Who We Are

Babylist is the leading registry, e-commerce, and content platform for growing families. More than 9 million people shop with Babylist every year, making it the go-to destination for seamless purchasing, trusted guidance, and expert product recommendations for new parents and the people who love them. What began as a universal registry has grown into a full ecosystem for new parents, including the Babylist Shop, Babylist Health, and a flagship showroom in Los Angeles. Hundreds of brands in baby and beyond partner with Babylist to engage meaningfully with families during one of life’s most important transitions. With over $1 billion in annual GMV, and more than $500 million in 2024 revenue, Babylist is reshaping the $320 billion baby product industry. We’re helping parents feel confident, connected, and cared for at every step. As we build the generational brand in baby, our mission remains simple: to connect growing families with everything they need to thrive.To learn more, visit www.babylist.com.

Our Ways of Working

Babylist is remote-first with team members across the U.S. and Canada who move fast, think smart, and use AI as part of how they work every day — not as an experiment, as an expectation. We come together twice a year to build the relationships behind the work, and we hire people who are genuinely excited about what's possible and prove it through how they show up.

How We Build

Babylist is in the middle of a fundamental shift in how software gets made, and we are not tiptoeing into it. We are rebuilding our engineering culture around a simple belief: AI changes everything. How teams are structured, how decisions get made, how fast ideas become working software. Our engineers own problems end to end, working directly with product, design, and business partners with short feedback loops and real stakeholder access. We ship, learn, and iterate fast. When something is not working, we throw it out and start over — project failure and personal failure are not the same thing here. AI tools are as natural to our workflow as an IDE or version control. We are not exploring this, we are living it. Our engineers use AI to explore tradeoffs, pressure-test designs, and move from problem to solution in hours instead of days. They generate code with AI so they can stay focused on the decisions that actually require human judgment — not the routine ones. More velocity means more time for craft: better test coverage, stronger architecture, and deeper customer understanding. We hold ourselves to a higher quality bar because of AI, not in spite of it. We are building this playbook in real time, and we are looking for people who want to build it with us. If you have already changed how you work because of AI — or you are ready to — and you care more about shipping something great than following a prescribed process, we should talk.

Our Tech Stack 

• React, Native iOS and Android
• Ruby on Rails
• AWS, Terraform
• MySQL
• Sidekiq
• Redis
• Shopify

What the Role Is

As Babylist’s Senior Engineering Manager, Machine Learning & Discovery+Site Monetization, you’ll lead the team driving the next era of our machine learning and personalization efforts across the product. This is a hands-on leadership role for a manager who remains technically fluent while scaling people, systems, and impact. You’ll manage and grow a high-performing ML-focused pod spanning product recommendations, search, PLPs, and site monetization, with a clear mandate: maximize revenue and user value across surface areas through personalization.

You’ll guide the team on technical direction behind our homepage feed, “add next” experience, and other core personalization surfaces, while building the foundations for long-term ML excellence at Babylist.

Who You Are

• 3+ years of experience as a front-line manager delivering production-grade software or ML systems
• 5+ years of experience as an individual contributor in ML or Data Science, plus proven experience leading teams that build end-to-end machine learning systems, ideally in personalization, recommendations, or consumer-facing ML products
• Deep familiarity with the Python ML ecosystem (e.g., pandas, sklearn, xgboost, PyTorch) and comfortable reviewing designs, unblocking technical challenges, and setting high technical standards
• Experience supervising ML Engineers and/or Data Scientists; bonus points for experience managing Software Engineers
• Strong understanding of the full ML lifecycle, including data pipelines, workflow orchestration (e.g., Airflow), deployment, monitoring, and iteration in production
• Product-minded: passionate about technology, but primarily motivated by solving real user problems and driving business impact
• Experience working with large-scale, real-time or near-real-time data systems
• Comfortable owning and evolving the ML tech stack end-to-end, from data and modeling through production systems
• Background in consumer-facing products; experience in e-commerce, marketplaces, or complex user journeys is strongly preferred
• You're genuinely excited about what AI can do - not just as a concept, but as something you want to get your hands on. At Babylist, every team uses AI daily, and we're looking for people who lean in.

How You Will Make An Impact

• Build and lead the ML & personalization function: Hire, mentor, and support a multidisciplinary team (Machine Learning Engineers, Software Engineers, Product Manager, and occasional Designer), shaping the roadmap, standards, and culture for ML at Babylist
• Own high-impact ML initiatives: Deliver personalization systems that improve discovery, engagement, and monetization across key surfaces, directly impacting business outcomes
• Set technical and algorithmic direction: Define modeling approaches, data strategy, and system architecture in partnership with senior technical leaders, balancing experimentation with scalable execution
• Be hands-on when needed: Review ML/DS designs and code, build proof-of-concept models, and conduct your own data science research to de-risk ideas and accelerate progress
• Develop talent: Coach engineers on technical growth, career progression, and performance, combining strong technical standards with empowerment
• Drive cross-functional alignment: Partner closely with Product, Design, Analytics Engineering, and Data to translate ambiguous business problems into clear technical direction
• Understand the full funnel: Develop a deep understanding of Babylist’s user journeys, user feedback loops, and financial models — and how ML systems influence them end-to-end

About Compensation

We use a market-based approach to compensation. The starting salary range for this role is:

USD - $250,000 to $308,000
CAD - $230,000 to $288,000

Your starting salary will be based on your location, experience, and qualifications, with increases over time tied to performance, role growth, and internal pay equity.

Why You Will Love Working At Babylist

Our Culture

• We work with focus and intention, then step away to recharge
• We believe in exceptional management and invest in tools and opportunities to connect with colleagues
• We build products that positively impact millions of people's lives
• AI tools are as natural to how we work as your IDE or version control — we're not exploring this, we're living it.

Growth & Development

• Competitive pay and meaningful opportunities for career advancement
• We believe technology and data can solve hard problems
• We're committed to career progression and performance-based advancement

Compensation & Benefits

• Competitive salary with equity and bonus opportunities
• Company-paid medical, dental, and vision insurance
• Retirement savings plan with company matching and flexible spending accounts
• Generous paid parental leave and PTO
• Remote work stipend to set up your office
• Perks for physical, mental, and emotional health, parenting, childcare, and financial planning

Important Notices

Recorded Interviews Babylist uses an interview recording tool to record and transcribe interviews for evaluation purposes in accordance with applicable privacy laws. By participating in an interview, you consent to this recording and transcription.

Interview Integrity At Babylist, every team uses AI daily and we love it. During interviews though, we want to see you — your thinking, your problem-solving, your creativity. All interviews and assessments should be completed independently without AI tools or third-party assistance unless we tell you otherwise. We'll always be clear when AI is welcome. Misrepresentation during the process may result in removal from consideration.

Protect Yourself from Scams All official communication comes from the Babylist Talent Team via @babylist.com email addresses. We will never ask for payment, bank information, or personal financial details. If you receive outreach via WhatsApp, Telegram, or a non-Babylist email, it's not us. Verify open roles on our careers page.

Connections at Babylist In line with our conflict of interest policy, please let us know if you have a family member or close personal relationship with a current Babylist employee. This helps us keep our process fair for everyone.

Text Message Updates You may opt in to receive SMS updates about your application. Opting out won't affect your status. Message and data rates may apply. Reply STOP to unsubscribe or HELP for assistance. See our Privacy Policy for details.

Reddit has a flexible first workforce. At Reddit we continue to grow our teams with the best talent. We're completely remote friendly and will continue to be after the pandemic.

Advertising is Reddit’s primary revenue driver and we have an ambitious goal to turn it into a massive business. Although several large digital ad platforms already exist, advertisers are being increasingly drawn to Reddit because of our passionate communities. We believe our community-centric platform has created the opportunity for Reddit to build a highly differentiated ads business.

About the Ads Data Science Team:

The Ads Data Science team at Reddit leverages data to maximize advertiser value on Reddit through robust data foundations, metrics, and strategic insights generated through experimentation and cutting-edge DS methods. 

We work on a wide range of challenging problems in the areas of ads targeting, relevance modeling, auction optimization, measurement, and user experience. We are a highly collaborative team of passionate data scientists and engineers who are constantly pushing the boundaries of what's possible with machine learning and statistical modeling.

About the Role:

We are looking for a highly motivated and experienced Data Scientist to join our growing Ads Data Science team. As a Data Scientist, you will play a key role in developing as well as applying cutting-edge DS models/methods to improve our understanding of the dynamics that drive the success of our advertising platform, and identify opportunities to accelerate that success. You will work closely with stakeholders across product, engineering, sales, marketing, and data to identify opportunities, define metrics, and build solutions that drive significant impact for Reddit.

