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

We are seeking a Machine Learning Engineer to join our team at MORSE.  You will play a pivotal role in designing, implementing, and managing complex ML algorithms and systems, with a focus on computer vision (CV) and other types of data. You will be responsible for acquiring truth data, integrating algorithms, testing algorithms, combining algorithms, reviewing literature to stay on top of the latest-and-greatest methods, analyzing data from field tests, and developing advanced algorithms.  MORSE’s AI & ML work crosses modalities, and experience or interest in the fields of Large Language Models (LLM), audio analysis, computer vision, and advanced reasoning is a plus. You will work with MORSE’s current team of engineers to transition algorithms to production, which may run on on-prem servers, on the cloud, or on a real-time embedded system. You will be part of our team working to accelerate our US National Security customers abilities to use natural language processing capabilities in mission-critical environments. 

Responsibilities: 

• Develop, fine-tune, train, and optimize Computer Vision algorithms processing tasks such as object detection and tracking.  

• Use MLOps tools for efficient experiment tracking, data management, and reproducibility 

• Write robust, efficient, and maintainable code 

• Track the latest advancements with machine learning research to bring new techniques and methodologies to MORSE 

• Conduct experiments and perform rigorous evaluations to assess the effectiveness and efficiency of CV models 

Skills and Requirements: 

• US CITIZENSHIP REQUIRED and the ability to obtain a U.S. Security Clearance 

• Masters or Ph.D. in Computer Science, Computer Engineering, Data Science, Aerospace, Mathematics, Physics, or related field 

• Proven experience in applying CV models, techniques, frameworks, and libraries to implement and fine-tune models 

• Proven experience testing and validating the performance of AI technologies in real-world applications 

• Proficiency in Python  

• Experience with cloud platforms (AWS and Azure)  

• Experience with Docker 

• Experience with MLOps tools such as Airflow, MLFlow, AimStack, etc. 

• Exceptional communication skills and the ability to work well with customers 

• Understanding of Department of Defense requirements and standards is a plus 

We are looking for a Manager, Customer Success Engineering (AI & Cloud Support) who is passionate about delivering exceptional support experiences and building high-performing teams.

As a Manager, CSE (AI & Cloud Support) at DigitalOcean, you will operate as a Support Manager leading a team of Customer Success Engineers (CSEs) supporting strategic customers across cloud infrastructure and emerging AI/ML workloads. Reporting to the Senior Manager of Customer Success Engineering, you will own day-to-day support operations, team performance, and customer experience outcomes.

This role is focused on driving operational excellence while building strong technical depth within the team across AI, GPUs, Kubernetes, Databases and core cloud offerings. You will play a key role in ensuring consistent support coverage, developing SMEs, and partnering cross-functionally with Product and Engineering to improve both customer experience and product quality.

The ideal candidate brings a deep understanding of cloud and AI/ML ecosystems, including machine learning operations (MLOps), and enterprise support, combined with a passion for innovation and automation, and a proven ability to lead teams in delivering “white-glove” experiences for strategic customers.

What You’ll Do:

Team Leadership & Development

• Lead, hire, train, mentor and develop a high-performing team of Customer Success Engineers (CSEs), driving accountability, performance, and career growth
• Establish performance metrics (KPIs/SLAs) and conduct regular 1:1s, performance reviews, and career development planning.
• Own end-to-end support operations, including queue management, escalations, and shift planning to ensure consistent 24x7 coverage.
• Drive improvements in key support metrics such as CSAT, response times, resolution times, and overall support quality.
• Build and strengthen technical expertise within the team across core areas such as Kubernetes (DOKS), Databases, Compute, and AI/ML workloads.
  
  

Strategic Customer Support

• Act as the ultimate point of technical escalation for our largest, most strategic enterprise customers across Cloud and AI/ML workloads, stepping in to manage critical incidents and high-severity (Sev1/Sev2) issues.
• Design and implement customized support plans, SLAs, and escalation pathways tailored to the needs of strategic accounts.
• Partner closely with Technical Account Managers (TAMs), Growth Account Managers (GAM) to conduct Executive Business Reviews (EBRs) and ensure customers are maximizing the value of our Cloud and AI/ML products.
• Proactively identify risks and opportunities within strategic accounts to improve customer experience, adoption, and retention.

Technical & Cross-Functional Operations

• Serve as the Voice of the Customer (VoC) to Product and Engineering teams, synthesizing support data to advocate for bug fixes, feature requests, and UX improvements.
• Own and continuously improve escalation protocols between AI/ML Support and CloudOps, Infrastructure Engineering, and Product — including Jira escalation routing, Sev1 bridge management, and post-incident documentation.
•  Own the development and maintenance of SOPs, escalation runbooks, HVC support playbooks, and knowledge base content — treating documentation infrastructure as a core operational lever for team scalability.
• Contribute to the vision for AI and automation within support—building intelligent tooling and driving the team toward an automation-first model to improve efficiency, scalability, and customer experience.
• Foster a culture of continuous learning, ensuring the team stays ahead of evolving cloud technologies, AI/ML frameworks, and industry trends.

Key Metrics:

• Customer Satisfaction (CSAT) for strategic accounts
• Time to Response and Resolution (TTR) for strategic customers
• Tier 1 resolution rate vs. escalation rate
• Time-to-escalation and engineering handoff SLA adherence
• SLA adherence and escalation response times
• Support productivity and quality (QA scores)
• Post-incident documentation completion rate

What You’ll Add to DigitalOcean:

• Experience: 5+ years of experience in Technical Support, Customer Success, or Technical Account Management within B2B SaaS, Cloud, or AI/ML environments ideally including experience supporting AI-native, high-growth companies with 24x7 production dependencies on GPU infrastructure.
• Leadership: 2+ years of people management experience leading technical, customer-facing teams, preferably in a high-growth, post-acquisition, or rapidly scaling environment.
• Technical Domain Expertise: Solid understanding of AI/ML concepts, including Generative AI, Large Language Models (LLMs), natural language processing (NLP), and MLOps. Deep familiarity with GPU infrastructure (NVIDIA H100/H200, bare metal GPU provisioning) and AI inference workloads is strongly preferred.
• Coding/Integration: Proficiency in reading and debugging code (Python preferred) and troubleshooting RESTful APIs and cloud architecture.
• Communication: Excellent verbal and written communication skills, with the ability to translate complex technical or AI concepts for diverse audiences OR to both highly technical engineers and non-technical business executives.
• Problem-Solving: Proven ability to remain calm under pressure and de-escalate high-stakes situations with enterprise clients.

