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

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: - Trust is essential to the success of the Airbnb platform for both guests and hosts. The Safety team invests significant effort in keeping the community safe and building this trust. - We are responsible for detecting reservations with party intent that could violate Airbnb platform policies. Our goal is to block these users before check-in to prevent critical life safety incidents for our guests and hosts. The Difference You Will Make: - Guests provide signals about potential party intent during the booking process. Combining these signals with internally available data offers a significant opportunity to develop various heuristics and machine learning models to predict the likelihood of unauthorized parties. - As a Senior Machine Learning Engineer on the Trust & Safety team, you will collaborate with product managers, data scientists, designers, and customer service operations to innovate new ways to predict and prevent critical life safety and property damage incidents on the platform. - The problems you tackle will often be vague and ambiguous, requiring you to possess a strong curiosity to deeply investigate various user patterns and behaviors to understand how and where these life safety incidents might occur. A Typical Day: - Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases. - Working together with a wide variety of business functions to stop critical life safety and property damage incidents in real time. - Creating new holistic machine learning model detection strategies by collaborating with other trust and safety prevention teams around the Trust Organization. - Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for fraud detection and mitigation. - Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases. Your Expertise: - 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields - A Bachelor’s, Master’s or PhD in CS/ML or related field - Strong programming (Scala / Python / Java/ C++ or equivalent) and data engineering skills - Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization) and domains (eg. natural language processing, computer vision, personalization and recommendation, anomaly detection) - Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive) - Industry experience building end-to-end Machine Learning infrastructure and/or building and productionizing Machine Learning models - Exposure to architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models) - Experience with test driven development, familiar with A/B testing, incremental delivery and deployment. - Experience with the Trust and Risk domain is a plus. Your Location: This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com. Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process. We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application. How We'll Take Care of You: Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits. Pay Range $191,000—$223,000 USD

• Build and maintain data pipelines for large video generation models, including data ingestion, parsing, filtering, preprocessing, and dataset curation at scale, using tools such as AWS S3 and DynamoDB. • Design and run annotation workflows across platforms such as MTurk, Prolific, and Mechanical Turk, including task design, quality control, and label validation. • Train, evaluate, and improve smaller supporting models used for data filtering, quality assessment, preprocessing, or other parts of the ML pipeline. • Partner closely with research and engineering teams to turn experimental workflows into scalable, repeatable systems that support model training and evaluation. • Own data quality across the pipeline by identifying bottlenecks, failure modes, and low-quality sources, and continuously improving tooling and processes. • Build internal tools and automation that make it easier to prepare datasets, launch annotation jobs, monitor outputs, and support model development end to end. • Drive larger pipeline projects from start to finish, such as new dataset creation efforts or upgrades to labeling and preprocessing infrastructure. • Work within a Kubernetes-based training infrastructure, ensuring datasets are properly prepared, formatted, and delivered to training clusters. • Profile and optimize research model inference scripts used in preprocessing steps, ensuring that model-driven filtering and transformation stages run within practical time and cost constraints when applied to large-scale raw data.

We are looking for a Machine Operator to join our dynamic and fast-growing team and be part of the programming, operating, monthly preventative maintenance, and keeping a clean work area while adhering to all company safety policies. Machine operators will work closely with other members of the shop to ensure the completion of correct and accurate parts What You'll do: - Operate forklifts safely and efficiently in a shop environment - Read and interpret blueprints, drawings, and work instructions accurately - Use measuring tools (tape measures, calipers, etc.) with precision to ensure quality standards - Operate a variety of shop equipment, including waterjet, plasma, laser, press brake, plate roll, shear, mill, and band saw - Perform MIG and/or TIG welding on mild steel to meet project specifications - Maintain a clean, safe, and organized work environment - Assume other duties as assigned The Experience we are looking for: - 5+ Years of experience operating multiple forms of relevant shop equipment - 2+ Years of welding experience preferred (Mig or Tig) - Hands-on experience in a metal fabrication or manufacturing shop environment - Strong ability to read blueprints and use measuring tools accurately Additional Requirements: - Willingness to travel up to 10% across the continental U.S. - High school diploma or GED required - Valid and current driver’s license with a clean driving record - Must successfully complete a background check and pre-employment/random drug tests - Legally authorized to work in the United States Bonus Points for: - Prior experience with waterjet - Aluminum welding experience Why Join RMS Energy: We’re not just another power services company. We’re a tight-knit, mission-driven team that values safety, teamwork, innovation, and continuous growth - Competitive Compensation – Overtime potential and merit-based raises - Flexible Work Environment – Remote work with project-based travel - Full Benefits – Medical, dental, and vision coverage fully paid for employees, starting the month after hire - Steady Employment & Career Growth – Be part of a fast-growing company with promotion potential - 401(k) with Company Match – Traditional & Roth options + free investment guidance - Top-Tier Equipment – Provided to support you in the field - Compensated Travel Time plus Per Diem – Earn while seeing new places - Education Support – Paid training, certifications, and industry memberships - Generous PTO – Paid vacation, holidays, and sick leave - Employee Assistance Program – Legal, financial, and mental wellness support Want to be part of something meaningful? Apply today and join a team where People, Purpose, and Power come together – your future starts here. RMS Energy is an Equal Opportunity Employer. We believe diverse teams drive better outcomes, and we’re committed to creating an inclusive environment where all employees feel valued and empowered. For more information about RMS Energy, please visit www.rmsenergy.com.