Responsibilities:

• Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform
• Develop ML models & DS methods to for improved anomaly detection, prediction, pattern recognition
• Communicate findings and recommendations to stakeholders across the organization
• Collaborate with product, engineering, sales, and marketing partners to define product and program requirements and translate them into data science solutions
• Stay up-to-date on the latest advancements in machine learning and data science

Qualifications:

• Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research
• For M.S. holders: 3+ years of industry experience in applied science or data science roles
• For Ph.D. holders: 2+ years of industry experience in applied science or data science roles
• Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design
• Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus
• Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch
• Experience with SQL and relational databases
• Excellent communication and presentation skills
• Passion for Reddit and the online advertising industry

Bonus Points:

• Experience with online advertising and ad tech
• Experience with causal inference and A/B testing
• Contributions to open-source projects or publications in relevant conferences or journals

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Comprehensive Medical Benefits & Health Care Spending Account
• Registered Retirement Savings Plan with matching contributions
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

#LI-Remote

Reddit has a flexible first workforce. At Reddit we continue to grow our teams with the best talent. We're completely remote friendly and will continue to be after the pandemic.

Advertising is Reddit’s primary revenue driver and we have an ambitious goal to turn it into a massive business. Although several large digital ad platforms already exist, advertisers are being increasingly drawn to Reddit because of our passionate communities. We believe our community-centric platform has created the opportunity for Reddit to build a highly differentiated ads business.

About the Ads Data Science Team:

The Ads Data Science team at Reddit leverages data to maximize advertiser value on Reddit through robust data foundations, metrics, and strategic insights generated through experimentation and cutting-edge DS methods. 

We work on a wide range of challenging problems in the areas of ads and platform measurement, campaign and creative management, advertiser growth and retention, monetization, and the intersection of brand and community engagement. We are a highly collaborative team of passionate data scientists and engineers who are constantly pushing the boundaries of what's possible with machine learning and statistical modeling.

About the Role:

We are looking for a highly motivated and experienced Senior Data Scientist to join our growing Ads Data Science team. As a Senior Data Scientist, you will play a key role in developing as well as applying cutting-edge DS models/methods to improve the adoption and performance of our advertising platform through data-driven insights. You will work closely with product managers, engineers, and other data scientists to identify opportunities, define metrics, and build solutions that drive significant impact for Reddit.

Responsibilities:

• Design, develop, and apply DS solutions to inform improvements in advertiser experience and Reddit's ad platform
• Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform
• Collaborate with product managers and engineers to define product requirements and translate them into data science solutions
• Develop ML models & DS methods to improve anomaly detection, prediction, & pattern recognition 
• Communicate findings and recommendations to stakeholders across the organization
• Stay up-to-date on the latest advancements in machine learning and data science
• Mentor and guide junior data scientists on the team

Qualifications:

• Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research
• For M.S. holders: 5+ years of industry experience in applied science or data science roles
• For Ph.D. holders: 4+ years of industry experience in applied science or data science roles
• Platform experience and a deep understanding of the ads ecosystem
• Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design
• Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus
• Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch
• Experience with SQL and relational databases
• Excellent communication and presentation skills
• Passion for Reddit and the online advertising industry

Bonus Points:

• Experience with online advertising and ad tech
• Experience with causal inference and A/B testing
• Contributions to open-source projects or publications in relevant conferences or journals

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Comprehensive Medical Benefits & Health Care Spending Account
• Registered Retirement Savings Plan with matching contributions
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

#LI-Remote

We are a Canadian leader in digital automotive solutions. Our flagship brands — AutoTrader.ca, AutoSync, Dealertrack Canada and CMS — help Canadians buy, sell, and finance vehicles with confidence. 

AutoTrader.ca is Canada’s largest automotive marketplace, with over 25 million monthly visits. 

As part of AutoScout24 group, Europe’s largest online car marketplace, we’re shaping the future of automotive retail in Canada and beyond.

What's in it for you:

We understand that there is life at work and life outside of work. Here are a few benefits we all benefit from that support us to be our creative best. 

• Gym discounts 

• Employee and Family Assistance program 

• Virtual wellness events 

• Conferences & training budget 

• Regular internal training programs 

• Financial planning with 3% matching Pension  

• Competitive salary 
• Annual bonus structure 

For a career where you can drive our business and shape your future, apply now. 

Who you are:

We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a Data Scientist Intern within the Insights & Integrations Consultancy team that will support ongoing projects by assisting in data analysis, visualization, and the development of solutions that help optimize fleet operations and enhance customer decision-making. If you love technology and are keen to join an industry leader — we would love to hear from you!

What you'll do:

As a Data Scientist Intern within the Insights & Integrations Consultancy team, you will gain hands-on experience in leveraging data to drive strategic insights for customers and partners. In this role, you will have the opportunity to work with Geotab’s open data platform, learning how to explore and interpret telematics data to solve real-world business challenges. You will collaborate with cross-functional teams, including Data Scientists, Product Managers, and Integration Engineers, to support key initiatives focused on portfolio expansion, retention, and solution innovation.

Throughout your internship, you will develop a deeper understanding of industry trends, fleet analytics, and Geotab’s ecosystem, while contributing to meaningful projects that have a direct impact on customers and partners.

The opportunity:

• 12 month work-term beginning September 2026.
• Full-time, paid internship: Monday - Friday, 37.5hrs/week.
• Your first week at Geotab begins with 'GEO Launch' - a one-week Employee Orientation. Click here to learn more!
• Learn more about the Geotab Campus Program here.
• This posting is for an existing vacancy.

How you'll make an impact:

• Develop foundational knowledge of the Geotab ecosystem and telematics data, assisting in data exploration, visualization, and analysis to support customer and partner insights.
• Collaborate with Data Scientists and cross-functional teams to optimize the use of Geotab’s technology, providing analytical support for ongoing projects.
• Assist in building and refining data-driven dashboards for internal and external stakeholders, facilitating informed decision-making.
• Support continuous improvement initiatives by gathering feedback, identifying trends, and contributing to data-driven recommendations.
• Work closely with mentors to apply statistical and machine learning techniques in fleet analytics, helping develop predictive models and insights under guidance.
• Contribute to proof-of-concept development, assisting in the creation of small-scale models or prototypes that demonstrate the feasibility and impact of analytics-driven solutions.
• Stay informed on industry trends, emerging data science techniques, and best practices to enhance analytical capabilities.
• Maintain and update documentation on data analysis processes, methodologies, and findings for internal knowledge-sharing.
• Assist in maintaining data pipelines using SQL and Python-based workflows to support ongoing analytics initiatives.

What you'll bring to the role:

Who you are:

We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a Data Scientist who will assist in the design and construction of Geotab's data mining and model building processes from vast amounts of raw data. This position collaborates with internal technical teams in analyzing trends to make improved decision-making in producing effective customer-facing products. If you love technology, and are keen to join an industry leader — we would love to hear from you! 

What you'll do:

As a Data Scientist, your key area of responsibility will be designing and constructing Geotab’s data modeling processes, creating algorithms and predictive models to solve complex business problems, and generating queries from Big Data Infrastructure. You will also be responsible for developing Machine Learning models, testing product performance, and maintaining reports and dashboards to drive key business decisions. You will need to work closely with Geotab’s internal technical teams to gather requirements and ensure data-driven products meet expectations.

To be successful in this role you will be a strong team-player with an entrepreneurial mindset who is comfortable in a flat organization and able to engage with all levels of the organization. In addition, the successful candidate will have strong analytical and problem-solving skills with high accuracy and meticulous attention to detail. The successful candidate will also be highly organized and able to manage multiple tasks and projects simultaneously to ensure timely results.

How you'll make an impact:

• Design and construct Geotab’s data modeling processes. 

• Create algorithms and predictive models to extract information required to solve complex business problems. 

• Generate queries from Geotab’s Big Data Infrastructure from Data Warehousing database (i.e. Google BigQuery). 

• Use Machine Learning (ML) packages (e.g. Scikit-learn and Tensorflow) to develop ML models and features. 

• Test the performance of data-driven products and make recommendations for improving Geotab’s product suite. 

• Collaborate with internal technical teams to gather requirements. 

• Responsible for the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards to drive key business decisions. 

• Support a platform providing ad-hoc and automated access to large dataset.

What you'll bring to the role:

Why job seekers choose Geotab:

Note: This isn’t an active role right now, but we’re building a community of great talent for future opportunities at Caylent. If your background aligns with what we’re looking for, our team may reach out to learn more about you and explore potential future fits.

The Mission

We are seeking an exceptional Machine Learning Architect to join our growing Cloud Native Applications team. The right candidate is someone who has deep expertise in ML system design and is passionate about working with our customers, partners, and colleagues to drive innovation forward. Your mission will be to work alongside Caylent’s Engineers, Engineering Managers, and Project Managers to deliver AWS solutions across our diverse and forward-thinking customer base. You’ll work with the latest technologies and support customers looking to bring cutting-edge ideas to market.

Your Assignment

• You will be a mission control specialist, guiding Cayliens and Customers alike through Agile ceremonies like stand-ups, retrospectives, and more.
• You will translate customer requirements and into a workable backlog of tickets for engineers.
• Delegate tickets to a team of engineers in order to complete customer projects.
• Lead requirements gathering, backlog grooming, and architecture discussions.
• Apply your understanding of DevOps pipelines, including build automation, branching strategies, CI/CD, Infrastructure as Code, security, monitoring, logging, and alerting.
• Troubleshoot and resolve issues in customer dev, test, and production environments.
• Automate software testing at multiple levels (component, configuration item, subsystem, system) and monitor results.
• Write production quality code, including unit and integration tests. 
• Work with a team to deliver top-quality cloud native applications on AWS for customers.
• Clearly communicate and document your designs, processes, and procedures.
• You will demonstrate a passion to Stay Curious as you mentor peers, tackle new technologies, and learn from our word-class team of engineers.
• Coach and mentor less experienced teammates, coach and be mentored by world class engineers.