Preferred Qualifications:

• Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and AI toolchains (e.g., LangChain, Hugging Face).
• Experience with major cloud platforms (AWS, Google Cloud, Azure) and their native AI/ML services.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• ITIL or equivalent service management certification

Compensation Range: 

• $125,000 - $153,000

*This is a remote role

JR: 2026-7692

#LI-Remote

We are looking for a Strategic Technical Account Manager who is passionate about making an impact, builds genuine relationships with senior stakeholders, and understands how to integrate infrastructure choices into business strategy.

As a Strategic Technical Account Manager (TAM) at DigitalOcean, you will join a dynamic team dedicated to revolutionizing cloud computing and AI. 

What You’ll Do:

Strategic Relationship Management

• Own the technical relationship with DigitalOcean’s highest-value customers, including Fortune 500 companies and global technology leaders.
• Serve as the trusted technical advisor and executive-facing point of contact, building deep, lasting relationships with VP- and SVP-level stakeholders.
• Drive regular technical and strategic alignment sessions (QBRs, EBRs), proactively shaping the customer’s roadmap to maximize the value of DigitalOcean infrastructure.
• Operate as a thought partner, challenging customers to modernize, optimize, and scale AI/ML and GPU workloads using cutting-edge cloud architecture patterns.

Executive Presence & Influence

• Engage with C-level and VP-level contacts with confidence, clarity, and authority.
• Deliver high-impact, boardroom-ready presentations on architecture decisions, cost-performance tradeoffs, and innovation opportunities.
• Navigate complex org structures, ensuring DigitalOcean maintains executive alignment and account control at every level.

Technical Consultation & Innovation Support

• Provide expert guidance on designing and scaling AI/ML, containerized, and GPU-intensive workloads on DigitalOcean.
• Assist customers with architectural planning, workload migration, performance tuning, and incident response.
• Collaborate directly with engineering and product teams to address customer roadblocks and influence the product roadmap based on enterprise needs.

Growth, Retention & Advocacy

• Own technical success across a portfolio of DigitalOcean’s largest and most strategic accounts.
• Identify opportunities for workload expansion, cost optimization, and operational efficiency.
• Act as the voice of the customer, providing structured feedback to influence DigitalOcean's offerings and go-to-market strategies.
  Lead cross-functional account planning with Account Management, Product, and Engineering.

What You’ll Add to DigitalOcean:

Enterprise Customer Experience

• 7+ years supporting enterprise or strategic accounts in a customer-facing technical role (TAM, Solutions Architect, or equivalent).
• Experience working with large-scale organizations like AMD, Intel, or similar, including managing executive-level relationships and navigating matrixed stakeholders.
• Deep understanding of cloud architecture for AI/ML, including GPU orchestration, inference/training pipelines, and model deployment.

Technical Depth

• Proficient in at least one major cloud platform (AWS, GCP, Azure) and familiar with DigitalOcean or other developer-focused platforms.
• Strong hands-on skills with Linux systems, networking, containers (Docker/Kubernetes), and automation (Terraform, Ansible).
  Programming/scripting in Python, Go, or similar—especially relevant to AI/ML and MLOps use cases.
• Understanding of AI/ML infrastructure stacks: PyTorch, TensorFlow, ONNX, Hugging Face, NVIDIA Triton, etc.
• Understanding of AI/ML infrastructure, including frameworks like PyTorch, TensorFlow, and ONNX, with hands-on experience optimizing GPU workloads on both NVIDIA (CUDA, TensorRT) and AMD (ROCm, MIOpen) platforms.
• Comfort with modern DevOps and data stack tools (CI/CD, GitOps, observability, pipelines).

Executive Presence & Communication

• Proven ability to engage with and influence executive decision-makers.
• Exceptional communication skills, including the ability to translate complex technical topics into strategic business impact.
• Comfortable presenting to large audiences, running technical workshops, and defending architectural decisions.

Bonus Points

• Cloud certifications (AWS SA Pro, GCP PCA, Azure Architect) or NVIDIA certifications.
• Experience with high-performance computing, edge AI, or hybrid/multicloud strategies.
• Background in startup or high-growth environments where agility and influence matter.
• Familiarity with DigitalOcean’s developer ecosystem, API, and service portfolio.

Compensation Range: 

•  $174,240 - $217,800

*This is a remote role

#LI-Remote

The Role

At SoFi, we’re on a mission to help our members get their money right. We are seeking a Principal Product Manager to lead the strategy and roadmap for our Fraud, Risk & ML Platform. This platform is the unified foundation for event-driven decisioning, risk-based interdictions, real-time feature stores, and self-serve ML capabilities across the enterprise.

You will shape SoFi’s risk and ML future, transforming complex backend processes into a seamless, self-serve platform that powers real-time decisioning for every SoFi product. Your goal is to accelerate organizational adoption of machine learning and expedite business growth by simplifying the integration of complex risk defenses that protect our members and the business.

What You’ll Do

• Strategic Roadmap: Own the multi-year vision for the Fraud, Risk & ML Platform, focusing on self-serve onboarding, reliability at scale, and superior developer experience.
• The "Model Factory": Direct the MLOps lifecycle to reduce model time-to-market from months to weeks. You will enable teams to build, deploy, and monitor ML models at enterprise scale.
• Platform Integration: Partner with internal product teams to identify friction in platform adoption and build features that eliminate integration pain points.
• Stakeholder Alignment: Drive onboarding for Fraud, AML, and Policy Engine use cases by aligning with Fraud, Risk, Data Science, and Infrastructure stakeholders on usage patterns, signals, features, and treatments, and guide execution teams to successfully deliver on those goals.
• Platform-as-a-Service: Drive Developer Experience (DX) as a core product feature; define clean APIs, versioning, SLAs, and documentation to reduce critical-path dependencies.
• Governance & Scalability: Establish a federated extension model for custom events, features, challenges, & actions to enable safe extensibility and cross-product reuse.
• Metrics & Impact: Define and track success metrics, including time-to-onboard, model deployment velocity, decision quality (e.g. precision/recall/F1), step‑up completion/abandon rates, and platform reliability.
• Flex into adjacent fraud initiatives, helping cover the gray space between domain detection/prevention improvements and platform onboarding/utilization.
• Evangelism: Educate partner teams on platform capabilities to ensure SoFi is fully leveraging centralized infrastructure rather than building bespoke silos.

