Are you looking to thrive in a stimulating work environment?  

Join Levio, a leader in digital transformation, and take your career to the next level. You will work alongside high-caliber professionals on ambitious, large-scale technology projects, directly embedded in our clients’ environments. At Levio, we value expertise, curiosity, and continuous improvement — and we give you the space to grow.  

About the Role

Are you looking to thrive in a stimulating work environment?  

Join Levio, a leader in digital transformation, and take your career to the next level. You will work alongside high-caliber professionals on ambitious, large-scale technology projects, directly embedded in our clients’ environments. At Levio, we value expertise, curiosity, and continuous improvement — and we give you the space to grow.  

About the Role

We are seeking a Senior Machine Learning Engineer to join the Growth Tech Alliance. In this role, you will architect and deploy the robust infrastructure behind our intelligent marketing systems. You will be responsible for maturing algorithmic prototypes into high-performance production systems, ensuring our AI-driven marketing optimization is served reliably and autonomously at a global scale.

S'more about the team

We are hiring a Senior Machine Learning Engineer to take our AI tooling to the next level by architecting and deploying the robust infrastructure behind our intelligent marketing optimization systems. You will provide critical engineering execution for our AI initiatives. You will develop scalable microservices for predictive scoring, orchestrate complex LLM-based agents for creative intelligence. As the ML engineering expert for the team, you will drive the maturation of algorithmic prototypes into high-performance production systems with maximum Speed & Agility, shaping the future of how HelloFresh automates marketing at an unprecedented scale.

Lettuce share what this role will be responsible for

As a core member of the engineering team, you will focus on productionizing ML infrastructure across several domains:

• Build robust integration layers for visual AI pipelines that process multi-modal embeddings that power various predictive models.
• Transition proof-of-concept models into resilient production microservices and architect LLM-based orchestration frameworks.
• Engineer high-throughput, low-latency data pipelines to process 1P data and pipeline signals into external platforms like Meta and Google.
• Collaborate with data scientists and other engineers in a cross-functional team to improve HelloFresh’s value forecasting efficiency.
• Establish CI/CD processes, feature stores, and drift detection to ensure continuous delivery and model reliability.
• All other duties, as assigned

Sound a-peeling? Here's what we're looking for

• Experience leading the end-to-end lifecycle of production ML systems, from architectural design to scalable deployment and monitoring.
• Expertise in leveraging hyperscaler ecosystems (AWS, GCP, Azure) to build cost-effective, resilient, and automated ML infrastructure.
• Deep technical proficiency with modern ML frameworks (PyTorch, TensorFlow, HuggingFace)
• Expert programming skills in Python and PySpark.
• A BS/MS in Computer Science or a related engineering field, coupled with a proven track record of bringing ML systems from prototype to high-traffic production.
• Proven experience engineering robust data architectures that reliably process and combine diverse data formats, ranging from structured offline conversion data to unstructured multimedia assets.
• A demonstrated bias for action and extreme ownership, eager to adopt Gen AI tools to creatively solve architectural challenges and enhance engineering velocity.

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

#Engineering

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.

The Growth Tech Alliance's goal is to enable HelloFresh to drive smarter marketing through AI-powered automation, intelligent systems, and advanced optimization. We've pioneered cutting-edge approaches to programmatic campaign optimization using state-of-the-art AI/ML technologies and are now scaling these capabilities across our global marketing operations. Our team’s key responsibilities include designing, building, maintaining autonomous decision-making systems, advanced ML models for marketing optimization, and real-time intelligence that drives measurable business impact. We work at the intersection of modern AI architectures, deep statistical modelling, and enterprise-scale ML operations.