Your Qualifications

Core Consulting Qualifications 

Customer Experience 

• Confidently addresses client questions and identifies gaps in requirements with  proposed next steps. 
• Focus on delivering clear, actionable solutions that meet client goals

Collaboration 

• Facilitates alignment across stakeholders or technical priorities and deliverables.
• Navigates differing perspectives with professionalism to ensure shared understanding  and progress. 

Communication 

• Breaks down complex technical concepts for non-technical audiences in a clear, concise  manner. 
• Adapts communication style based on audience and context to improve clarity and decision-making. 

Technical Qualifications

• Experience with IaC tools (CloudFormation, CDK, Terraform).
• Expert level experience in Amazon SageMaker and ML libraries (TensorFlow, MXNet, PyTorch, Scikit-learn).
• Strong understanding of ML concepts: feature engineering, hyperparameter tuning, optimization strategies.
• Familiarity with MLOps tools (MLflow, Neptune, Comet).

Benefits 

• 100% remote work
• Medical Insurance for you and eligible dependents
• Generous holidays and flexible PTO
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

Base Salary Range: The expected base salary range for this position is CAD $152,000 - $171,000 per year, commensurate with experience and qualifications.

Additional Compensation Components: In addition to the base salary, the compensation package may include bonuses, commissions, equity, and other incentives. The specific components will vary depending on the role and individual and/or company performance.

This role may require up to 25% travel, depending on business needs. 

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.

• Strong extended health benefits: Health & Prescription coverage, vision and dental care, virtual health care, out-of-country medical coverage
• Insurance coverage (life, short-term disability, long-term disability, and more)
• Health Care Spending Account
• Two different Employee Assistance Programs
• 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. 

Note: Benefits may vary by location and are subject to local regulations.

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_Vancouver_Canada

1-2 years, full-time (fixed term opportunity)

Location - Remote (Canada)

Though we have an office based in Calgary, this is a remote position that is open to candidates across Canada. PurposeMed's office is located on the unceded and traditional territories of the peoples of the Treaty 7 region which includes the nations of the Siksika, the Piikani, the Kainai, the Îethka Stoney Nakoda which consists of the Chiniki, Bearspaw, and Good Stoney Bands, and people of the Tsuut’ina First Nation. The city of Calgary is also homeland to the historic Northwest Métis and to the Otipemisiwak Métis government, Métis Nation Battle River Territory, Nose Hill Métis District 5 and Elbow Métis District 6.

Who we hope to find:

This 1–2 year fellowship is designed explicitly as a transition-to-industry opportunity. It bridges the gap between academic training and real-world application in digital health, clinical operations, and business analytics, while maintaining the academic rigour and publication support expected of a postdoctoral position.

The Fellow will be formally affiliated with the University of Calgary and embedded within PurposeMed’s Data and Analytics team. This dual structure offers a rare professional advantage: a named academic home combined with direct industry immersion. Most uniquely, you will be supported by a three-person supervisory triad—academic, clinical, and technical—ensuring you grow in all three dimensions simultaneously through the mentorship of Dr. Ranjani Somayaji (U of C), Dr. Caley Shukalek (CMO), and our Head of Data.

In this role, you’ll make an impact by:

Elevating Patient Care & Health Equity

• Applying biostatistical methods including regression analyses, ROC curves, risk scoring, and survival analysis to real-world healthcare datasets
• Partnering with clinical, operations, and product teams to design and conduct analyses that drive decision-making and improve patient outcomes
• Participating in patient communication initiatives that make analytical findings accessible to the communities PurposeMed serves

Bridging Data Rigor and Business Strategy

• Translating complex statistical insights into actionable recommendations for clinical, ops, and leadership teams
• Collaborating in the development, validation, and communication of metrics and KPIs for clinical, operational, and business performance
• Collaborating with PurposeMed's data engineering team to extract, manage, and transform data from cloud-based healthcare data warehouses

Advancing Academic Research & Partnerships

• Contributing to peer-reviewed publications, white papers, and conference presentations in collaboration with Dr. Somayaji
• Collaborating with government agencies, non-profits, and community partners, including HIV CTN-affiliated organizations, on joint research initiatives
• Receiving structured mentorship in business analytics, product decision-making, and applied healthcare data science

We need someone who has:

• A PhD in Biostatistics, Statistics, Epidemiology, or a closely related quantitative field (conferred or imminent, PhD candidates with a confirmed defense timeline are welcome to apply)
• Demonstrated experience with advanced statistical analyses: regression modeling (linear, logistic, mixed-effects), ROC curves, risk score development and validation, survival analysis (Kaplan-Meier, Cox proportional hazards)
• Proficiency in R, including tidyverse and statistical modeling packages
• Working knowledge of SQL for querying healthcare data warehouses
• Excellent written and verbal communication skills and is able to present findings to clinical, operational, and non-technical audiences
• A proven ability to work independently and collaboratively in multidisciplinary teams

It would also be great if you have:

• Experience using Python (pandas, scikit-learn, statsmodels)
• Familiarity with cloud data warehouses (Snowflake, BigQuery, or Redshift) and version control (Git)
• Prior experience with real-world healthcare or clinical research datasets, particularly those with missingness, selection bias, or irregular observation times
• Exposure to BI tools (Looker, Tableau, Power BI) or R dashboard frameworks (Shiny, flexdashboard)
• Awareness of dbt or similar data transformation frameworks
• An interest in or prior experience with LGBTQ+ health, HIV/sexual health, gender-affirming care, or chronic disease management
• A prior connection to the HIV CTN, Alberta Innovates, University of Calgary research networks, or Dr. Somayaji's research group

Compensation:

• $80,000 CAD a year
• Please note that this role will be hired through the University of Calgary which provides a benefits package. This role will be contracted to PurposeMed and will not be eligible for the benefits listed below.

Do you think you could be our next Postdoctoral Fellow? We look forward to seeing your application!

#LI-Remote

About Life360

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app,Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 95.8 million monthly active users (MAU), as of December 31, 2025, across more than 180 countries.

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basically family).

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com.

Life360 is a Remote First company, which means a remote work environment will be the primary experience for all employees. All positions, unless otherwise specified, can be performed remotely (within the US and Canada) regardless of any specified location above. 

We are AI Native

We are building an AI native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance. Your Recruiter will provide clear guidance as you move through the interview process.

Undisclosed use of AI not previously discussed with or approved by your Recruiter may impact your candidacy.

About The Team

Data Science at Life360 is a lean, high-impact, matrixed team with individuals embedded in business units and working cross-functionally with Product, Analytics, Engineering, and business stakeholders. We are dedicated to enhancing and optimizing the user experience, accelerating growth, and generating revenue through subscriptions, partnerships, and ads. We leverage a variety of technical skills and tools including experimentation, machine learning, advanced analytics, and agentic AI (AI-Native development) to deliver exceptional customer value.

About the Job

We are seeking a Senior Data Scientist to scale Life360’s growth and user retention efforts. This role will be a core contributor to the Growth team’s strategy and operations. You will work closely with business leads, lifecycle marketing, and user acquisition teams to optimize the entire funnel—from registrations to subscriptions and churn reduction. Your expertise will help Life360 make smarter, faster, and more scalable decisions, directly impacting millions of users.

This role requires strong technical expertise and practice experience in machine learning, statistical modeling, experimentation, and causal inference, as well as the ability to collaborate with cross-functional teams to drive measurable business outcomes.

For candidates based in the US, the salary range for this position is $129,000 to $190,000 USD. For candidates based out of Canada, the salary range for this position is $149,500 to $176,000 CAD. We take into consideration an individual's background and experience in determining final salary - therefore, base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity.

What You’ll Do

• Experimentation & Causal Inference: Design and implement scalable A/B tests to train causal inference models and multi-armed bandits to optimize registration, trial conversion, and retention strategies.
• Automation & Personalization: Build and scale automation tools for experimentation and segmentation, ensuring teams can efficiently test and iterate on hypotheses.
• Growth Optimization: Develop predictive models and statistical frameworks to improve trial-to-subscription conversion, reduce churn, and enhance overall customer LTV.
• User Segmentation & Personalization: Leverage clustering, behavioral analysis, and geospatial data to identify key user segments and tailor experiences accordingly.
• Product Development: Collaborate with product managers and engineers to conceive and develop innovative product ideas, driving feature iteration and development to make impact.
• Cross-functional Collaboration: Partner closely with growth, lifecycle marketing, and user acquisition teams to translate insights into scalable marketing and product strategies.
• Data Infrastructure & Tooling: Work with data engineering to improve the data ecosystem, ensuring robust, scalable pipelines for experimentation and analysis.
• Thought Leadership: Act as a mentor and thought leader within the data science team, helping to define best practices in experimentation, machine learning, data-driven decision-making, and agentic AI (AI-Native) best practices.