What You’ll Need

• 7+ years of Product Management experience: with a focus on platform, infrastructure, or developer-facing products
• Technical fluency: Deep familiarity with APIs, event-driven systems (sync/async), high-throughput data pipelines, ML lifecycle (training, features, deployment, monitoring), risk decisioning engines/workflows
• ML Infrastructure: Proven experience operationalizing models in production using tools like Snowflake, Flink/streaming, Kafka, or AWS/SageMaker
• Platform mindset:  A strong bias for normalization and generic, self-serve capabilities over bespoke builds.
• Exceptional Communication & Evangelism: ability to translate complex risk policy, business goals, and technical constraints into precise platform requirements, while rallying teams around a clear, forward-looking platform roadmap and strategy.
• Adaptive leadership: team‑first, able to navigate the gray space between domain‑specific fraud detection and horizontal platform utilization
• Direct experience with fraud and risk detection and prevention challenges and techniques. 

Nice to Have

• Experience with risk decisioning, rules engines, risk mitigations and step-up challenges. 
• Experience in FinTech or highly regulated financial environments.
• Background in building or scaling Generative AI for task‑automating agents (agentic workflows)
• Direct experience partnering with Data Science and MLEs to productionalize real-time scoring.

Sr. Director Back-end Engineering – AI Infrastructure Orchestration

Company Introduction  

We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion-dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant and reliable force in South Korean commerce.  

We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurs surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day.  

Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world. 

Role Overview 

         Strategy and Leadership

• Define and execute the long-term vision and roadmap for the company’s AI infrastructure orchestration layer, aligning it with overall business and AI Services goals.
• Lead, mentor, and grow a high-performing engineering and operations team focused on AI infrastructure, and platform engineering.
• Manage budget and resource allocation for AI infrastructure delievrables.
• Act as a key liaison between AI Infra and other services owners and consumers, core engineering, Cloud infrastructure, and executive leadership.

        AI Infra Development and Operations

• Oversee the design, implementation, and maintenance of the core orchestration platforms for large-scale AI model training (e.g., distributed training, hyperparameter tuning) and deployment (e.g., containerization, serverless functions, edge deployment).
• Ensure reliability, security, and compliance of the AI infrastructure, meeting strict standards for data governance and model integrity.
• Establish Service Level Objectives (SLOs) and Key Performance Indicators (KPIs) for the AI platform services and lead efforts for continuous optimization and performance tuning.

       Success Metrics

       A successful Senior Director - AI Infrastructure Orchestration will be measured by:

• The time-to-market for AI Infar build, scale and operate
• The resource utilization rate and cost efficiency of the AI compute infrastructure.
• The reliability and uptime of the core AI platform services.
• The talent retention and development within the AI Infrastructure team.

       Technology and Architecture

• Select, evaluate, and integrate the core technologies required for the AI stack (e.g., Kubernetes, Kubeflow, Ray, ML frameworks, GPU/accelerator management, distributed file systems).
• Champion infrastructure-as-code (IaC) principles to manage and provision AI resources consistently and at scale.

Basic Qualifications  

        Education: Bachelor's or Master’s degree in Computer Science, Engineering, or a related technical field.

        Experience:

• 15+ years of progressive experience in software engineering, infrastructure, or platform operations.
• 5+ years of experience leading and managing technical teams, ideally in a Director or Sr. Director level or equivalent capacity.
• Deep, hands-on experience designing and operating large-scale distributed systems and cloud-native architectures
• Proven experience specifically with AI infrastructure orchestration (e.g., using Kubernetes, Kubeflow, or similar MLOps tools) and managing acceleratedcompute resources (GPUs, TPUs etc).
• 15+ years of Cloud backend engineering, Cloud Design, Deployment, DevOps
• 15+ years of experience leading system design, architecture leveraging Private Clouds and AWS and/or Azure/ GCP.
• 10+ years of demonstrable building and operating infrastructure as code. Infra Automation, Comfortable with many flavors of Linux
• 15+ year’s of experience in building high-performance, highly-available and scalable distributed systems in the cloud.
• 15+ year’s of experience in building and managing high-performance, highly-available and scalable Hybrid Cloud cloud environments .
• Excellent cross-group collaboration, outstanding verbal and written communication.

       Skills:

• Expert-level knowledge of containerization and orchestration (Docker, Kubernetes).
• Strong background in DevOps and MLOps principles and tooling.
• Proficiency in at least one modern programming language (e.g., Python, Go).
• Exceptional strategic planning, organizational, and written/verbal communication skills.

Preferred Qualifications:

• Prior experience managing infrastructure for training and inferecning of large language models (LLMs) or foundation models.
• Experience in a regulated industry with strict compliance requirements.
• AI Private Cloud -Building and operating

Pay & Benefits: 

Our compensation reflects the cost of labor across several US geographic markets. At Coupang, your base pay is one part of your total compensation.   

The base pay for this position ranges from $184,000/year in our lowest geographic market to $376,000/year in our highest geographic market. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience. 

 General Description of All Benefits:    

• Medical/Dental/Vision/Life, AD&D insurance   
• Flexible Spending Accounts (FSA) & Health Savings Account (HSA)  
• Long-term/Short-term Disability  
• Employee Assistance Program (EAP) program  
• 401K Plan with Company Match  
• 18-21 days of the Paid Time Off (PTO) a year based on the tenure  
• 12 Public Holidays  
• Paid Parental leave  
• Pre-tax commuter benefits  
• MTV - [Free] Electric Car Charging Station   

General Description of Other Compensation:  

“Other Compensation” includes, but is not limited to, bonuses, equity, or other forms of compensation that would be offered to the hired applicant in addition to their established salary range or wage scale.   

