Build, Deploy, and Maintain AI for an Unpredictable WorldStriveworks helps organizations harness the power of artificial intelligence to solve real-world national security and business challenges by serving as the command center between data, models, and business outcomes. Founded by data scientists and engineers, Striveworks set out to make the journey from deployment to ongoing optimization simple and effective. With Striveworks, organizations aren’t just deploying AI—they’re building systems that remain reliable, adaptable, and ready to scale in an unpredictable world. Mission-critical operations require models that perform where they’re deployed, scale as workloads grow, and adapt rapidly as AI capabilities advance. Striveworks meets these demands, increasing reliability and performance while lowering costs—and enabling confident, data-driven decision-making in dynamic environments. The RoleAs a Staff Machine Learning Engineer at Striveworks, you will be challenged—and trusted—on day one to be both a core contributor and a customer-facing technical leader on the projects and direction of the company. In this mission-critical work, you will design AI-powered solutions, you will bring them into being—from data shaping and ingestion, through analytics, to end-user delivery—and you will inform future platform capabilities. You’re right for this opportunity if you value and possess technical expertise and enjoy pushing the boundaries of your own capabilities. You are passionate about applying both software engineering and data science to solve real-world problems. You are comfortable simultaneously leading a team, executing directly on critical elements of the plan, and presenting vision and outcomes to your customer. Your day-to-day will include: - Working with customers, engineers, and other stakeholders to define clear requirements that solve the customers’ problems and leverage the capabilities of our AI operations platform. - Translating requirements into a technical approach, design, scoping estimate, and execution plan. - Leading execution teams to achieve on-time completion of project deliverables mapped to customer business value while making key individual contributions throughout the process. - Designing, orchestrating, and automating complex data pipelines and algorithms within modern architectures (cloud, event-driven, microservices, etc.). - Guiding the development of machine learning models and custom analytics applied to image, video, text, geospatial, time series, and structured data. - Raising insights, opportunities, challenges, and feedback in order to improve group-level practice, capture reusable functionality, expand company opportunities, and accelerate time to value. - Conducting mission-critical fieldwork and interfacing with customers and other stakeholders at their work sites. This position offers a fully remote work environment, or you can work hybrid/on site at our office in northwest Austin, TX. You will be expected to travel up to 20% of the time. The Right FitIn addition to the specific skills and expertise detailed below, we are looking for individuals who share our values. Sharing a set of values allows us to move at the speed of trust. Collectively, we value a high-trust work environment where people respect each other and use candor kindly and constructively. We value work that intersects passion and perseverance, we geek out about the potential of our contributions, and we find joy in working hard on things that matter. Finally, we value taking ownership, having agency, and feeling individual responsibility for collective results. Here’s what we’re looking for: - Advanced degree in data science, machine learning, computer science, or a related discipline and 10+ years of relevant experience - Broad proficiency in programming languages common to machine learning (excellence in Python is essential, as is knowledge of libraries like TensorFlow, PyTorch, and scikit-learn) and systems programming (e.g., Go, Rust, C++, Java, Scala, etc.) - Proficiency in the design and delivery of algorithms, data structures, and production analytics and in the use of design patterns in cloud environments - Demonstrated experience defining, scoping, planning, and delivering complex technical solutions in production environments - Proficiency with modern software engineering tools and processes (Agile, version control, issue tracking, CI/CD, debugging, etc.) - Demonstrated ability to lead, manage, and mentor small cross-functional teams that work across office, remote, and customer sites - Ability to communicate complex topics with professionalism, competence, and clarity to internal and external stakeholders (both technical and non-technical) via documents, presentations, and conversations - Eligibility and willingness to obtain and maintain a Secret (or above) US security clearance - Due to the nature of this role, candidates must have US citizenship The Wish ListWe’re very interested in candidates who possess the above qualifications, and we appreciate and consider the addition of: - Experience with Kubernetes (k8s) and horizontally scaled architectures - Knowledge of messaging systems such as NATS, Kafka, RabbitMQ, or similar - Experience building AI agents