What We’re Looking For

Desired Experience & Qualifications

• 4+ years of experience in data science, machine learning, or analytics, with a focus on growth, experimentation, and user retention.
• Strong experience in causal inference, A/B testing methodologies, and statistical modeling.
• Deep understanding of machine learning models for user segmentation, personalization, and predictive analytics.
• Proficiency in Python (Pandas, Scikit-learn, PyTorch, or TensorFlow) and SQL for data manipulation and analysis.
• Experience with big data technologies such as Spark, AWS, Databricks.
• Experience with product development or customer life cycle management.
• Strong business acumen with the ability to translate data insights into actionable business strategies.
• Excellent communication and leadership skills, with the ability to influence cross-functional teams.
• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field

Preferred Qualifications

• Experience in subscription-based products, lifecycle marketing, or user acquisition.
• Experience with geospatial data and mobile location-based services.
• Familiarity with Bayesian modeling, uplift modeling, and reinforcement learning for experimentation.
• Experience with workflow automation tools for experimentation and data science operations.
• Experience in the consumer technology sector, particularly within a fast-paced and sometimes ambitious development setting.

Core Expectations

• Problem-solving mindset - You structure ambiguous problems precisely before reaching for a tool, AI or otherwise
• Collaborative approach - You can explain technical tradeoffs and articulate ideas effectively, work well across teams, and value diverse perspectives
• Ownership mentality - You take responsibility for your work from design through production and beyond
• AI-native working style - You use AI tooling (Claude Code or equivalent) as a genuine development partner: delegating discrete tasks, reviewing outputs critically, and running parallel workstreams rather than hand-holding one agent at a time

We believe culture fit and problem-solving ability matter more than checking every technical box. We're happy to help you grow into areas where you have less experience.

Our Benefits

• Competitive pay and benefits.
• Medical, dental, vision, life and disability insurance plans (100% paid for US employees). We offer supplemental plans for medical and dental for Canadian employees.
• 401(k) plan with company matching program in the US and RRSP with DPSP plan for Canadian employees.
• Employee Assistance Program (EAP) for mental wellness.
• Flexible PTO and 12 company wide days off throughout the year.
• Learning & Development programs.
• Equipment, tools, and reimbursement support for a productive remote environment.
• Free Life360 Platinum Membership for your preferred circle.

Life360 Values

Our company’s mission driven culture is guided by our shared values to create a trusted work environment where you can bring your authentic self to work and make a positive difference 

• Be a Good Person - We have a team of high integrity people you can trust. 
• Be Direct With Respect - We communicate directly, even when it’s hard.
• Members Before Metrics - We focus on building an exceptional experience for families. 
• High Intensity High Impact - We do whatever it takes to get the job done. 

Our Commitment to Diversity

We believe that different ideas, perspectives and backgrounds create a stronger and more creative work environment that delivers better results. Together, we continue to build an inclusive culture that encourages, supports, and celebrates the diverse voices of our employees. It fuels our innovation and connects us closer to our customers and the communities we serve. We strive to create a workplace that reflects the communities we serve and where everyone feels empowered to bring their authentic best selves to work.

We are an equal opportunity employer and value diversity at Life360. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or any legally protected status.  

We encourage people of all backgrounds to apply. We believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful. Even if you don’t meet 100% of the below qualifications, you should still seriously consider applying!

#LI-Remote

____________________________________________________________________________

While candidates in the listed locations are encouraged for this role, we are open to remote candidates in other locations in Canada

Location: Montreal, QC

As a Solutions Architect at Databricks within the Field Engineering org you will partner with our customers to design scalable data architectures using Databricks technology and services. You have technical depth and business knowledge and can drive complex technology discussions which express the value of the Databricks platform throughout the sales lifecycle. In partnership with our Account Executives, you will engage with our customers' technical leads, including architects, engineers, and operations teams with the goal of establishing yourself as a trusted advisor to achieve tangible outcomes. You will work with teams across Databricks and our executive leadership to represent your customer's needs and build valuable customer engagements and report to the Field Engineering Manager.

The impact you will have:

• You will work with Sales and other essential partners to develop account strategies for your assigned accounts to grow their usage of the platform.
• Establish the Databricks Lakehouse architecture as the standard data architecture for customers through excellent technical account planning.
• You will build and present reference architectures and demo applications for prospects to help them understand how Databricks can be used to achieve their goals to land new users and use cases.
• Capture the technical win by consulting on big data architectures, data engineering pipelines, and data science/machine learning projects; prove out the Databricks technology for strategic customer projects; and validate integrations with cloud services and other 3rd party applications.
• Become an expert in, and promote Databricks inspired open-source projects (Spark, Delta Lake, MLflow, and Koalas) across developer communities through meetups, conferences, and webinars.

What we look for:

• 5+ years in a customer-facing pre-sales, technical architecture, or consulting role with expertise in at least one of the following technologies:
• Big data engineering (Ex: Spark, Hadoop, Kafka)
• Data Warehousing & ETL (Ex: SQL, OLTP/OLAP/DSS)
• Data Science and Machine Learning (Ex: pandas, scikit-learn, HPO)
• Data Applications (Ex: Logs Analysis, Threat Detection, Real-time Systems Monitoring, Risk Analysis and more)
• Experience translating a customer's business needs to technology solutions, including establishing buy-in with essential customer stakeholders at all levels of the business.
• Experienced at designing, architecting, and presenting data systems for customers and managing the delivery of production solutions of those data architectures.
• Fluent in SQL and database technology.
• Debug and development experience in at least one of the following languages: Python, Scala, Java, or R.
• [Desired] Built solutions with public cloud providers such as AWS, Azure, or GCP
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Travel to customers in your region up to 30% of the time.

Location: Ottawa, ON

As a Solutions Architect on the Canada team focused on Digital Native customers, you will shape the future of the big data landscape by working with the most sophisticated data engineering and data science teams in the world.

Reporting to the Field Engineering Manager, you will collaborate with customers, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide customers through the competitive landscape, best practices, and implementation; and develop technical champions along the way.

The impact you will have:

• You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
• Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
• Collaborate with your fellow Solutions Architects, using your skills to support each other and our users
• Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.

What we look for:

• 5+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
• Experience building distributed data systems
• Comfortable programming in, and debugging, Python and SQL
• Have built solutions with public cloud providers such as AWS, Azure, or GCP
• Expertise in one of the following:
  • Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
  • Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
• Available to travel to customers in your region
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Nice to have: Databricks Certification

NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply.  We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.

The Opportunity

This is a senior technical client leadership role that blends deep hands-on ML expertise with strategic advisory and consulting skills. You will be the most experienced ML voice across a diverse and expanding book of customer engagements — from early-stage companies bringing ambitious ML ideas to market, to established enterprises modernizing how they build and operate AI systems on AWS.

You will shape strategy, influence architecture, and leave every team you touch better than you found it. You bring the scientific depth to design and evaluate models rigorously, the engineering depth to architect production ML systems at scale, and the consulting instincts to translate both into business value for customers.

If you have led the hard conversations, shaped the architecture decisions that mattered, and built the things others benchmark against — and you are looking to do that across a growing portfolio of varied and interesting customers — this is the role for you.

What You'll Do

• Lead end-to-end ML assessments across infrastructure, data pipelines, model lifecycle, and organizational readiness — producing recommendations that drive executive decision-making and earn Caylent the next engagement.
• Partner with sales and solutions teams through the proposal and scoping phase, contributing the technical depth needed to shape well-grounded statements of work.
• Serve as the senior technical authority on client engagements — possibly across multiple projects simultaneously — providing architectural guidance, ensuring technical quality from your project team members, and getting hands-on when the engagement demands it, without owning day-to-day implementation responsibilities.
• Own or orchestrate high-quality POCs that give customers confidence before committing to a larger initiative.
• Advise customers on ML operations standards and architecture — covering MLOps pipeline design, model lifecycle management, LLMOps patterns, and production monitoring frameworks — translating operational complexity into decisions and guardrails their teams can own and sustain.
• Shape how Caylent wins its most technically complex opportunities — contributing the architectural thinking and credibility that turns prospects into customers.
• Strengthen the ML practice from the inside — through peer guidance, technical interviews, and contributions to accelerators, reference architectures, and thought leadership content.

What You Bring

The non-negotiables

• 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
• Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring.
• Deep expertise in at least two or three ML domains — whether traditional ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape.
• Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards and decisions that engineering teams can execute and executives can act on.
• Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization (quantization, vLLM), and distributed training (DeepSpeed, FSDP) — combined with RAG and agentic system design, including multi-agent architectures, event-driven workflows, MCP integration, and human-in-the-loop patterns on AWS. Technical authority to prescribe the right approach and set architectural standards that teams can execute against.
• Proven ability to operate independently in complex customer environments — navigating ambiguity, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.