Details to Consider 

• This job posting may be closed prior to the stated end date for application if all openings are filled.
• Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process.
• Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws. 

Equal Opportunities for All

Coupang is an equal opportunity employer. Our unprecedented success could not be possible without the valuable inputs of our globally diverse team.

Privacy Notice  

• Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice located below: https://www.coupang.jobs/privacy-policy/  

Coupang is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, or gender (including pregnancy, gender identity, gender expression, sexual orientation, transgender status), national origin, age, disability, medical condition, HIV/AIDS or Hepatitis C status, marital status, military or veteran status, use of a trained dog guide or service animal, political activities, or affiliations, citizenship, or any other characteristic or class protected by the laws or regulations in the locations where we operate. If you need assistance and/or reasonable accommodation in the recruiting process due to a disability, please contact us at usrecruiting@coupang.com.      

About impact.com
impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products - Performance (affiliate), Creator (influencer), and Advocate (customer referral) - unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands - including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results.

Your Role at impact.com:

We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group. You'll own the design and deployment of machine learning models that optimize yield, pricing, and inventory allocation at scale — sitting at the intersection of data science, platform engineering, and marketplace economics.

This is a high-craft, high-ownership individual contributor role. You'll work end-to-end: architecting the data pipelines that feed your models, engineering the features that drive performance, and deploying real-time inference systems that make decisions at speed. Your work directly determines how effectively Impact's programmatic marketplace balances advertiser performance with publisher monetization — making this one of the highest-leverage technical roles in the business.

You'll collaborate closely with Product, Data Science, and Programmatic Delivery Engine Engineering, but you operate with significant autonomy. You're expected to bring both the modeling rigor of a data scientist and the production instincts of an ML engineer — and to be genuinely excited about both.

What You'll Do:

Yield Optimization & Pricing Models

• Design and deploy ML models that optimize auction pricing, bid shading, floor price setting, and yield across Impact's programmatic inventory.
• Build and iterate on real-time pricing algorithms that balance short-term revenue efficiency with long-term publisher and advertiser health.
• Develop and maintain feedback loops that allow pricing models to adapt to shifting market conditions, inventory mix, and demand patterns.
• Quantify the revenue impact of pricing model improvements; communicate tradeoffs between yield maximization, fill rate, and partner ROI to stakeholders.

Inventory Allocation & Supply Optimization

• Own ML-driven inventory allocation logic: routing, pacing, and matching supply to demand across partner segments, deal types, and campaign objectives.
• Build models that forecast inventory availability, demand curves, and clearing prices to support proactive allocation decisions.
• Identify and address inefficiencies in inventory utilization — including unsold inventory, suboptimal deal matching, and allocation imbalances across the publisher base.

Data Architecture & Feature Engineering

• Design and own the data infrastructure that feeds programmatic models: event pipelines, feature stores, training datasets, and real-time feature serving.
• Engineer high-signal features from auction logs, bid stream data, user signals, contextual attributes, and historical performance — at the scale of programmatic data volumes.
• Build robust data pipelines with production-grade standards: reliability, observability, versioning, and efficient reprocessing.

Real-Time Inference & Production ML

• Deploy models to production real-time inference environments; own latency, reliability, and throughput requirements for auction-time decision-making.
• Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers.
• Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic.
• Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration.

Experimentation & Performance Measurement

• Design and execute rigorous A/B and holdout experiments to measure the causal impact of model changes on yield, fill rate, advertiser performance, and publisher revenue.
• Build evaluation frameworks that go beyond offline metrics — validating model behavior in live auction environments where feedback signals are delayed or noisy.
• Translate experimental results into clear business narratives; present findings and recommendations to Product and business stakeholders.

Self-Learning Systems & Feedback Loops

• Research and implement adaptive, self-learning components within the programmatic stack — including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate.
• Design feedback mechanisms that close the loop between auction outcomes, model updates, and system behavior; reduce reliance on manual tuning and rule-based overrides.
• Stay current with advances in programmatic ML, auction theory, and online optimization; evaluate applicability to Impact's specific marketplace dynamics.

Cross-Functional Collaboration

• Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business requirements into modeling approaches and communicate technical tradeoffs clearly.
• Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse.
• Document models, architectures, and experimental findings to a standard that enables review, replication, and knowledge transfer across teams.

What You Bring:

Required

• Experience: 5+ years in data science, ML engineering, or quantitative research, with at least 2+ years building and deploying ML models in programmatic advertising, ad tech, marketplace optimization, or a closely related domain (e.g., real-time bidding, dynamic pricing, auction systems).
• Programmatic & marketplace depth: Demonstrated understanding of programmatic auction mechanics (RTB, header bidding, floor pricing, deal types, bid shading) and how ML can be applied to optimize outcomes across the supply-demand stack.
• Production ML engineering: Proven ability to take models from prototype to production independently — including real-time inference, monitoring, retraining pipelines, and SLO ownership.
• Data architecture: Experience designing and building data pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems.
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and large-scale data tools (Spark, Kafka, or equivalent streaming/batch frameworks).
• Experience with real-time feature serving and low-latency model deployment (REST APIs, gRPC, or streaming inference).
• Familiarity with production ML workflows: model versioning, drift monitoring, A/B testing, evaluation, and retraining.
• Experience processing and modeling at programmatic data scale: high-cardinality auction logs, bid stream data, impression and click events.
• Experimentation rigor: Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques).
• Communication: Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, Economics, or similar); Master's/PhD preferred.

Preferred / Nice to Have

• Direct experience with SSP, DSP, or exchange-side yield optimization — particularly floor price optimization, bid landscape modeling, or deal matching algorithms.
• Familiarity with auction theory (first-price vs. second-price dynamics, optimal reserve pricing, revenue equivalence) and its practical implications for programmatic ML.
• Experience with contextual bandits, multi-armed bandits, or reinforcement learning applied to real-time decisioning problems.
• Knowledge of online learning and adaptive algorithms in production environments with non-stationary data distributions.
• Familiarity with privacy-preserving ML techniques relevant to programmatic (differential privacy, federated learning, cookieless attribution modeling).
• Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training.
• Exposure to supply forecasting, inventory management, or capacity planning in programmatic or marketplace contexts.
• Familiarity with Impact's affiliate and partnership ecosystem, or prior experience at the intersection of performance marketing and programmatic delivery.