and workflows - Experience with complex and varied data types, such as multi-spectral imagery, full motion video, acoustic or sonar signals, synthetic aperture radar, or hardware telemetry - Experience delivering technology solutions in secure government environments - Active Secret (or above) US security clearance The anticipated base pay range for this position is $200,000–$250,000/year. Striveworks’ total compensation package includes a competitive base salary, equity grants, and cash bonuses. The Benefits - Medical/dental/vision insurance - Voluntary life, long-term disability, accident, and hospital indemnity insurance - HSA and FSA (including dependent care FSA) plans - 401(k) plan - Unlimited PTO - Paid parental leave Check us out on Built In! Striveworks is an Equal Opportunity Employer and does not discriminate in employment on the basis of race, color, religion, belief, sex (including pregnancy and gender identity or expression), national origin, social or ethnic origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factors. Striveworks will not tolerate discrimination or harassment of any kind. If you require assistance or a reasonable accommodation in the application process, please contact Operations at hr@striveworks.us. In compliance with federal law, all persons hired will be required to verify their identity and eligibility to work in the United States and to complete an employment eligibility verification form upon hire. Striveworks is a participating employer in the E-Verify program.

WHO WE ARE Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com. THE ROLE At Zeta Global, we are spearheading innovations in data-driven marketing, leveraging advanced machine learning and AI to deliver unparalleled insights and performance. We are seeking a visionary Senior Product Manager to lead our Machine Learning Operations initiatives, focusing on developing retail-specific models and a user-friendly Model Workbench that empowers marketers to harness the power of Machine Learning without the need for deep technical expertise. Job Description: - Machine Learning Operations Leadership: Spearhead the development and maintenance of robust ML Ops processes to streamline model deployment, monitoring, and management. - Retail Model Innovation: Design and refine machine learning models tailored to the retail industry, enhancing our predictive capabilities and market understanding. - Model Workbench Development: Lead the creation of a Model Workbench platform that enables marketers to easily interact with and utilize machine learning models, incorporating user-friendly interfaces and tools. - Customer-Centric Product Development: Ensure all product development is guided by a deep understanding of customer needs and market demands, delivering solutions that are both intuitive and powerful. - Agentic Infrastructure Integration: Work towards integrating traditional ML with advanced generative models, focusing on explainability, actionable recommendations, and recursive training based on user interactions. Requirements: - Experience in Product Management: Proven track record of successful product management, ideally in a technology-focused environment. - Expertise in Machine Learning: Strong understanding of ML Ops practices, with experience in developing and managing Machine Learning models, particularly for retail applications. - Technical Proficiency: Comfortable working with both traditional and generative machine learning technologies, with a keen insight into model explainability and user interaction. - Strategic Thinking: Ability to think strategically about product development, integrating customer insights and market trends into actionable product features. - Communication Skills: Excellent communication skills with the ability to collaborate effectively across cross-functional teams and articulate product visions clearly to both technical and non-technical stakeholders. - Background in Retail Analytics: Direct experience or a strong understanding of the retail industry's data analytics needs. - Familiarity with Agentic Infrastructure: Knowledge of combining traditional ML models with generative models to enhance user interaction and model training. BENEFITS & PERKS - Unlimited PTO - Excellent medical, dental, and vision coverage - Employee Equity and Stock Purchase Plan - Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!! COMPENSATION RANGE The compensation range for this role is $150,000 - $190,000 depending on location and experience. PEOPLE & CULTURE AT ZETA Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression. We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/ ZETA IN THE NEWS! https://zetaglobal.com/press/?cat=press-releases #LI-ND1 #LI-Remote