Strong differentiators

• AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional.
• Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
• Exposure to varied industries and problem types in a consulting or client-facing context.
• Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments.
• Hands-on experience designing and deploying SRE agents and AI-driven operations workflows in production — spanning automated incident detection, triage, and remediation — with the ability to integrate across observability platforms and translate AI operations outcomes into measurable business value.

Technical Stack

Our practice spans a broad range of ML domains. Candidates are expected to prescribe — not just recognize — with the judgment to maximize what AWS makes possible and the experience to know how open-source tooling strengthens it.

ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI

AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation)

Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts

AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly

Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue

Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate

Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama

MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness

LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation

Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo

The role

Are you passionate about food? Do you consider yourself an expert and enthusiast in developing and deploying large-scale recommender systems? Then this position might be the perfect fit for you!

You will join a high-impact team of data scientists, focusing exclusively on menu personalization. As a Senior Data Scientist, you will play a pivotal role in designing, implementing, and improving our personalized recommendation algorithms for culinary products across HelloFresh’s global markets. In this role, you can expect to have a large and tangible impact on customer experience and business growth by ensuring customers always see the most relevant and appealing menu options. You will work closely with other personalization Data Scientists, as well as Data, Backend, and Machine Learning Engineers, together delivering statistical solutions and ML models to production.

To succeed in this role you should be curious, able to deal with uncertainty, a fast learner, and someone who relentlessly prioritizes problem statements and tailors the solution accordingly. You should also be comfortable suggesting multiple solutions based on your experience, and prioritizing tasks based on effort and potential impact. As a senior member of the team, you will be expected to work independently when necessary, as well as support other team members occasionally. 

What you’ll do

• Join and become an integral part of a high-impact team covering menu personalization.
• Contribute to designing and implementing the next generation of our recommender models, from experimentation and data collection to operationalization and closely aligning them with non-technical stakeholders.
• Shape and transform the future of customer experience at HelloFresh by proposing new personalization approaches and evolving the statistical and ML models in pace with future company strategies.
• Design and execute A/B tests to measure the impact of new recommendation strategies on key metrics.
• Work with state-of-the-art technologies like AWS (EMR, Glue, S3, etc), Kafka, PySpark, Airflow, Databricks, MLFlow, Claude (AI), as well as our in-house MLOPs tools.
• Collaborate closely within other functions (data engineering, backend engineering, ML engineering, product) to translate business objectives into concrete, data-driven personalization strategies.
• Analyze the performance of our models to understand customer behavior, and come up with improvements.
• Contribute to a collaborative and knowledge-sharing environment.

What you’ll bring

• Proven track record of scientific work in your Bachelor's, Master's, or PhD in computer science, statistics, physics, economics, mathematics, data science or similar.
• Long-term (3+ yrs) work experience as a (senior) data scientist focused on developing and deploying recommender systems or personalized ML models in a production environment.
• In-depth knowledge of various recommendation algorithms, evaluation metrics, and the practical challenges of building and maintaining a modern recommender system.
• Fluency and long-term experience in Python, including high familiarity with its scientific stack such as Numpy, Pandas, Scikit-learn, Matplotlib. (Experience in Databricks and PySpark is a plus).
• Ability to retrieve (using SQL), manipulate, and analyse large, heterogeneous datasets.
• Experience with software development practices and tools (Git, CI/CD, Docker, AWS).
• You love to learn, are a critical thinker and creative problem solver who strives for excellence.
• You are a team player who loves to collaborate and work with stakeholders from different backgrounds in an international environment.
• Exceptional communication and collaboration capabilities.
• Experience working with and leveraging Generative AI tools (e.g., Claude, Copilot) to support model development, experimentation, code generation, and documentation.

Interacting with product managers, UX designers, data and backend engineers, as well as business stakeholders, is very much part of our day-to-day, so communication skills are vital. We are looking for strong problem-solvers who can apply their statistical modeling and machine learning skills to a wide range of business problems in the personalization space while also acting as an ambassador to coach team members and stakeholders.

Above all, we are looking for people who will make HelloFresh better. We believe there are many different ways of developing skills, and we love diverse experiences! So even if you don’t “tick all the boxes” but think you’d thrive in this role, we would really like to learn more about you.

Let’s cut to the cheese, this is why you'll love it here

• Box Discount - Amazing discounts on 1 box per week! 75% discount on weekly HelloFresh and Chefs Plate meal kits AND 50% off weekly Factor meal box.
• Health & Wellness - Health & Dental benefits from day 1, a Health Spending Account, unlimited access to the Headspace app to meet your self-care needs, and 25% discount on GoodLife fitness memberships!
• Vacation & PTO - Time off is also an important part of self-care! We offer generous vacation and PTO to help you create a good work-life balance. 
• Family Benefits - A parental leave top-up program for expectant parents.
• Growth & Development - We support your career progression and invest in your continued learning through experiences and initiatives owned by our dedicated L&D team
• Work Hard & Have Fun - From team socials to engaging company days, you’ll have plenty of opportunity to experience the fun!
• Diversity & Inclusion Initiatives - With impactful ERG’s like FreshPride, Women Empowered and LIMES, we are committed to our diversity, equity & inclusion efforts.
• Food Puns - this one is kind of a big dill if you haven’t already noticed. We even have some punny meeting room names!

Flexible Hybrid Approach

At HelloFresh, we know that flexible work arrangements are essential in enabling you to do your best work, while balancing your personal and life needs. Offering remote work flexibility, along with the opportunity to interact and collaborate in the office are all a part of creating a great employee experience. 

To meet these needs, we are pleased to provide Flexible Hybrid work. Flexible Hybrid is a people-first approach that is based on choice, trust, personalization, and empowers teams to choose when and how often they work from the office and work from home, in addition to team days and company days. This means a minimum of 2 days in office per week, with most teams in office between 2-3 days a week.

#LI-HYBRID

HelloFresh Canada uses AI-integrated technology to help us process and evaluate applications more efficiently. This includes tools that screen and assess candidate qualifications based on the requirements for this role. While these tools assist our workflow, all final selection decisions are made by our hiring team.

This is a posting for an existing vacancy. We are actively seeking to fill this position.

#DATA

P-225

While candidates in the listed locations are encouraged for this role, we are open to remote candidates in other locations in eastern Canada

As a Solutions Architect at Databricks within the Field Engineering org you will partner with our customers to design scalable data architectures using Databricks technology and services. You have technical depth and business knowledge and can drive complex technology discussions which express the value of the Databricks platform throughout the sales lifecycle. In partnership with our Account Executives, you will engage with our customers' technical leads, including architects, engineers, and operations teams with the goal of establishing yourself as a trusted advisor to achieve tangible outcomes. You will work with teams across Databricks and our executive leadership to represent your customer's needs and build valuable customer engagements and report to the Field Engineering Manager.

The impact you will have:

• You will work with Sales and other essential partners to develop account strategies for your assigned accounts to grow their usage of the platform.
• Establish the Databricks Lakehouse architecture as the standard data architecture for customers through excellent technical account planning.
• You will build and present reference architectures and demo applications for prospects to help them understand how Databricks can be used to achieve their goals to land new users and use cases.
• Capture the technical win by consulting on big data architectures, data engineering pipelines, and data science/machine learning projects; prove out the Databricks technology for strategic customer projects; and validate integrations with cloud services and other 3rd party applications.
• Become an expert in, and promote Databricks inspired open-source projects (Spark, Delta Lake, MLflow, and Koalas) across developer communities through meetups, conferences, and webinars.

What we look for:

• 5+ years in a customer-facing pre-sales, technical architecture, or consulting role with expertise in at least one of the following technologies:
• Big data engineering (Ex: Spark, Hadoop, Kafka)
• Data Warehousing & ETL (Ex: SQL, OLTP/OLAP/DSS)
• Data Science and Machine Learning (Ex: pandas, scikit-learn, HPO)
• Data Applications (Ex: Logs Analysis, Threat Detection, Real-time Systems Monitoring, Risk Analysis and more)
• Experience translating a customer's business needs to technology solutions, including establishing buy-in with essential customer stakeholders at all levels of the business.
• Experienced at designing, architecting, and presenting data systems for customers and managing the delivery of production solutions of those data architectures.
• Fluent in SQL and database technology.
• Debug and development experience in at least one of the following languages: Python, Scala, Java, or R.
• [Desired] Built solutions with public cloud providers such as AWS, Azure, or GCP
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Travel to customers in your region up to 30% of the time.

OVERVIEW

Instacart will increase the number of features launched by 2x by the end of 2026 by effectively using AI. A primary driver of knowing which features to launch includes analytical work traditionally performed by data scientists in collaboration with a product team. We are developing systems to accelerate analyses completed by data experts and enable anyone to self-serve most analytics questions.