What Sets You Apart

• Marketplace intuition. You understand programmatic auctions not just as an engineer but as an economist — you think about incentive structures, equilibrium dynamics, and how model decisions ripple through the supply-demand stack.
• Full-stack ML ownership. You're as comfortable designing a feature store schema as you are tuning a gradient boosting model or debugging a latency spike in production. You own the whole chain.
• Feedback loop thinking. You don't just deploy models — you design the systems that make them smarter over time. You think about how today's decisions become tomorrow's training signal.
• Rigor under real-world constraints. You know how to run clean experiments in environments where feedback is delayed, data is noisy, and business pressures create tradeoffs. You don't let imperfect conditions become an excuse for imprecise thinking.
• Pragmatic delivery. You ship. You balance the perfect with the production-ready, iterate fast, and know when an MVP outperforms a six-month research project.
• Collaborative depth. You build genuine technical trust with engineering and product partners — not just by having good ideas, but by following through, communicating clearly, and making the integration easy.

Salary Range: $165,000 - $185,000 per year, plus an additional 5% variable annual bonus contingent on Company performance and eligible to receive a Restricted Stock Unit (RSU) grant.

*This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time.

Benefits and Perks: 

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Medical, Dental, and Vision insurance
• Office-only catered lunch every Thursday, a healthy snack bar, and great coffee to keep you fueled 
• Flexible spending accounts and 401(k)
• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses. 

impact.com is proud to be an equal-opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

#LI_Seattle

ABOUT THIS OPPORTUNITY

onX is seeking a Staff Software Engineer who will contribute to meaningful, real-world outcomes that have a lasting impact for customers and the business. In this role, you’ll focus on the design, development, and scaling of our core AI/ML platforms and product-facing services as part of the AI Foundations Team, supporting the integration of cutting-edge artificial intelligence solutions across onX's product lines. You’ll take ownership of your work, collaborate closely with partners across the organization, and help move initiatives forward in an environment where priorities evolve, learning is celebrated, and you’re encouraged to keep stretching your skills. This role will report to the Director of Engineering.

WHAT YOU'LL DO
^{Essential Job Duties & Functions}

• Work on the design, development, and deployment of large-scale AI systems and services, including models, pipelines, product experiences and infrastructure.
• Help Drive the technical strategy and roadmap for AI feature development, focusing on scalability, repeatability, reliability, and performance.
• Mentor and guide other engineers in best practices and productionizing AI technologies.
• Collaborate with Product and Data Science teams to translate business problems into technical requirements and deliver high-impact AI solutions (combining LLMs, Vector Databases, ML and other technology in novel ways to serve onX’ unique audience).
• Look for opportunities to embed AI as a repeatable mechanism in daily workflows by integrating experimentation into real work, and continuously refining its use with sound judgment and validation.
• Be a leader in AI safety, ensuring AI-enabled products are aligned with our values and mission.
• Look for opportunities to embed AI as a repeatable co-pilot in daily workflows by integrating experimentation into real work, and continuously refining its use with sound judgment and validation.

WHAT YOU’LL BRING

• Bachelor’s degree in Computer Science, Data Science, or a related technical field, or equivalent experience.
• 10+ years of professional software engineering experience, with at least 3 years focused on building and scaling production AI/ML systems, is required.
• Expertise in building scalable backend systems.
• Deep knowledge of MLOps practices, cloud infrastructure (AWS/GCP/Azure), and CI/CD pipelines for machine learning models and the common inference APIs/SDKs and frameworks.
• Experience with modern AI technologies, including Large Language Models (LLMs) and their various architectures, methods such as RAG, vector databases, and the common AI stack including TensorFlow/Keras, PyTorch, Transformers, etc.
• A strong curiosity for exploring new technologies, including AI
• A shared passion for and ability to demonstrate onX’s Company Values.
• Permanent US work authorization is a condition of employment with onX.

ADDED BONUSES

Though not required, we would be thrilled to consider candidates with any of the following:

• A history of engaging in AI development, from the early ML/NN (YOLO, Darknet) to the LLM era.
• Experience in monitoring and debugging production AI systems (inference, evals, judging, benchmarks) including cost and performance optimizations.
• Interest or experience in edge/on-device inference (MLX, JAX, transformers on TPU/NPU, etc.)
• Interest, experience or experimentation in training/fine-tuning transformer/diffusion models, dataset curation and captioning, etc.

THE ONX EXPERIENCE

onX is a distributed company with more than 400 employees across the country. We come together regularly to work in person and stay connected through regional basecamps and a culture that balances individual ownership with deep collaboration.

While we move quickly, we’re not a scrappy start-up. We operate with clear goals, structure, and frameworks that guide how we prioritize and execute. Priorities matter. While they may shift, data shapes how we evolve as our business, products, and the world around us change.

Clear priorities and structure don’t limit ownership - they make it possible. You’ll have the autonomy to define your work and make meaningful decisions within clear strategic boundaries. You’ll partner closely with others to solve complex problems and build solutions that scale across teams and platforms. Along the way, you’ll be supported with feedback, tools, and opportunities to grow your craft as you take on new challenges.

WHERE YOU CAN WORK

onX has created a thriving distributed workforce designed to foster connection, collaboration, and shared experience across several US locations. We have two HQ locations in Bozeman and Missoula, MT and established virtual workforce Basecamps in Austin, TX; Denver, CO; Kalispell, MT; Minneapolis, MN; Portland, OR; Salt Lake City, UT; and Seattle, WA.

HOW YOU’LL BE COMPENSATED

onX is committed to compensating all employees fairly and equitably for their contributions.  Applicants can expect to make between $169,000 to $211,000 upon hire. The pay range will vary based on experience, skills, certifications, and education among other factors as required in the job description. In addition, full-time onX employees are eligible for a grant of common share options with a vesting schedule and a potential annual bonus of 10% based on company performance.