S'more about the team

As a Senior Data Scientist in AdTech, you will design, build, and deploy production-grade machine learning models that drive significant marketing ROI. You will provide critical expertise in advancing our intelligent marketing optimization systems, focusing on the entire ML lifecycle from data preprocessing and feature engineering to large-scale deployment and performance monitoring. By translating complex marketing challenges into robust production systems, you will ensure our AI-driven capabilities deliver measurable business impact and enhanced efficiency at a  global scale

Lettuce share what this role will be responsible for

• Develop, build, and iterate on advanced machine learning models across core adtech domains to optimize marketing performance and contribute to the orchestration of highly efficient marketing workflows.
• Design and implement ML models for Media Optimization, and contribute to their operationalization into high-impact production systems.
• Contribute to Creative Automation efforts by building and enhancing predictive AI models to guide our global production studios.
• Design and build ML models for Value Optimization to intelligently isolate high-value customers.
• Adhere to and contribute to the team's ML operations (MLOps) practices, focusing on experiment tracking, system observability, and model reliability in production.
• Rapidly prototype innovative machine learning solutions, including Gen AI-forward approaches, and integrate them into our existing, orchestrated multi-agent systems to enable complex autonomous workflows.
• Collaborate closely with Product, Engineering, and Marketing to identify high-impact programmatic automation opportunities and execute robust A/B testing and experimentation designs for models in high-noise environments.
• All other duties, as assigned

Sound a-peeling? Here's what we're looking for

• MSc or equivalent experience in a quantitative discipline with proven experience building and deploying AI/ML systems from prototype to production at scale
• Expert proficiency in Python, PySpark, and SQL, hands-on experience with cloud environments (GCP, AWS, or Azure), and practical experience with modern machine learning frameworks such as HuggingFace, TensorFlow or similar.
• Expertise in applied machine learning relevant to AdTech, including predictive modeling, optimization algorithms, and modern LLM capabilities preferred.
• Proven experience tackling complex marketing data problems, such as algorithmic budget pacing, LTV prediction pipelines, or AI-driven media optimization systems.
• Strong foundation in statistical analysis and experimentation, with practical experience designing A/B tests to validate the incremental business impact of algorithmic systems
• Demonstrated ability to effectively communicate intricate analytical findings to diverse audiences, bridging the gap between deep technical implementation and core business KPIs
• Track record of building production ML systems with robust practices, demonstrating a bias for action and extreme ownership, treating the business outcomes of your algorithms with a true Founder Mentality
• Experience with programmatic advertising ecosystems, marketing analytics, and the operational aspects of running performance-critical ML models in high-traffic environments

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.

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Fraud Data Science team safeguards Robinhood and its customers by detecting and preventing fraud and abuse across our platform. We leverage machine learning and analytics to combat malicious behavior in real time, supporting a safe and trusted experience for all users. Our work has a direct impact on customer security, company risk posture, and regulatory compliance.

As a Senior Data Scientist on the Fraud team, you will own the design and deployment of ML solutions that proactively surface suspicious activity, reduce financial loss, and improve fraud detection precision. You’ll collaborate closely with engineering, product, risk, and compliance partners to influence system architecture, shape policy through data, and enhance the safety and integrity of our platform.

This role is based in our Menlo Park office, with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams. 

What you’ll do

• Design and deploy fraud detection models to protect Robinhood users and assets in real time
• Analyze behavioral data to uncover emerging fraud vectors and support rapid incident response
• Develop robust data pipelines and monitoring systems to ensure model accuracy and reliability
• Partner with engineering and product teams to implement safeguards and user-facing features
• Guide experimentation strategy and contribute to long-term fraud prevention roadmap

What you bring

• 5+ years of experience in data science or applied ML, with a focus on fraud detection or risk mitigation
• Advanced proficiency in Python and SQL; experience with ML frameworks like XGBoost, LightGBM, or TensorFlow
• Strong statistical acumen with experience in anomaly detection, pattern recognition, and A/B testing
• Excellent communication skills and ability to influence decision-making across technical and non-technical audiences
• A collaborative mindset and proactive approach to navigating ambiguity in fast-paced environments

What we offer

• Challenging, high-impact work to grow your career.
• Performance-driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching.
• Best-in-class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents.
• Lifestyle wallet — a highly flexible benefits spending account for wellness, learning, and more.
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits.
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.

Join the team revolutionizing AI computing at Tenstorrent. You'll work on TT-Forge, our MLIR-based compiler that enables developers to run AI on all configurations of Tenstorrent hardware using an open-source, performant, and general-purpose compiler. You will be at the forefront of the AI hardware revolution, building compiler technologies that redefine what’s possible.