ABOUT THE JOB

You would be responsible for driving AI for Analytics data science work which includes:

1. Product strategy: Using data to identify the most promising opportunity areas along with contributing data expertise to refine and develop new product ideas.
2. AI expertise: You will be using and deploying a variety of AI systems in production settings.
3. Intelligent systems: Improving a product feature by adding intelligent functionality to the product that relies on data science methods including statistical and machine learning methods

The role requires strong XFN collaboration where an ideal candidate can:

1. Drive critical efforts to completion with little oversight, while occasionally jumping into roles adjacent to data science (i.e. data engineering, machine learning engineer, etc).
2. Create the product roadmap
3. Ruthlessly prioritize among requests from multiple competing stakeholders
4. Act as a senior cross-functional leader of the team, aligning the team on principles, processes and goals

ABOUT YOU

Minimum Qualifications

• 6+ years of work experience in a data science or related field
• Have deeply engaged with AI workflows
• Expert in Python (pandas), SQL, git, and Jupyter notebooks
• Strong statistical and analytical skills
• Built a machine learning, statistical, or AI model that made its way into production even if the code you wrote was completely rewritten before being deployed

Preferred Qualifications

• 8+ years of work experience in a data science or related field
• Led a cross-functional team to design a system of metrics that connected the business strategy to a set of core metrics and assigned ownership of those metrics to the team
• Significant causal experimentation experience 
• Built a complex machine learning or statistical model and deployed/supported it in production
• Experience with Python (pandas; scikit-learn, statsmodels, or PyTorch; a visualization library that is not matplotlib; a web framework such as flask), SQL (Snowflake), git, Jupyter notebooks

#LI-remote

The Role

We are seeking a Senior AI Engineer to join our AI & Data Group on the AI Orchestration team. This role is responsible for designing, building, operating and scaling the orchestration layer that powers League’s AI-driven healthcare experiences. 

Sitting at the intersection of AI systems design, ML productionization, and distributed software engineering, you will take the probabilistic capabilities of large language models and engineer them into reliable, measurable, and scalable product experiences.

This is a hands-on senior engineering role for a practitioner who moves beyond prototypes to own the hard problem of making AI work at scale. You will translate leading edge AI innovation into reliable, secure, and compliant systems. Your work will directly impact how AI is operationalized across League’s digital health ecosystem—ensuring systems are scalable, observable, performant, and safe in a regulated environment.

• Brief Summary of Work:
  • At Robots & Pencils, we harness the power of data to help our clients achieve meaningful digital transformation. We’re seeking a Senior Data Architect/Strategist who blends advanced data knowledge with real-world problem solving, someone who can move seamlessly between developing predictive models and translating complex data into clear, actionable insights. 
    
    This is a high-impact role ideal for a candidate who thrives at the intersection of innovation and execution. You’ll be an integral part of a multidisciplinary team using data science and machine learning to drive intelligent products, automation, and smarter business decisions. 
    
    
    
    
    
    About You 
    
    You’re passionate about discovering what data can reveal. You approach problems with curiosity and scientific rigor, always looking to test, learn, and improve. You have deep experience with statistical modeling, machine learning, and experimentation. You're collaborative, business-savvy, and known for delivering results that move the needle. 
    
    Experience is required in technical leadership, mentoring of staff and recommend training of staff. Also experience in working closely with complex customers. 
    
    
    
    
    
    
    
    
    
    What You’ll Do 
    
    Modeling & Advanced Analytics 
    
    Design, develop, and deploy predictive and prescriptive models across a variety of domains (e.g., customer behavior, operational efficiency, personalization). 
    Apply machine learning, deep learning, and statistical techniques to solve real-world business challenges. 
    Drive experimentation (A/B testing, multi-variate testing) and causal inference to validate hypotheses and measure impact. 
    
    Data Exploration 
    
    Analyze large, complex datasets to extract key insights and translate them into strategic recommendations. 
    Communicate findings clearly and effectively to both technical and non-technical audiences, using compelling data knowledge and visualization. 
    Collaborate with product managers and business stakeholders to identify opportunities and frame data science solutions. 
    
    Partnership & Enablement 
    
    Work closely with data engineers, analysts, and software developers to build scalable, data-powered applications. 
    Mentor junior data scientists, supporting technical development and scientific rigor. 
    Contribute to the development of reusable assets, tools, and processes to increase team velocity and impact. 
    
    
    
    
    
    Qualifications & Experience 
    
    7+ years of professional experience in data science, statistics, and applied machine learning. 
    Deep proficiency in Python or R, with strong skills in libraries like scikit-learn, TensorFlow/PyTorch, models, algorithms and ontologies. 
    Building and deploying Data Mesh architectures. 
    Strong experience in AWS tools and infrastructure and cloud AI and data tools essential. Experience in working in other cloud environments (GCP, or Azure) would be an advantage. 
    
    Demonstrated success deploying models into production environments using APIs, pipelines, or ML frameworks. 
    Proven track record in statistical modeling, time series forecasting, NLP, or optimization. 
    Experience designing and analyzing controlled experiments (A/B testing, uplift modeling). 
    Background in quantitative disciplines such as Computer Science, Statistics, Mathematics, or Engineering. 
    Experience with tools such as Databricks, SageMaker, and Snowflake is essential. 
    Experience of Palantir would be an advantage. 
    AWS cloud certifications or ML specialization credentials are an advantage. 
    
    Core Competencies 
    
    Scientific Thinking – Brings strong analytical and statistical foundations; frames problems rigorously and tests hypotheses systematically. 
    Practical Execution – Focuses on delivering actionable insights and real-world model impact, not just theoretical accuracy. 
    Business Acumen – Understands the “why” behind the data; aligns analysis with client goals and product strategy. 
    Ownership & Accountability – Delivers high-quality, reliable work end-to-end; takes pride in outcomes. 
    Communication – Explains technical results clearly and persuasively to diverse audiences. 
    Team Collaboration – Works effectively across functions and time zones; contributes to a culture of openness and shared learning. 
    
    
    
    
    
    Why Join Robots & Pencils? 
    
    At R&P, we don’t just build software; we help our clients build the future. We are a diverse, globally distributed team that thrives on solving challenging problems and delivering exceptional results. If you're a data scientist who’s excited about building intelligent systems that deliver measurable impact, we’d love to hear from you.
• Tech stack:
  • Python, TensorFow/PyTorch, Data Mesh, AWS, Databricks, SageMaker, Snowflake

Affinity stitches together billions of data points from massive datasets to create a powerful, accurate representation of the world's professional relationship graph. Based on this data, we offer our users the insights and visibility they need to nurture and tap into the opportunities in their team's network. 

This role is part of the AI Platform team, which owns the AI services that power Affinity's industry-leading relationship intelligence platform. We extract and retrieve information from billions of structured and unstructured data points to deliver actionable insights to customers.

As a Senior Machine Learning Engineer, you will collaborate with data engineers, software engineers, and product managers to shape the future of private capital's leading CRM platform. You will design and build AI systems that efficiently uncover insights from compelling business interaction data – an exciting and unique opportunity within the industry.

This is an applied machine learning position with a strong emphasis on engineering, rather than research. You will play a key role in advancing our ML Engineering capabilities, particularly in information retrieval and eventually recommendation systems. 

What you’ll be doing:

• Own the full ML lifecycle: Take projects from ideation to production, including feature engineering, model selection, deployment, and model observability and evaluation.
• Translate business needs into ML solutions: Gather product requirements and translate them into robust ML system design requirements. 
• Build recommendation and ranking systems: Architect and launch ranking and recommendation infrastructure from scratch, initially via integrated off-the-shelf models, and evolving to targeted and customized solutions in the long term.
• Solve complex problems: Work on a variety of information extraction, information storage and information retrieval problems for both structured and unstructured data. 
• Collaborate cross-functionally: Partner with cross-functional (product, infra, data engineering, and software engineering) teams to build robust, high-scale systems that underlie all of our data processing and ML Operations.

Qualifications

Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every qualification. At Affinity, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role, but your past experience doesn’t perfectly align with the qualifications above, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

Required:

• 5+ years of experience in software engineering and/or Machine Learning experience in applying machine learning in production. 
• Hands-on experience developing ranking or recommendation systems from scratch, deployed at scale using techniques such as learn-to-rank, explainable recommendations    
• Strong understanding of machine learning techniques, including clustering and decision trees
• Experience with serving ML models for streaming and batch inference at scale.
• Experience with vector or graph databases.
• Proficiency in Python and modern ML frameworks (PyTorch, Scikit-learn, or similar).
• Track record of building maintainable, testable, and production-grade codebases.
• Experience with observability tools for online and offline model evaluation, A/B testing, and tracing for AI applications.

Nice to Have:

• Experience with dataset engineering, including data curation, augmentation, and synthesis, to assist ML model improvement. 
• Experience with graph-based recommendation systems, such as graph NN. 
• Experience with packaging, CI/CD and pipeline automation. 

Tech stack: Our ML pipeline manages multiple Python services that support various AI features, including utilizing OCR to extract information from unstructured data, serving embedding models to vectorize chunks, and ranking a list of recommendations based on relevance and user preference.

How we work:

Our culture is a key part of how we operate, as well as our hiring process:

• We iterate quickly. As such, you must be comfortable embracing ambiguity, be able to cut through it, and deliver value to our customers.
• We are candid, transparent, and speak our minds while simultaneously caring personally with each person we interact with. 
• We make data-driven decisions and make the best decision for the moment based on the information available.

If you’d want to learn more about our values click here.