WHAT WE’RE OFFERING YOU

• Competitive salaries, annual bonuses, equity, and opportunities for growth
• Comprehensive health benefits, including a no-monthly-cost medical plan
• Paid parental leave of 13 weeks for birthing parents and 5 weeks for non-birthing parents
• 401k matching at 100% for the first 3% you save and 50% from 3-5%
• Company-wide outdoor adventures and amazing outdoor industry perks
• Annual “Get Out, Get Active” funds to fuel your active lifestyle
• Flexible time away package that includes PTO, STO, VTO, and paid holidays

PERFORMANCE ESSENTIALS

In this role, success is driven by cognitive abilities such as concentration and problem-solving, essential for our computer-centric tasks. onX will explore reasonable accommodations to ensure that individuals with diverse abilities can fully engage in and contribute to the essential physical and mental functions of the job. If you need assistance or accommodation, please contact us at PC@onxmaps.com. 

Application Deadline is [day of week], [month] [day] at [0:00 pm] MST

Position open until filled. 

#LI-Remote

We are seeking a skilled MLOps & Agentic Platform Engineer. This role involves managing model registries, developing continuous training loops, and implementing A/B testing infrastructure. The ideal candidate will have a strong DevOps/MLOps background and be adept at deploying scalable microservices and building observability dashboards.

Responsibilities:

• Manage model registries, continuous training loops, and A/B testing infrastructure.
• Deploy agents as scalable microservices on Kubernetes.
• Build observability dashboards to track token usage, latency, and agent reasoning paths.

Qualifications:

• Strong DevOps/MLOps background (Kubernetes, Docker, Terraform).
• Experience with MLflow, Weights & Biases, or LangSmith.
• Knowledge of building scalable microservice architectures.

ABOUT THIS OPPORTUNITY

We are looking for a Data Scientist who views machine learning as a product, not just a research project. You will be a foundational member of our AI team, responsible for designing, building, and deploying sophisticated models that solve complex business problems. You aren't just an expert in XGBoost or Transformers; you are an expert in building the pipelines that make these models reliable, scalable, and impactful.

WHAT YOU'LL DO
^{Essential Job Duties & Functions}

• End-to-End Model Development: Design and implement the full ML lifecycle—from exploratory data analysis (EDA) and feature engineering to model selection, tuning, and validation.
• GCP Architecture: Leverage the full Google Cloud AI suite (Vertex AI, BigQuery ML, Dataflow, and Pub/Sub) to build robust, cloud-native ML solutions.
• MLOps & Engineering: Implement CI/CD for machine learning (CT - Continuous Training) to ensure models remain performant in production environments.
• Strategic Leadership: Partner with stakeholders to translate ambiguous business challenges into technical roadmaps. Mentor junior scientists and advocate for best practices in code quality and experiment tracking.
• Look for opportunities to embed AI as a repeatable co-pilot in daily workflows by integrating experimentation into real work, and continuously refining its use with sound judgment and validation.

WHAT YOU’LL BRING

• 5+ years in a professional Data Science role with a track record of deploying models at scale.
• Proficiency in Vertex AI, BigQuery, Cloud Storage, and Looker. Experience with Kubeflow is a major plus.
• Expert-level Python (pandas, scikit-learn, PyTorch/TensorFlow), Spark, and advanced SQL.
• Deep understanding of statistics and ML theory (e.g., Gradient Descent, Bias-Variance tradeoff, Bayesian inference).
• Experience with ETL/ELT processes, specifically using dbt.
• A strong curiosity for exploring new technologies, including AI
• A shared passion for and ability to demonstrate onX’s Company Values.
• Permanent US work authorization is a condition of employment with onX.

Our Tech Stack

• Data Warehouse: BigQuery
• Orchestration: Airflow (Cloud Composer)
• Modeling: Vertex AI Pipelines, JupyterLab
• Deployment: Docker, GKE (Google Kubernetes Engine)

Pro Tip for the Candidate: We value "clean code" as much as "smart math." If you treat your notebooks like production scripts and your Git history is a work of art, you’ll fit right in.

What Sets You Apart

• The "Product" Mindset: You understand that a model with 99% accuracy is useless if it costs more to run than the value it creates.
• Infrastructure as Code: You are comfortable with Terraform or similar tools to manage your cloud resources.
• Communication: You can explain the "why" behind a complex neural network architecture to an executive without using jargon.

THE ONX EXPERIENCE

onX is a distributed company with more than 400 employees across the country. We come together regularly to work in person and stay connected through regional basecamps and a culture that balances individual ownership with deep collaboration.

While we move quickly, we’re not a scrappy start-up. We operate with clear goals, structure, and frameworks that guide how we prioritize and execute. Priorities matter. While they may shift, data shapes how we evolve as our business, products, and the world around us change.

Clear priorities and structure don’t limit ownership - they make it possible. You’ll have the autonomy to define your work and make meaningful decisions within clear strategic boundaries. You’ll partner closely with others to solve complex problems and build solutions that scale across teams and platforms. Along the way, you’ll be supported with feedback, tools, and opportunities to grow your craft as you take on new challenges.

WHERE YOU CAN WORK

onX has created a thriving distributed workforce designed to foster connection, collaboration, and shared experience across several US locations. We have two HQ locations in Bozeman and Missoula, MT and established virtual workforce Basecamps in Austin, TX; Denver, CO; Kalispell, MT; Minneapolis, MN; Portland, OR; Salt Lake City, UT; and Seattle, WA.

HOW YOU’LL BE COMPENSATED

onX is committed to compensating all employees fairly and equitably for their contributions. For this position, applicants can expect to make between $126,000 to $149,000 upon hire. The pay range will vary based on experience, skills, certifications, and education among other factors as required in the job description. In addition, full-time onX employees are eligible for a grant of common share options with a vesting schedule and a potential annual bonus of 10% based on company performance.

WHAT WE’RE OFFERING YOU

• Competitive salaries, annual bonuses, equity, and opportunities for growth
• Comprehensive health benefits, including a no-monthly-cost medical plan
• Paid parental leave of 13 weeks for birthing parents and 5 weeks for non-birthing parents
• 401k matching at 100% for the first 3% you save and 50% from 3-5%
• Company-wide outdoor adventures and amazing outdoor industry perks
• Annual “Get Out, Get Active” funds to fuel your active lifestyle
• Flexible time away package that includes PTO, STO, VTO, and paid holidays

PERFORMANCE ESSENTIALS

In this role, success is driven by cognitive abilities such as concentration and problem-solving, essential for our computer-centric tasks. onX will explore reasonable accommodations to ensure that individuals with diverse abilities can fully engage in and contribute to the essential physical and mental functions of the job. If you need assistance or accommodation, please contact us at PC@onxmaps.com. 