 This role is hybrid and based out of Toronto, ON.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

• A passionate software engineer eager to work on compiler technologies and the challenges of AI hardware, whether from compilers, systems, or broader software backgrounds.
• Fluent in C++ and Python, with experience building complex systems that bridge high-level frameworks to low-level execution.
• Excited by compiler optimization and machine learning, with experience in PyTorch, JAX, TensorFlow, or deep systems programming.
• A collaborative problem-solver who thrives in open-source and enjoys working closely with hardware and software engineers.

What We Need

• A drive to solve novel challenges in AI compilation, from optimizing computational graphs to creating custom dialects and transformation passes.
• Experience or strong interest in MLIR and how modular compiler frameworks connect AI models to advanced hardware.
• Motivation to build technology that impacts the future of AI, knowing your work will enable the next wave of breakthroughs.

What You Will Learn

• How to build open-source compiler frameworks supporting diverse AI models and workloads, including training and multi-chip scaling.
• Deep expertise in compiler technologies including custom MLIR dialects (TTIR, TTNN, TTKernel) and transformation passes.
• New methods for human-in-the-loop compiler optimization using TT-Explorer, making advanced tuning tools usable by all developers.
• How compiler technology powers Tenstorrent’s mission to deliver affordable, open-source AI computing in a highly competitive space.

Join the team revolutionizing AI computing at Tenstorrent. You'll work on TT-Forge, our MLIR-based compiler that enables developers to run AI on all configurations of Tenstorrent hardware using an open-source, performant, and general-purpose compiler. You will be at the forefront of the AI hardware revolution, building compiler technologies that redefine what’s possible.

 This role is hybrid, and can be based out of Santa Clara, CA; Austin, TX; or Toronto; ON.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

• A passionate software engineer eager to work on compiler technologies and the challenges of AI hardware, whether from compilers, systems, or broader software backgrounds.
• Fluent in C++ and Python, with experience building complex systems that bridge high-level frameworks to low-level execution.
• Excited by compiler optimization and machine learning, with experience in PyTorch, JAX, TensorFlow, or deep systems programming.
• A collaborative problem-solver who thrives in open-source and enjoys working closely with hardware and software engineers.

What We Need

• A drive to solve novel challenges in AI compilation, from optimizing computational graphs to creating custom dialects and transformation passes.
• Experience or strong interest in MLIR and how modular compiler frameworks connect AI models to advanced hardware.
• Motivation to build technology that impacts the future of AI, knowing your work will enable the next wave of breakthroughs.

What You Will Learn

• How to build open-source compiler frameworks supporting diverse AI models and workloads, including training and multi-chip scaling.
• Deep expertise in compiler technologies including custom MLIR dialects (TTIR, TTNN, TTKernel) and transformation passes.
• New methods for human-in-the-loop compiler optimization using TT-Explorer, making advanced tuning tools usable by all developers.
• How compiler technology powers Tenstorrent’s mission to deliver affordable, open-source AI computing in a highly competitive space.

Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

As a Data Scientist on the Mapping team, you will collaborate with our world class team of engineers, product managers, and designers to grow and improve the quality of recommended routes and accuracy of our travel time estimations. We're looking for a passionate, driven Data Scientist who is excited to dive into our spatial data and build a best-in-class mapping product that provides safe, efficient, and seamless navigation for our rideshare drivers.

Data Science is at the heart of Lyft’s products and decision-making. You will leverage data and rigorous, analytical thinking to shape our mapping products and make business decisions that put our customers first. This will involve identifying and scoping opportunities, shaping priorities, recommending technical solutions, designing experiments, and measuring the impact of new features. You will help us solve some of the most impactful problems in mapping, including:

• How do we improve the quality of our map data in order to improve our recommendations?
• How do we benchmark and measure the success of our services?
• How do we validate features of the real world that affect our routing algorithms?
• Are we meeting our travel estimation promises to our customers?