Weave is looking for engineers hungry for fun challenges who can join our self-empowered teams and contribute in both technical and non-technical ways. You will be joining a team of talented developers that share a common interest in distributed backend systems, data, scalability, and continued development. You will get a chance to apply these, and other skills, to new and ongoing projects to make machine learning more approachable, data more available, and easier to discover and use by helping design how teams build out AI powered features at Weave. Our teams are cross-functional agile teams composed of a product owner, backend and frontend devs and devops. Teams are highly autonomous with the ownership and ability to act in Weave’s best interest. Above all, your work will impact the way our customers experience Weave while working closely with a highly skilled team to accomplish varying goals and cultivate our phenomenal culture. PURPOSE The Machine Learning Team's mission is to enable product innovation by making it painless for developers to build ai powered applications that require access to large sets of data. Machine learning is challenging but we are striving to democratize access to the tools and technology that powers it so teams can build cutting edge features safely and responsibly without a PhD in Data Science. As a Machine Learning Engineer on the team you’ll be building models for new products with emerging technologies, at scale. We handle data for hundreds of millions of people daily. - This position will be available for fully remote in the US with an opportunity to work in an office, if located near the Lehi, UT Headquarters. - Reports to: Engineering Director What You Will Own - Design and Develop machine learning infrastructure, tooling, and models to help teams deliver world class experiences. - Help product and development teams understand the data lifecycle and the inherent experimental nature of machine learning. - Build internal products and platforms to enable teams to incorporate AI into their features and customer facing products. - Consult with teams to help them understand common patterns, anti-patterns, and tradeoffs of machine learning. Guide them through creating excellent customer experiences end to end. - Build scalable, resilient services to support data integration, event processing, and platform extensions. - Contribute to the continued evolution of product functionality that services large amounts of data and traffic. - Write code that is high-quality, performant, sustainable, and testable while holding yourself accountable for the quality of the code you produce. - Coach and collaborate inside and outside the team. You enjoy working closely with others - helping them grow by sharing expertise and encouraging best practices. - Work in a cloud environment, considering the implementation of functionality through several distributed components and services. - Work with our stakeholders to translate product goals into actionable engineering plans. What You Will Need to Accomplish the Job - High integrity, team-focused approach, and collaboration skills to build tight-knit relationships across Weave with various roles and stakeholders. - Responsive person with a strong bias for action. - 5+ years of experience in any structured back-end language, i.e. Go, Java or Python (Go and Python experience is a plus). - Experience moving and storing TBs of data or 100M’s to 10B’s of records. - Experience building and deploying ML driven B2B multi-tenant applications in production environments. - Experience with common ML technologies such as Python, Jupyter, Workflow Engines (Dagster, MLFlow, KubeFlow, etc), DVC, Triton Server, LLMs, Postgres, and others. - Experience with modern ML tools and techniques such as LLMs, RAG, Prompt Engineering, Fine Tuning, multi-modal models, and others. - Experience with data labelling or annotation for audio or text use cases. - Understanding of distributed systems and building scalable, redundant, and observable services. - Expertise in designing and architecting systems for distributed data sets and services. - Experience building solutions to run on one or more of the public clouds (e.g., AWS, GCP, etc.). - Experience providing stable well designed libraries and SDKs for internal use. - Self driven and a thirst for learning in a quickly changing industry. - Demonstrated track record of delivering complex projects on time and have experience working in enterprise-grade production environments. - Strategic thinker with a strong technical aptitude and a passion for execution. What Will Make Us Love You - A background with data analysis, visualization, and presentation. - 3+ years of experience in data science, machine learning, or predictive analytics in addition to engineering experience. - Experience with natural language models, embeddings, and inference in production, at scale. - Experience with real-time audio models and voice use cases such as transcription, ASR pipelines with interruption detection, audio alignment, and speech synthesis. - Experience with emerging technologies such as Model Context Protocol (MCP). - Proficient understanding of containers, orchestrators, and usage patterns at scale including networking, storage, service meshes, and multi-cluster communication. Experience with Kubernetes or GKE and the Operator Pattern (GCP), specifically, a plus. - Experience with highly sensitive data such as PHI (HIPAA) and PII data. - Experience with automation and container based workflow engines. - Experience with GitOps, IaC, and configuration driven systems. - A preference for open source solutions. - A track record of clean abstractions and simple to use APIs. - Deep understanding of distributed data