What you'll enjoy at Affinity:

• We live our values: As owners, we take pride in everything we do. We embrace a growth mindset, engage in respectful candor, act as playmakers, and "taste the soup" by diving deep into experiences to create the best outcomes for our colleagues and clients.
• Health Benefits: We cover both you and your dependents' extended health benefit premiums and offer flexible personal & sick days to support your well-being.
• Retirement Planning: We offer an RRSP plan to help you plan for your future.
• Learning & Development: We provide an annual education budget and a comprehensive L&D program.
• Wellness Support: We reimburse monthly for things like home internet, meals, and wellness memberships/equipment to support your overall health and happiness.
• Team Connection: Virtual team-building activities and socials to keep our team connected, because building strong relationships is key to success.

Please note that the role compensation details below reflect the base salary only and do not include any equity or benefits. This represents the salary range that Affinity believes, in good faith, at the time of this posting, that it will pay for the posted job.

A reasonable estimate of the current range is $160,000 to $220,000 CAD. Within the range, individual pay depends on various factors including geographical location and review of experience, knowledge, skills, abilities of the applicant. 

At Affinity, we may use artificial intelligence (AI) tools as part of our recruitment process to help screen and evaluate candidate applications. While AI assists our hiring team in managing applications efficiently, it never replaces decisions made by real people. We are committed to fair and transparent hiring practices, and our AI tools are regularly monitored to ensure they support—not replace—human judgment. 

Additional Information:

• This job posting represents an active vacancy that we are currently seeking to fill.

Affinity stitches together billions of data points from massive datasets to create a powerful, accurate representation of the world's professional relationship graph. Based on this data, we offer our users the insights and visibility they need to nurture and tap into the opportunities in their team's network. 

This role is part of the AI Platform team, which owns the AI services that power Affinity's industry-leading relationship intelligence platform. We extract and retrieve information from billions of structured and unstructured data points to deliver actionable insights to customers.

As a Senior Machine Learning Engineer, you will collaborate with data engineers, software engineers, and product managers to shape the future of private capital's leading CRM platform. You will design and build AI systems that efficiently uncover insights from compelling business interaction data – an exciting and unique opportunity within the industry.

This is an applied machine learning position with a strong emphasis on engineering, rather than research. You will play a key role in advancing our ML Engineering capabilities, particularly in information retrieval and eventually recommendation systems. 

What you’ll be doing:

• Own the full ML lifecycle: Take projects from ideation to production, including feature engineering, model selection, deployment, and model observability and evaluation.
• Translate business needs into ML solutions: Gather product requirements and translate them into robust ML system design requirements. 
• Build recommendation and ranking systems: Architect and launch ranking and recommendation infrastructure from scratch, initially via integrated off-the-shelf models, and evolving to targeted and customized solutions in the long term.
• Solve complex problems: Work on a variety of information extraction, information storage and information retrieval problems for both structured and unstructured data. 
• Collaborate cross-functionally: Partner with cross-functional (product, infra, data engineering, and software engineering) teams to build robust, high-scale systems that underlie all of our data processing and ML Operations.

Qualifications

Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every qualification. At Affinity, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role, but your past experience doesn’t perfectly align with the qualifications above, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

Required:

• 5+ years of experience in software engineering and/or Machine Learning experience in applying machine learning in production. 
• Hands-on experience developing ranking or recommendation systems from scratch, deployed at scale using techniques such as learn-to-rank, explainable recommendations    
• Strong understanding of machine learning techniques, including clustering and decision trees
• Experience with serving ML models for streaming and batch inference at scale.
• Experience with vector or graph databases.
• Proficiency in Python and modern ML frameworks (PyTorch, Scikit-learn, or similar).
• Track record of building maintainable, testable, and production-grade codebases.
• Experience with observability tools for online and offline model evaluation, A/B testing, and tracing for AI applications.

Nice to Have:

• Experience with dataset engineering, including data curation, augmentation, and synthesis, to assist ML model improvement. 
• Experience with graph-based recommendation systems, such as graph NN. 
• Experience with packaging, CI/CD and pipeline automation. 

Tech stack: Our ML pipeline manages multiple Python services that support various AI features, including utilizing OCR to extract information from unstructured data, serving embedding models to vectorize chunks, and ranking a list of recommendations based on relevance and user preference.

How we work:

Our culture is a key part of how we operate, as well as our hiring process:

• We iterate quickly. As such, you must be comfortable embracing ambiguity, be able to cut through it, and deliver value to our customers.
• We are candid, transparent, and speak our minds while simultaneously caring personally with each person we interact with. 
• We make data-driven decisions and make the best decision for the moment based on the information available.

If you’d want to learn more about our values click here.

Work Location: San Francisco, New York, or US Remote (Affinity is registered to employ in certain U.S. states)

For those located in San Francisco or New York, for this role we're embracing a hub-hybrid model, designed to balance flexibility with meaningful in-person collaboration. Team members within commuting distance are expected in-office 2–3 days per week, typically Tuesday through Thursday. We believe great things happen when people come together intentionally to connect, create, and build momentum as a team.

What you'll enjoy at Affinity:

• We live our values: As owners, we take pride in everything we do. We embrace a growth mindset, engage in respectful candor, act as playmakers, and "taste the soup" by diving deep into experiences to create the best outcomes for our colleagues and clients.
• Health Benefits: We cover your medical, dental, and vision insurance premiums with comprehensive PPO, HDHP and HMO options (in CA), and offer flexible personal & sick days to support your well-being.
• Retirement Planning: We offer a 401(k) plan to help you plan for your future.
• Learning & Development: We provide an annual education budget and a comprehensive L&D program.
• Wellness Support: We reimburse monthly for things like home internet, meals, and wellness memberships/equipment to support your overall health and happiness.
• Team Connection: Virtual team-building activities and socials to keep our team connected, because building strong relationships is key to success.

Please note that the role compensation details below reflect the base salary only and do not include any equity or benefits. This represents the salary range that Affinity believes, in good faith, at the time of this posting, that it will pay for the posted job.

A reasonable estimate of the current range is $160,000 to $235,000 USD. Within the range, individual pay depends on various factors including geographical location and review of experience, knowledge, skills, abilities of the applicant. 

About the role

Responsibilities

• Build and advance our most sensitive and business-critical ML and GenAI models that power underwriting decisions and risk selection.
• Drive and execute ML projects end-to-end: problem framing, data exploration, feature engineering, model design, prototyping, offline/online evaluation, deployment, and monitoring.
• Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid and reproducible experimentation.
• Apply state-of-the-art ML and GenAI workflows (e.g., gradient-boosted trees, deep learning, LLMs, prompt engineering, transfer learning) to improve underwriting accuracy, automation, and decision support.
• Own model quality and robustness by defining success metrics, running ablations and diagnostics, and iterating to outperform prior baselines.
• Survey and incorporate recent advances in ML/GenAI research into our core underwriting capabilities, balancing scientific rigor with practical constraints.
• Collaborate closely with underwriters, product, data, and engineering partners to clarify requirements, align on tradeoffs, and ensure models integrate cleanly into production workflows.
• Communicate methods and results clearly through documentation, presentations, and design reviews; share learnings and patterns that level up the broader team.
• Contribute to a culture of scientific and data excellence by bringing mature empathy, best practices, and lightweight processes to experimentation, code review, and model governance.

Skills and Qualifications

• Ph.D. or MS in a quantitative or computational field (e.g., Computer Science, Statistics, Applied Math, Electrical Engineering) or equivalent practical experience.
• 5+ years of full-time experience developing and deploying ML- and data-based solutions in production.
• Practical, hands-on experience with supervised and unsupervised learning methods, including model selection, regularization, and calibration.
• Expertise in statistical analysis methods, especially regression analysis, statistical inference, and forecasting / time-series methods.
• Strong proficiency in Python and core ML libraries (e.g., scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow) and SQL for working with large, messy datasets.
• Experience with experiment design and evaluation (e.g., A/B tests, offline metrics vs. online KPIs, guardrails) and with ensuring reproducible results.
• Comfortable and effective in ambiguous problem spaces; demonstrated ability to own and drive projects with minimal oversight and process.
• Exceptional written and oral communication skills, with the ability to explain complex modeling choices and tradeoffs to technical and non-technical stakeholders.
• Minimum 1+ year of experience in insurance underwriting modeling (pricing, risk scoring, eligibility, or related applications).

Bonus Points

• Experience with modern GenAI techniques relevant to underwriting (e.g., using LLMs for document understanding, unstructured-to-structured extraction, or underwriter copilot workflows).
• Familiarity with model governance in regulated environments, including documentation, validation, monitoring, and change management.
• Experience with ML orchestration and MLOps tools (e.g., Airflow, Prefect, MLflow, SageMaker) for managing training, deployment, and monitoring at scale.
• Exposure to causal inference or uplift modeling for understanding the impact of underwriting or guideline changes.
• Experience working with cyber or P&C insurance data (exposures, limits, deductibles, claims, external risk signals).

Compensation

As a remote-first organization, our compensation reflects the cost of labor across several Canadian geographic markets. 

In Alberta, British Columbia & Ontario the base salary for this position ranges from $115,300.00/year up to $144,100.00/year. 