Responsibilities

• Leverage data and analytic frameworks to identify opportunities for growth and efficiency
• Partner with product managers, engineers, and operators to translate analytical insights into decisions and action, and implement products to drive business goals
• Define and implement decision frameworks, measurement strategies, and scientific methodologies that bring consistency and rigor to business decisions and forecasts, balancing opportunity and uncertainty
• Deliver integrated, high-quality analytical outputs spanning multiple projects while navigating ambiguity, cross-team dependencies, and open-ended scope
• Design and analyze online experiments; communicate results and act on launch decisions
• Establish metrics that measure the health of our products, as well as rider and driver experience
• Identify and drive impact and alignment, shaping product and business strategy through data-centric presentations 
• Contribute to the Science community (hiring, onboarding, documentation, knowledge-sharing, tooling improvements), helping make Decision Science at Lyft more effective and scalable

Experiences

• Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience
• 3+ years experience in a data science role or analytics role
• Demonstrated ability to own multi-project analytical scopes with ambiguous problem definitions and cross-functional integration
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Proficiency in SQL - able to write structured and efficient queries on large data sets
• Experience in programming, especially with data science and visualization libraries in Python or R, and machine learning libraries such as PyTorch, TensorFlow, Keras
• Experience in online experimentation and statistical analysis, and communicating results and recommendations to senior stakeholders
• Strong communication, critical thinking, and prioritization skills, including the ability to challenge assumptions, propose alternatives, and balance short-term vs. long-term tradeoffs
• Experience in applying machine learning techniques is a plus (e.g. reinforcement learning) to solve customer problems (e.g. personalization, segmentation)
• Expertise in metric design, causal analysis, behavioral analytics, decision frameworks and measurement strategy is a plus
• Experience working with ETL pipelines a plus

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is $108,000 - $135,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

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 we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Supportability Evaluation team acts as stewards of the financial ecosystem. Our mission is to protect Stripe’s reputation with our global financial partners by architecting highly precise, automated supportability controls. We develop the AI/ML models and systems that detect and action supportability violations in real-time. We're responsible for building high-fidelity detection engines that ensure our merchants remain compliant across the globe, balancing the scale of millions of users with the surgical precision required by the world’s largest financial institutions.

What you’ll do

As a Machine Learning Engineer in Supportability, you will be responsible for designing, building, training, evaluating, deploying, and owning AI/ML models in production. You will work closely with software engineers, machine learning engineers, product managers, and data scientists to operate Stripe’s ML powered systems, features, and products. You will also have the opportunity to contribute to and influence AI/ML architecture at Stripe and be a part of a larger community.

Responsibilities

• Design state-of-the-art AI/ML models and large scale systems for detection and decisioning for Stripe products based onAI/ML principles, domain knowledge, and engineering constraints
• Drive the expansion of Stripe's largest LLM-based system, scaling its usage and integrating new capabilities through agentic approaches or supervised learning.
• Rapidly prototype new AI/ML-based approaches to achieve key business goals.  
• Develop processes to train and evaluate models in offline and online environments
• Integrate models into production systems and ensure their scalability and reliability
• Collaborate with product and strategy partners to propose, prioritize, and implement new product features
• Engage with the latest developments in AI/ML and take calculated risks in transforming innovative ideas into productionized solutions
• Explore cutting-edge AI/ML techniques and evaluate their potential to solve business problems

Who you are

We are looking for ML Engineers who are passionate about building AI/ML and AI systems that touch the lives of millions. You have experience building and evaluating advanced AI/ML models, and deploying them to production. You are comfortable with ambiguity, love to take initiative, have a bias towards action, and thrive in a collaborative environment.

Minimum requirements

• 2+ years of industry experience building and shipping AI/ML systems in production
• Proficient with AI/ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark 
• Knowledge of various AI/ML algorithms and model architectures
• Hands-on experience in designing, training, and evaluating machine learning models
• Hands-on experience in productionizing and deploying models at scale
• Experience rigorously evaluating model performance, including cleaning data, and working with data-generating processes to improve signal and reduce noise in high-noise datasets.
• Proficiency in creatively applying modern machine learning techniques and Generative AI models to solve complex business problems.

Preferred qualifications

• MS/PhD degree in AI/ML or related field (e.g. math, physics, statistics)
• Experience with DNNs including the latest architectures such as transformers and LLMs
• Experience working in Java or Ruby codebases
• Proven track record of building and deploying AI/ML systems that have effectively solved ambiguous business problems
• Experience with online experimentation such as A/B testing or multi-armed bandits.
• Experience with model calibration

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Support Experience engineering organization builds and improves Stripe’s user support from end to end: how users get help within our products, how they get in touch with us when they have questions, and how our teams use internal tools to answer those questions. We’re accountable for the quality and reliability of this support stack and we use data and firsthand user research to continuously improve it. 

