical design decisions and tradeoffs across multiple stakeholders
• Strong collaboration and communication skills, especially when working with data scientists, researchers, and cross-functional partners
• A bias toward impact, learning, and pragmatic solutions in a fast-moving, high-stakes domain

Focus:

•  - ML infrastructure and operations rather than model research.
•  - Building, deploying, and maintaining ML pipelines and systems.
•  - All roles are expected to be hands-on coding roles.
•  - All MLOps roles are expected to be Seattle-based.

Nice to Have

• Experience working on ML systems in business-critical environments (e.g., pricing, forecasting, logistics, marketplaces, risk)
• Familiarity with ML ops concepts and tools (e.g., model serving frameworks, feature stores, experiment tracking, model registries)
• Experience with tools such as MLflow, Airflow, Spark, or Delta Lake
• Experience monitoring model performance in production (e.g., drift detection, quality alerts, dashboards)
• Experience with streaming / event-driven systems (e.g., Kafka) or scheduling/orchestration tools
• Comfort working in a Linux-based, cloud-hosted environment (e.g., AWS)
• Interest in real estate or other messy, high-stakes domains with imperfect data

The base pay range for this position is $205,000 - $281,000 annually, plus RSUs and bonuses. Pay within this range varies by work location and may also depend on your qualifications, job-related knowledge, skills, and experience.  We also offer a comprehensive package of benefits including unlimited PTO, medical/dental/vision insurance, life insurance, and 401(k) to eligible employees.

#LI-RO

What You’ll Do

Are you a senior-level data scientist with a passion for building and deploying high-impact fraud or credit risk models? Koalafi is seeking an experienced Data Scientist to lead the development, deployment, and monitoring of machine learning models that sit at the core of our portfolio’s profitability. This role requires someone who thrives in an end-to-end environment—designing predictive models, operationalizing them in production, and ensuring they continue to perform in a dynamic market.

You will be a key contributor to Koalafi’s decisioning ecosystem, owning models that directly influence credit outcomes, fraud mitigation, and the financial performance of the company. Beyond technical expertise, you will bring strong business intuition, enabling you to translate modeling insights into strategic decisions. This position reports to the Manager of Data Science and regularly partners with senior leaders across Risk, Fraud, Analytics, and Technology.

Responsibilities

• Build, deploy, and maintain production-grade credit and fraud models that are foundational to our real-time decisioning platform and essential to portfolio profitability
• Own the full MLOps lifecycle—from feature engineering, model training, and experiment management to production deployment, performance monitoring, drift detection, and continuous optimization
• Architect and scale end-to-end ML pipelines, ensuring reliability, reproducibility, and seamless integration with core decisioning services
• Design robust model monitoring frameworks that enable tracing, profiling, explainability, and rapid root-cause analysis for production incidents or model degradation
• Partner with data science, risk, and engineering leaders to shape modeling strategy, improve credit policy, and strengthen fraud defenses in response to customer behavior and macroeconomic trends
• Drive continuous improvement of existing models, incorporating new data sources, advanced techniques, and rigorous validation processes
• Communicate complex model logic and insights to non-technical stakeholders, clearly linking modeling decisions to business outcomes and strategic priorities

About You

• 5+ years of hands-on experience building and deploying machine learning models, with a strong grasp of the end-to-end modeling lifecycle from feature engineering to validation and productionization
• 5+ years of professional experience writing performant, maintainable Python code in a collaborative production environment, leveraging core data science libraries like pandas, numpy, xgboost, and scikit-learn
• 2+ Years of experience working on Credit or Fraud risk models
• Proficient in SQL for querying, transforming, and analyzing large datasets, and comfortable working across relational databases and cloud-based data platforms
• Strong understanding of data structures, algorithms, and software engineering principles, and apply them to build robust and scalable data solutions
• Bachelor’s degree in a quantitative or STEM field (e.g., Statistics, Mathematics, Computer Science, Engineering) and demonstrate strong analytical and problem-solving skills in your work
• Location Requirement: This position requires regular in-person attendance at one of our two office locations (Richmond, VA or Arlington, VA). Candidates must already be located within a commutable distance to either location, as relocation assistance is not available at this time

Preferred Qualifications

• Advanced technical and analytical background, ideally with a Master’s or PhD in a quantitative or STEM field, and a strong understanding of probability, statistics, and predictive modeling algorithms (e.g., Boosting, Random Forests, Decision Trees, Bayesian models)
• Exposure to data and compute platforms such as Snowflake and Databricks
• Background in financial services or experience working in fast-moving, high-growth environments such as startups
• Experience with modern ML infrastructure and tooling, including MLOps frameworks (e.g., MLflow, BentoML), CI/CD automation, and model observability, monitoring, and lifecycle management
• Familiarity with large language models (LLMs) and their deployment in production environments

• Design, implement, and maintain ML deployment pipelines for scalable production systems.
• Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
• Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
• Partner with data scientists to transition models from research/prototype into production-ready deployments.
• Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
• Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
• Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
• Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.

• 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
• Proven experience deploying and maintaining machine learning models in production at scale.
• Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
• Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
• Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
• Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
• Strong understanding of MLOps best practices, monitoring, and automation.
• Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
• Strong communication and collaboration skills across technical and non-technical teams.

• Leverage 5+ years of experience in data science to design, implement, and optimize machine learning models and pipelines.
• Develop, fine-tune, and evaluate large language models (LLMs) for a variety of applications, ensuring accuracy, performance, and robustness.
• Collaborate with engineering and product teams to integrate AI/ML solutions into our platform in a scalable and maintainable way.
• Conduct applied research, staying current on advances in LLMs, generative AI, and data science methodologies, and translate them into practical solutions.
• Build end-to-end workflows, from data exploration and feature engineering to training, validation, deployment, and monitoring in production.
• Apply modern containerization and orchestration techniques (e.g., Docker, Kubernetes) to support reproducible experimentation and deployment.
• Work with cloud platforms (e.g., Databricks, AWS, GCP, Azure) to manage data pipelines, large-scale training jobs, and distributed systems.
• Collaborate across teams to ensure our AI capabilities align with platform goals and business needs.

• 5+ years of experience as a Data Scientist or Machine Learning Engineer, with proven success in deploying models to production.
• Hands-on experience with large language models (LLMs); fine-tuning experience strongly preferred.
• Strong background in Python and ML frameworks such as PyTorch or TensorFlow.
• Proficiency in containerization and orchestration technologies (Docker, Kubernetes).
• Experience with cloud platforms and ML ecosystems (Databricks, AWS, GCP, Azure).
• Familiarity with MLOps best practices, including model deployment, monitoring, and CI/CD for ML.
• Strong analytical and problem-solving skills, with the ability to translate research into production-ready solutions.
• Excellent communication and collaboration skills, with the ability to work effectively across product, engineering, and leadership teams.
• A proactive, self-starter mindset with a passion for applied research and innovation.