For all other locations, the base salary for this position ranges from $103,800.00/year up to $129,690.00/year. 

Consistent with applicable laws, an employee's pay within this range is based on a number of factors, which include but are not limited to relevant education, skills, job-related knowledge, qualifications, work experience, credentials, and/or geographic location. Your Recruiter can share more on the specific target salary range for your location during the interview process. Coalition, Inc. reserves the right to modify this range as needed.

Vacancy Status: This posting is for an existing vacancy.

Use of AI: We utilize AI-assisted tools to help us organize and review the high volume of applications we receive. However, our human recruiting team makes all final hiring decisions and interview selections, valuing personal connection over algorithms.

Application Updates: Consistent with our commitment to transparency, if you are interviewed for this role, we will provide a status update on your application within 45 days of your final interview.

Perks

• 100% medical, dental, and vision coverage
• Flexible PTO
• Annual home office stipend and WeWork access
• Mental & physical health wellness programs like Headspace, Lumino, and more!
• Competitive compensation and opportunity for advancement

The Team:

Our Business Applications and Analytics team plays a pivotal role in driving business success through data-driven decision-making and operational excellence. This dynamic team manages enterprise-wide applications, including ERP, CRM, and BI systems. By harnessing the power of data and advanced analytical tools, we provide valuable insights that inform strategic initiatives, optimize processes, and enhance overall performance.  

Position Overview:  

Diligent is ‘All-In’ on AI and is seeking a dynamic and strategic AI leader to join our team as an AI Solutions Architect. As part of the Business Applications and Analytics team, this crucial role is designed to accelerate our Internal AI capabilities across multiple business functions, including Marketing, GTM/Sales, Customer Success & Support, Finance, HR, Legal, and Product and Engineering teams. 

The primary goal of this position is to identify, prototype, deploy, and scale AI technologies and solutions that drive greater operational efficiency and effectiveness. The ideal candidate will be a technically adept individual with business acumen, able to bridge the gap between robust technical implementations and business needs, with the ability to partner with various business leaders to spot and act on opportunities for AI-driven transformation and innovation. 

Key Responsibilities:

• Partner with business leaders to understand challenges, identify AI opportunities, and implement effective AI/GenAI solutions.
• Lead the ideation, prototyping, deployment, and scaling of AI/GenAI technologies and agentic solutions tailored to business needs.
• Develop and communicate strategic frameworks and roadmaps for AI initiatives aligned with business goals.
• Champion best practices in AI/ML and GenAI development—including responsible AI, privacy, security, and ethical guidelines.
• Collaborate with cross-functional teams to integrate AI tools within core business processes, ensuring seamless, high-value adoption.
• Stay abreast of AI best practices, tools, and methodologies and introduce them to the organization.
• Drive AI and Data literacy across the company through training, workshops, and continuous education initiatives.
• Ensure compliance with data protection and privacy regulations in all AI implementations.
• Monitor and evaluate the performance and effectiveness of AI solutions and continually refine them.
• Collaborate with stakeholders to define and achieve key performance indicators (KPIs) and return on investment (ROI) for AI projects.
• Advocate and implement AI ethical guidelines and best practices.

Required Qualifications:

• Solid Foundations: Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related technical field.
• Practical Tinkerer: 5+ years in a technical role (software, data, DevOps, etc.), with hands on experience building end-to-end prototypes using GenAI tools (GPTs, Hugging Face, open-source LLMs, LangChain, Scikit-learn, etc.).
• Curious Explorer: Demonstrates a hands-on approach to learning in prompt-engineering, fine-tuning, RAG pipelines, or API Integrations, with a commitment to rapid failure, iteration, and sharing findings.
• Data Wrangler: Possesses the ability to source, clean, and transform raw data, including spreadsheets, logs, APIs, or CRM exports, into structured inputs for AI experiments and prototypes.
• Code Crafter: Capable of writing scripts or small applications to connect models, cloud services, or data sources. Prior experience with TensorFlow, PyTorch, Azure, AWS, or GCP is beneficial, but willingness to learn is equally important.
• Business Translator: Experienced as a technical sales engineer or solutions consultant, capable of turning AI experiments into compelling stories and metrics that resonate with non-technical stakeholders.
• Responsible AI Mindset: Demonstrated experience in addressing privacy, security, bias, and ethics in professional work, with a strong familiarity with industry-specific data privacy, AI ethics, and compliance requirements.
• Team Player: Essential qualities include clear communication and a growth mindset, as you will collaborate with marketers, sales professionals, engineers and leaders.

Preferred Qualifications:

• Demonstrates strong knowledge of AI use cases, including NLP, computer vision, data analytics, predictive modelling, and translating technical capabilities into business value, along with proficiency in large language models (LLMs), prompt engineering, RAG architectures, MLOps, LLMOps, and GenAI application deployment.
• Advanced programming and data engineering skills, with Python preferred, and experience in cloud-based AI solutions (AWS, GCP, Azure).
• Ability to architect and deploy enterprise-scale AI solutions, collaborating across sales, product, and implementation teams to ensure adoption and measurable ROI.
• Strategic thinker with strong analytical and problem-solving skills, capable of driving measurable business outcomes through AI/GenAI.

Role Summary

As a Senior AI/ML Engineer, you will lead the design and deployment of high-impact AI solutions, expertly bridging traditional predictive modeling with next-generation Agentic AI. You are equally comfortable tuning a Gradient Boosted Tree for structured data as you are architecting a multi-agent system for complex reasoning tasks. You will collaborate directly with clients to understand their needs, translate business challenges into technical solutions, and provide expert guidance on AI/ML best practices. The role requires overseeing dataset quality for model training and leveraging GCP for efficient model deployment and scaling. You will utilize the Google AI ecosystem (Vertex AI, Google ADK) and orchestration frameworks like LangChain/LangGraph to move AI solutions from experimental demos to reliable, production-grade systems grounded in statistical rigor.

Role and Responsibility

• Architect and implement sophisticated multi-agent systems and autonomous workflows leveraging the Google AI SDK, LangGraph, and LangChain to solve complex, non-linear business processes.
• Lead the design and construction of cloud-native solutions, using Terraform, Kubernetes, and Docker to ensure that AI models are deployed on scalable, reliable infrastructure.
• Apply rigorous statistical evaluation frameworks to model performance, moving beyond standard metrics to include uncertainty estimation, calibration, and robust hypothesis testing during model optimization (LoRA, QLoRA).
• Lead the development of custom predictive models and deep learning solutions, utilizing frameworks like PyTorch and Scikit-Learn to select suitable architectures—whether decision trees, neural nets, or ensembles—based on performance and client criteria.
• Design and implement state-of-the-art generative models for NLP and multimodal tasks, leveraging tools like OpenCV for image preprocessing and Stable Diffusion concepts where applicable.
• Champion MLOps best practices within the team, building validated data pipelines and CI/CD/CT workflows using Kubeflow and Vertex AI Pipelines to ensure model quality and integrity.
• Collaborate directly with clients to understand their unique needs, translating business challenges into technical solutions and providing expert guidance on dataset management best practices.
• Personally tackle the most difficult engineering challenges, identifying technical risks such as overfitting or latency issues, and optimizing hyperparameters to ensure precision and interpretability.

Required Experience and Qualifications

• 7+ years of technical experience, with at least 3+ years focused on ML/AI and 1 year in a consulting capacity.
• Experience building and evaluating agentic loops, including tool-use (function calling), self-reflection, and multi-step reasoning architectures.
• Deep proficiency in the modern Python AI stack, including extensive experience with core libraries (NumPy, Pandas, PyTorch) and specialized LLMOps/Agentic tools for monitoring and evaluation (e.g., LangSmith, Braintrust, AgentOps, or HoneyHive)
• Proven track record of building AI/ML solutions for users, including experience with GenAI common solutions like Vertex AI, OpenAI API, and vector database technologies.
• Strong foundation in probabilistic modeling, Bayesian statistics, and experimental design (A/B testing for AI) to ensure model reliability and groundedness.
• Excellent verbal and written communication skills, with the ability to confidently articulate complex AI concepts to business, technical, and non-technical stakeholders.

Education/Certifications

• Google Cloud: Professional Machine Learning Engineer
• Industry: Databricks Certified Machine Learning Professional OR DeepLearning.AI Generative AI Specialization.
• Education in: Computer Science, Mathematics, Machine Learning/Data Science

What We Offer

• Competitive Compensation – Market-aligned salary reflecting your expertise and impact
• Remote Work Flexibility – Work in a remote first organization, but can still collaborate at multiple offices globally.
• Comprehensive Health Benefits – Medical, dental, vision, and wellness coverage for you and your family.
• Flexible Paid Time Off – Take the time you need with a results-focused approach
• Professional Development – Work with industry leaders, and learn from the most talented engineers in the industry. 
• Google Cloud Training & Certifications – Access to leading cloud education resources.
• High-Impact Client Work – Enterprise engagements shaping the future of Cloud, Data Platforms, and Agents
• Collaborative Culture – Professional, transparent, and team-oriented environment.