Providing great support to users of all sizes is culturally important to everyone at Stripe. We are a group of friendly, user-oriented engineers that partner closely with Stripe’s world-class design, product, and operational teams. This includes the external-facing support interfaces (support.stripe.com), content, entry points, internal tooling, case routing, and helping product teams across the company reduce support volume by improving our products. We are also using the latest generative AI technologies to re-imagine support experiences, and are developing AI assistants for Stripe’s users and internally to help our operations teams be more productive.

What you’ll do

As a Machine Learning Engineer on the Support Experience team, you'll play a crucial role in enhancing our self-serve support experiences. You will be responsible for designing, building, training, evaluating, deploying, and owning ML models in production. For example, we apply LLMs to answer user questions with conversational agents and personalize product documentation, and are building automated systems to solve complex user problems. You will work closely with software engineers, machine learning engineers, product managers, and data scientists to operate Stripe’s ML powered systems, features, and products. You will also have the opportunity to contribute to and influence ML architecture at Stripe and be a part of a larger ML community. 

Responsibilities

• Design  and implement state-of-the-art ML models and large scale ML systems for enhancing self-serve support capabilities, balancing ML principles, domain knowledge, and engineering constraints
• Develop and optimize contextual conversation models and ML-powered resolution flows for common support scenarios, using tools such as PyTorch, TensorFlow, and XGBoost
• Create and refine pipelines for training and evaluating models in both offline and online environments, with a focus on improving support quality and user satisfaction
• Implement ML features that streamline information collection and processing for support agents, enhancing overall support efficiency
• Collaborate with product, strategy, and content teams to propose, prioritize, and implement new AI-driven support features and improve answer capabilities
• Stay current with the latest developments in ML/AI, particularly in natural language processing and conversational AI, and apply innovative ideas to improve support experiences

Who you are

We are looking for ML Engineers who are passionate about building ML systems that touch the lives of millions. You have experience developing efficient feature pipelines, building advanced ML models, and deploying them to production. You are comfortable with ambiguity, love to take initiative, have a bias towards action, and thrive in a collaborative environment.

Minimum requirements

• Bachelor's Degree in ML/AI or related field (e.g. math, physics, statistics)
• 3+ years in AI/ML and backend engineering, including building and operating production ML systems at global scale with stringent SLOs—balancing reliability, latency, and cost—with privacy, security, and compliance by design.
• Deep and up-to-date applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration architectures, post-training methods, code generation, benchmarks and evaluations, etc. Familiarity with classical ML methods and common frameworks e.g. Pytorch, TensorFlow.
• Proficient in Python; strong distributed systems and data science fundamentals.
• Experience working closely with product management, design, other engineers, and other cross-functional partners.
• Strong technical leadership and communication: mentoring and elevating engineers, elevating AI/ML awareness and posture within organizations, setting architectural direction, and driving alignment in ambiguity.

Preferred qualifications

• MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics)
• Experience working in Java or Ruby codebases
• Experience designing, deploying, and owning Agentic LLM solutions (e.g., multi-step orchestrators, tool use/function calling) specifically for complex customer support or internal workflow automation.
• Comfortable working with distributed teams across multiple locations and time zones

About the Role:

The Machine Learning team at Tubi drives the innovation behind personalized user experiences.  With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding and ads optimization that shape the future of streaming.

We are seeking a highly skilled Machine Learning Engineer to contribute to transformative projects in video personalization. In this role, you will design and implement advanced algorithms and systems to improve our personalization strategy. As a senior technical expert, you will tackle complex problems in machine learning at scale, collaborating closely with cross-functional teams to develop and optimize machine learning-driven solutions.

What You'll Do:

• Lead the design, development, and implementation of advanced recommendation systems and algorithms for a global audience
• Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas
• Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
• Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences.
• Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement

Your Background:

• 8+ years of industry experience building production Machine Learning systems
• MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field
• Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks
• Proficiency in building and deploying full-stack machine learning pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
• Solid understanding of statistical concepts such as hypothesis testing, regression analysis, and performance evaluation metrics for machine learning.
• Ability to deep dive into individual components and systems, as well as understand the overall architecture of machine learning solutions.

#LI-Hybrid #LI-SC1

About the Role:

The Machine Learning team at Tubi drives the innovation behind personalized user experiences. With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding, and ads optimization that shape the future of streaming.