Application Deadline is [day of week], [month] [day] at [0:00 pm] MST

Position open until filled. 

#LI-Remote

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

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

At Amperity, we’re an AI-first company helping the world’s leading brands create personalized customer experiences that build loyalty and fuel growth. Our AI-powered Customer Data Cloud,  built on multi-patented technology, enables more than 400 global brands, including Alaska Airlines, Wyndham Hotels & Resorts, and DICK’S Sporting Goods, to turn customer data into a competitive advantage.

We unlock the full value of customer data with simplicity and speed. AI is at the core of our platform and the way we work — from powering advanced identity resolution and predictive analytics to streamlining internal workflows and decision-making. It’s not just a capability; it’s part of our DNA.

Our team thrives on curiosity, collaboration, and transparency, fostering a culture where everyone can contribute, learn, and grow. We welcome talented individuals from diverse backgrounds to help us remove data bottlenecks, accelerate business impact, and push the boundaries of what AI can do for the world’s most innovative companies.

With offices in Seattle, New York City, London, and Melbourne, you’ll join a fast-growing team tackling critical challenges at the intersection of AI, data, and customer experience. Ready to make an impact? Let’s talk.

The Role

At Amperity, ML Engineers work in small, collaborative, and accountable teams. As a Lead ML Engineer, you'll lead ML projects that span multiple teams, guiding both the technical direction and the platform capabilities that power our AI-driven products. You'll work with Applied Scientists, Software Engineers, Product, and Customer Success teams to deliver production ML systems that create measurable customer impact. We are an AI-first company. We expect engineers to embrace AI assistance tools like Claude Code as a core part of their daily workflow. They use these tools to accelerate development, improve code quality, and velocity. We keep our processes lightweight, our experimentation rigorous, and our focus on delivering value through machine learning.

Interesting Problems

We're solving tough problems at the intersection of large-scale data, AI, and user experience. Some of the challenges you might work on include:

• Architect ML platform components—feature stores, model registries, and serving infrastructure—that help teams across the organization to deploy models reliably and at scale.
• Build automated training and deployment pipelines that support model improvement for data drift and model degradation.
• Design real-time and batch feature engineering systems that power identity resolution, customer segmentation, and predictive models at enterprise scale.
• Improve model inference latency to deliver ML predictions that meet strict Service level agreements while managing infrastructure costs.
• Establish MLOps best practices, SLOs, and operational standards that ensure production ML systems are reliable, observable, and maintainable.

About You

You're a technical visionary who combines deep ML engineering expertise with systems building and experience building infrastructure that supports and scales several types of machine learning models. You take ownership of ML systems end-to-end—from model development through production operation—and help set the technical direction for ML engineering across the organization. You embrace AI-first practices, using tools like Claude Code to accelerate your work and advocating for their adoption across teams. You value influence, mentorship, and well-reasoned decisions.

• 8+ years of experience building production ML systems, with experience designing ML infrastructure and platforms.
• Technical leadership experience driving ML platform evolution or major ML projects across multiple teams.
• Expertise in ML deployment patterns, model serving, feature engineering, and monitoring/observability for ML systems.
• Software engineering skills with experience in Python and familiarity with ML frameworks (e.g. XGBoost, PyTorch, PySpark).
• Experience with cloud-native ML infrastructure, containerization, and orchestration (Kubernetes, Docker).
• Enthusiastic about AI-first development practices, with experience using AI coding assistants to accelerate engineering workflows.
• Turn ambiguous ML infrastructure problems into actionable plans, and guide teams through delivery.
• Experience aligning ML technical strategy with our priorities and customer needs.
• Mentor engineers, and improve how we build and operate ML systems.

Technologies To Know

We don't expect you to have experience with everything we use—but if you're excited about learning, you'll do great here. You'll influence and learn:

• Large-scale data engines like Apache Spark, Presto, and Kafka.
• MLOps tooling including MLflow, feature stores, and model serving frameworks.
• Cloud-native infrastructure built with Kubernetes and Terraform, deployed across multiple cloud providers.
• Functional programming languages including Clojure and Python for ML pipelines.
• Machine learning models for entity resolution, classification, and customer analytics.
• AI coding assistants (such as Claude Code) as part of our daily development workflow.

Location

Seattle, WA 

Our hybrid work model includes three days in the office each week, providing a mix of in-person collaboration and remote flexibility

Compensation

Base Salary:  $190,000-$260,000. Individual compensation within this range will depend on several factors, including your skills, experience, education/training, and the level at which you join. We also consider internal equity, market conditions, and overall business needs.

Cash Incentives: Cash incentives are also available.

Stock Options:  The opportunity for ownership is an exciting part of Amperity’s total compensation package. Every employee at Amperity receives a new-hire equity grant, commensurate with the scope of their position.

Benefits

We offer all the benefits you'd expect from a great place to work: 100% employee healthcare coverage, transportation subsidies, a comfortable work environment with plenty of snacks, and other employee experience perks like events and activities, both in-person and remote. We also offer self-managed PTO and the flexibility to do your best work in the way that works for you. We provide an inclusive environment where you'll be challenged to find and unlock your full potential, surrounded by a team of world-class people driving for excellence. For more details on our benefits, please see our US Benefits & Perks Guide.

Amperity is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, sex (including pregnancy, childbirth, and reproductive health choices), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as someone with a disability, political views or activity, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local law.

About impact.com
impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products—Performance (affiliate), Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics, rely on impact.com to power more than 225,000 partnerships that deliver measurable business results.

Your Role at impact.com:

We are looking for a Senior Product Manager (PM) to drive strategy and execution across our data science and AI ecosystem — from the machine learning platform and data foundations to applied ML and AI-powered products that deliver measurable business impact.

This is a pivotal role at the intersection of data science, machine learning engineering, and product strategy.  You’ll partner with data scientists, ML engineers, data engineers, and business leaders to define and deliver the capabilities that enable our teams to develop, deploy, and scale AI-driven insights and automation across the company.