technologies such as streaming, data mesh, data lakes, warehouses, or distributed machine learning. - A desire to advance the state of the art with new and innovative technologies. - Enjoys working in a greenfield environment using rapid prototyping. - Enjoys working with open-ended, evolving problems, and domains. #LI-DNI At Weave, we use Artificial Intelligence (AI) tools to help us work more efficiently and create a smoother candidate experience. AI may assist with things like writing job descriptions, scheduling interviews, or reviewing applications against job-related criteria. For additional information, please review the External AI Policy Statement available on our Careers page. Weave is an equal opportunity employer that is committed to fostering an inclusive workplace where all individuals are valued and supported. We welcome anyone who is hungry to learn, problem-solve and progress regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other applicable legally protected characteristics. If you have a disability or special need that requires accommodation, please let us know. All official correspondence will occur through Weave branded email. We will never ask you to share bank account information, cash a check from us, or purchase software or equipment as part of your interview or hiring process.

We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the design and development of core ML models that power Instacart’s ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering. You will tackle fundamental challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer’s curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will also have the opportunity to shape our next-generation foundation model approach for ads ranking and contribute to cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID and domain language models. The Ads Response Prediction team owns all systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, sequential modeling and generative retrieval systems for next interaction recommendations, LLM integrations, relevance models, pCTR models, bidding models and incrementality models. The team optimizes for an efficient marketplace to ensure delightful customer shopping experience, desirable advertiser business outcome and Instacart Ads revenue. The team has strong ML infrastructure and MLOps support, including Delta/DBT-Spark data pipelines, Ray-based distributed training, and automated model deployment. This means you can focus your energy on advancing modeling science rather than building infrastructure. About the Job - Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces. - Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases. - Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning. - Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements. - Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction. - Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria. - Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives. About You Minimum Qualifications - PhD/Master in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field. - 6+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale. - Deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep & Wide, DeepFM, DCN, and multi-task learning formulations. - Strong foundation in causal inference, counterfactual reasoning, and training data bias mitigation. Ability to reason about selection bias, position bias, and propensity-based correction methods. - Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow, JAX). Fluency in data manipulation tools (SQL, Spark, Pandas). - Track record of formulating ambiguous problems into well-scoped ML research directions and delivering results through rigorous experimentation. - Strong written and verbal communication skills. Ability to explain complex modeling decisions to cross-functional stakeholders including product managers and data scientists. Preferred Qualifications - Experience in ads ranking or auction-based systems (pCTR, bid optimization, ROAS feedback loops, marketplace dynamics). - Hands-on experience with autoregressive sequence models for user behavior prediction, generative retrieval, or transformer-based ranking architectures. - Familiarity with learned representations such as Semantic IDs, product embeddings, or other approaches to reducing feature cardinality and cold-start challenges. - Experience with transfer learning or domain adaptation techniques (e.g., LoRA, adapter-based fine-tuning) applied to recommendation or ranking models. - Publication record in top-tier venues (KDD, WWW, RecSys, NeurIPS, ICML, SIGIR, or similar). - Experience mentoring junior engineers or shaping technical direction for a modeling team. - Familiarity with LLM-driven approaches to recommendation, including prompt-based personalization and AI-assisted model development (AutoML). Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here. Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here. For US based candidates, the base pay ranges for a successful candidate are listed below. CA, NY, CT, NJ $240,000—$253,500 USD WA $230,000—$243,000 USD OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI $221,000—$233,000 USD All other states $201,000—$212,000 USD