We are seeking a Director of Machine Learning Engineering and Infrastructure to lead a hybrid team bridging advanced ML engineering with world-class infrastructure design. In this role, you will own the strategic direction and execution for scaling our machine learning capabilities while ensuring our distributed systems and infrastructure can support innovation at massive scale. You will combine technical depth with leadership excellence to guide teams that deliver both foundational ML systems and high-performance distributed services.

What You'll Do: 

• Lead and manage high-performing teams across ML engineering and ML infrastructure, fostering a culture of innovation, collaboration, and growth.
• Define and execute the strategic roadmap for ML systems, including recommendation, personalization, and ads optimization.
• Oversee the design, development, and deployment of scalable ML pipelines: data ingestion, feature engineering, model training, evaluation, and serving.
• Architect distributed systems to support ML workloads at scale, ensuring reliability, observability, and operational excellence.
• Partner closely with Product, Engineering, and Content teams to align on business goals and deliver impactful ML-driven experiences.
• Support best practices in experimentation, evaluation, and ML system monitoring.
• Ensure cost efficiency, scalability, and performance in ML infrastructure investments.

Your Background:

• 10+ years of industry experience spanning machine learning engineering and distributed systems.
• 3+ years of leadership and management experience, with a proven ability to build and lead strong technical teams.
• MSc or Ph.D. in Computer Science, Machine Learning, or related field, or equivalent practical experience.
• Proven expertise in building and deploying end-to-end ML systems at scale, including recommendation and personalization systems.
• Strong background in distributed systems architecture, including low-latency services, streaming platforms, and large-scale serving.
• Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and ML infrastructure technologies.
• Track record of delivering high-quality, scalable, and fault-tolerant systems.
• Excellent communication skills and ability to influence product and technical strategy.
• Proven experience deploying large-scale serving systems on AWS and demonstrated expertise in leveraging Databricks for large-scale data processing and ML workflows

WHAT YOU'LL DO

Our Data Scientist, AI Deployment team is a group of creative technical experts who design and build end-to-end machine learning solutions that power 1-to-1 personalization for some of the world's leading brands. In this role, you will:

• Design ML use cases from the ground up — scoping solutions that optimize for real business value, accounting for the complexity of modern marketing journeys, and proactively identifying risks to set each engagement up for success
• Build and own the full ML pipeline —  taking customers' raw data through transformation, model training, and activation, so that model decisions are delivered to personalize experiences for millions of end users
• Drive customer success by providing ongoing technical guidance that ensures data science performance, successful adoption, and measurable outcomes
• Extend product capabilities by developing features and tools that support the broader AI deployment team and scale what's possible across engagements
• Partner with the Braze Product team to refine and advance Braze's reinforcement learning algorithms, pushing the self-learning capabilities of the platform forward
• Shape BrazeAI product strategy and roadmap by bringing customer-facing insights and deep technical expertise to the table

WHO YOU ARE

• Education: Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master’s or PhD in a relevant technical discipline preferred
• Experience: 3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
• Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
• Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
• Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL, and pipeline optimization, or reinforcement learning algorithms
• Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
• Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
• Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
• Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences

For candidates based in Ontario, the pay range at the start of employment for this position is expected to be between CA$112,000 - CA$168,000/year, with an expected On Target Earnings (OTE) between CA$125,000 - CA$188,000/year (including performance-based or variable compensation (bonus or commission). Your particular offer may vary depending on multiple individual factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

#LI-Hybrid

MaintainX est la première plateforme mobile au monde dédiée à la gestion des actifs et des tâches dans les environnements industriels et de première ligne. Nous proposons une solution moderne, basée sur l'IoT et le cloud, qui facilite la maintenance, la sécurité et l'exploitation des équipements et installations physiques.

Nous aidons plus de 12 000 organisations, dont Duracell, Univar Solutions, Titan America, McDonald's, Brenntag, Cintas, Xylem et Shell, à atteindre l'excellence opérationnelle et la fiabilité à grande échelle.

À la suite de notre série D de 150 millions de dollars menée par Bain Capital Ventures, Bessemer Ventures, August Capital, Amity Ventures et Ridge Ventures, MaintainX a levé un total de 254 millions de dollars, valorisant la société à 2,5 milliards de dollars.