What You'll Do:

ML Platform & Infrastructure

• Own the product vision and roadmap for our ML Platform, ensuring teams can easily experiment, train, deploy, and monitor models at scale.
• Partner with MLOps and Data Engineering to deliver shared components such as model registry, feature store, experiment tracking, and evaluation frameworks.
• Drive adoption of modern AI tooling (e.g., vector stores, LLM orchestration frameworks, GPU-based training environments).
• Define SLAs, performance metrics, and developer experience standards for the platform.

Data Strategy & Insights Enablement

• Lead the strategy for transforming raw data into structured, high-quality, and meaningful assets that power insight-driven product features such as competitive benchmarking, performance diagnostics, and opportunity identification.
• Define and evolve the data models, taxonomies, and semantic layers that make data consistent, interpretable, and ready for analysis.
• Partner with data science and engineering teams to ensure insights are accurate, contextual, and actionable, enabling teams and clients to make data-informed decisions confidently.

Applied ML & AI Use Cases

• Identify and prioritize high-value machine learning applications — such as recommendation systems, search relevance, anomaly detection, forecasting, and LLM-powered insights.
• Translate complex data science opportunities into productizable features with measurable outcomes.
• Collaborate with business and engineering teams to bring ML models into production — ensuring performance, interpretability, and ethical AI practices.
• Own success metrics (e.g., model ROI, adoption, latency, accuracy) and establish iteration loops with DS teams.

Strategy & Leadership

• Define the long-term strategy for AI/ML as a product capability — balancing foundational investments with applied innovation.
• Partner with senior leadership to align the ML roadmap with company priorities and data maturity.
• Champion best practices for experimentation, responsible AI, and data-driven decision making.
• Influence executive stakeholders through clear storytelling, value framing, and impact 

What You Bring:

• 6–10+ years of product management experience, including at least 3 years focused on data, ML, or AI products.
• Strong understanding of machine learning lifecycle, from experimentation to deployment and monitoring.
• Proven success managing technical platform products (ML infrastructure, data pipelines, or developer tools).
• Familiarity with cloud ML ecosystems (Vertex AI, Databricks, or similar).
• Ability to communicate fluently across data science, engineering, and business teams — translating complexity into clear priorities.
• Demonstrated ability to manage cross-functional roadmaps, prioritize tradeoffs, and measure product success through adoption and business impact.

Salary Range:  $130,000 - $150,000 per year, plus an additional 5% variable annual bonus contingent on Company performance with eligibility to receive Restricted Stock Unit (RSU) grant.

*This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time.

Benefits and Perks: 

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Medical, Dental, and Vision insurance
• Office-only catered lunch every Thursday, a healthy snack bar, and great coffee to keep you fueled 
• Flexible spending accounts and 401(k)
• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses. 

impact.com is proud to be an equal-opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

#LI_Seattle

About the role

You will deeply understand the research workflows of our Finetuning teams and automate the high-friction parts – turning days of manual experimentation into hours. You’ll build the tools and infrastructure that enable researchers across the organization to develop, evaluate, and optimize reward signals for training our models. Your scalable platforms will make it easy to experiment with different reward methodologies, assess their robustness, and iterate rapidly on improvements to help the rest of Anthropic train our reward models.

This is a role for someone who wants to stay close to the science while having outsized leverage. You'll partner directly with researchers on the Rewards team and across the broader Fine-Tuning organization to understand what slows them down: running human data experiments before adding to preference models, debugging reward hacks, comparing rubric methodologies across domains. Then you'll build the systems that make those workflows 10x faster. When you have bandwidth, you'll contribute directly to research projects yourself. Your work will directly impact our ability to scale reward development across domains, from crafting and evaluating rubrics to understanding the effects of human feedback data to detecting and mitigating reward hacks. 

We're looking for someone who combines strong engineering fundamentals with research experience – someone who can scope ambiguous problems, ship quickly, and cares as much about the science as the systems.

Note: For this role, we conduct all interviews in Python.

Responsibilities

• Design and build infrastructure that enables researchers to rapidly iterate on reward signals, including tools for rubric development, human feedback data analysis, and reward robustness evaluation
• Develop systems for automated quality assessment of rewards, including detection of reward hacks and other pathologies
• Create tooling that allows researchers to easily compare different reward methodologies (preference models, rubrics, programmatic rewards) and understand their effects
• Build pipelines and workflows that reduce toil in reward development, from dataset preparation to evaluation to deployment
• Implement monitoring and observability systems to track reward signal quality and surface issues during training runs
• Collaborate with researchers to translate science requirements into platform capabilities
• Optimize existing systems for performance, reliability, and ease of use
• Contribute to the development of best practices and documentation for reward development workflows

You may be a good fit if you

• Have prior research experience 
• Are excited to work closely with researchers and translate ambiguous requirements into well-scoped engineering projects
• Have strong Python skills
• Have experience with ML workflows and data pipelines, and building related infrastructure/tooling/platforms
• Are comfortable working across the stack, ranging from data pipelines to experiment tracking to user-facing tooling
• Can balance building robust, maintainable systems with the need to move quickly in a research environment
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Care about the societal impacts of your work and are motivated by Anthropic's mission to develop safe AI

Strong candidates may also have experience with

• Experience with ML research
• Building internal tooling and platforms for ML researchers
• Data quality assessment and pipeline optimization
• Experiment tracking, evaluation frameworks, or MLOps tooling
• Large-scale data processing (e.g., Spark, Hive, or similar)
• Kubernetes, distributed systems, or cloud infrastructure
• Familiarity with reinforcement learning or fine-tuning workflows 

Representative projects

• Building infrastructure that allows researchers to rapidly test new rubric designs against small models before scaling up
• Developing automated systems to detect reward hacks and surface problematic behaviors during training
• Creating tooling for comparing different grading methodologies and understanding their effects on model behavior
• Building a data quality flywheel that helps researchers identify problematic transcripts and feed improvements back into the system
• Developing dashboards and monitoring systems that give researchers visibility into reward signal quality across training runs
• Streamlining dataset preparation workflows to reduce latency and operational overhead

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