Alors que nous entrons dans notre prochaine phase de croissance, nous investissons massivement dans l'IA/ML, les LLM et l'IoT industriel afin de transformer le mode de fonctionnement des équipes de première ligne, en prédisant les pannes avant qu'elles ne se produisent, en automatisant les flux de travail et en intégrant l'intelligence dans chaque actif et chaque procédure.

Le poste

Nous recherchons un développeur(se) senior en apprentissage automatique appliqué hautement qualifié et motivé pour orienter la direction technique et l'architecture de nos initiatives en matière de maintenance prédictive et d'intelligence des actifs.

Vous combinerez une expertise approfondie en apprentissage automatique avec de solides compétences en génie logiciel et en sens du leadership. Vous encadrerez des ingénieurs, développerez des systèmes et piloterez la feuille de route pour l'intelligence de maintenance basée sur l'IA sur des milliers de sites industriels.

Ce poste se situe à la croisée de l'architecture d'apprentissage automatique, des systèmes de données IoT et de l'impact des produits, et constitue le fondement de la stratégie d'IA prédictive et générative de MaintainX.

Ce que vous ferez:

• Diriger la direction technique pour la maintenance prédictive, la détection des anomalies et l'intelligence alimentée par LLM dans tous les produits MaintainX.
• Concevoir des systèmes ML de bout en bout, de l'ingestion des données et le développement des fonctionnalités à la formation, au déploiement et à la surveillance des modèles.
• Encadrer une équipe croissante de développeurs(ses) ML et de données, en inculquant les meilleures pratiques en matière d'expérimentation, d'évaluation et de gestion du cycle de vie des modèles.
• Collaborer avec les responsables des produits et de développement afin d'aligner la feuille de route de l'IA sur les besoins des clients et les objectifs commerciaux.
• Concevoir des boucles de données et de rétroaction fiables qui relient la télémétrie des clients et les commentaires des opérateurs au réentraînement des modèles.
• Optimiser les performances grâce à des techniques telles que la quantification, la distillation et le service d'inférence évolutif.
• Travailler avec des cadriciels LLM (LangChain, LlamaIndex, Hugging Face) pour créer des systèmes de raisonnement et des flux de travail agentifs pour l'intelligence des actifs et du travail.
• S'assurer que l'infrastructure ML répond aux normes de production en matière de latence, de fiabilité, d'explicabilité et de sécurité.

À propos de vous:

• Plus de 7 ans d'expérience dans le domaine de l'apprentissage automatique, de la science des données ou de l'IA appliquée.
• Expertise en Python et excellente maîtrise de PyTorch, TensorFlow et des piles ML cloud (AWS, Databricks ou similaires).
• Expérience avérée dans le déploiement à grande échelle de systèmes ML de production, et pas seulement de prototypes.
• Solide expérience en LLM, modélisation de séries chronologiques et détection d'anomalies pour les données du monde réel.
• Capacité avérée à diriger les décisions architecturales, à encadrer les développeurs(ses) et à collaborer avec les équipes chargées des produits, des données et des plateformes.
• Connaissance des outils MLOps (Docker, Kubernetes, Weights & Biases, MLflow, SageMaker).
• Diplôme de deuxième cycle (master/doctorat) en informatique, apprentissage automatique ou dans un domaine connexe préféré.

Une attention particulière est accordée aux candidats présentant les caractéristiques suivantes:

• Expérience avec l'OCR pour l'extraction de données structurées à partir de documents.
• Expérience dans la modélisation de séries chronologiques pour la maintenance prédictive et la détection d'anomalies.
• Connaissance des systèmes IoT industriels (capteurs, télémétrie, edge computing).
• Expérience dans l'application de l'apprentissage par renforcement ou des architectures agentuelles pour les systèmes de prise de décision et de contrôle.
• Contributions à des frameworks ML open source ou à la recherche dans les domaines de la fiabilité, de l'explicabilité ou des jumeaux numériques.

Quels sont les avantages pour vous?:

• Un salaire compétitif et des opportunités d'équité significatives.
• Couverture des soins de santé, des soins dentaires et de la vue.
• Programme d'inscription 401(k) / RRSP.
• Prenez les congés dont vous avez besoin.
• Une culture du travail :
• Vous travaillerez avec des personnes intelligentes, humbles et optimistes à travers le monde.
• Un environnement méritocratique où les idées et les résultats sont publiquement célébrés.

Qui sommes-nous:

MaintainX s'engage à créer un environnement diversifié. Tous les candidats qualifiés seront pris en considération pour un emploi sans considération de race, de couleur, de religion, de sexe, d'identité ou d'expression de genre, d'orientation sexuelle, d'origine nationale, de génétique, d'invalidité, d'âge ou de statut d'ancien combattant.

MaintainX est la première plateforme mobile au monde dédiée à la gestion des actifs et des tâches dans les environnements industriels et de première ligne. Nous proposons une solution moderne, basée sur l'IoT et le cloud, qui facilite la maintenance, la sécurité et l'exploitation des équipements et installations physiques. Nous aidons plus de 12 000 organisations, dont Duracell, Univar Solutions, Titan America, McDonald's, Brenntag, Cintas, Xylem et Shell, à atteindre l'excellence opérationnelle et la fiabilité à grande échelle. À la suite de notre série D de 150 millions de dollars menée par Bain Capital Ventures, Bessemer Ventures, August Capital, Amity Ventures et Ridge Ventures, MaintainX a levé un total de 254 millions de dollars, valorisant la société à 2,5 milliards de dollars. Alors que nous entrons dans notre prochaine phase de croissance, nous investissons massivement dans l'IA/ML, les LLM et l'IoT industriel afin de transformer le mode de fonctionnement des équipes de première ligne, en prédisant les pannes avant qu'elles ne se produisent, en automatisant les flux de travail et en intégrant l'intelligence dans chaque actif et chaque procédure.

Ce que vous ferez:

• Concevoir, développer et optimiser des modèles d’apprentissage automatique (machine learning) pour la détection et la classification de défauts de bout en bout — des choix liés aux données et à l’entraînement jusqu’aux stratégies d’évaluation et aux contraintes de mise en production.
• Réaliser des analyses exploratoires de données (EDA) sur des données de vibration, des données OT (technologies opérationnelles) et des séries temporelles afin d’en tirer des insights et d’identifier des motifs révélateurs de défaillances ou d’anomalies.
• Mener des expérimentations et évaluer différents algorithmes en modélisation de séries temporelles, en traitement du signal et en méthodes statistiques afin d’optimiser la performance des modèles.
• Collaborer avec les chefs de produit (PM) pour la découverte de fonctionnalités et la priorisation de la feuille de route, notamment en validant des hypothèses produit, en définissant des indicateurs de succès et en quantifiant l’impact pour les utilisateurs finaux.
• Travailler en étroite collaboration avec des experts du domaine afin de valider les résultats et d’assurer leur pertinence dans des contextes réels.
• Participer activement à la communauté interne de pairs pour remettre en question le statu quo, améliorer les méthodes de travail et influencer les décisions d’architecture, tout en contribuant à une culture d’excellence en science appliquée.
• Participer aux rotations de garde (on-call).

À propos de vous:

• Maîtrise ou doctorat en informatique, science des données, génie mécanique, génie électrique ou dans un domaine connexe, avec une spécialisation en surveillance d’état (condition monitoring) ou en apprentissage automatique.
• Plus de 5 ans d’expérience démontrée en programmation avec des outils standards de ML tels que Python, PyTorch, TensorFlow, etc.
• Solides connaissances fondamentales en apprentissage automatique, en science des données et en statistiques.
• Bonne maîtrise des techniques de modélisation de séries temporelles et de l’ingénierie de caractéristiques (feature engineering).
• Capacité à livrer du code prêt pour la production, bien testé, maintenable et validé par des expérimentations rigoureuses.

Atouts:

• Expérience pratique dans le développement de modèles appliqués aux données OT et de vibration, à la surveillance d’état et à la détection ou classification de défauts.
• Connaissance des techniques de traitement du signal (p. ex. transformée de Fourier, analyse en ondelettes) et de leur application aux données OT et de vibration.

Quels sont les avantages pour vous?:

• Un salaire compétitif et des opportunités d'équité significatives.
• Couverture des soins de santé, des soins dentaires et de la vue.
• Programme d'inscription 401(k) / RRSP.
• Prenez les congés dont vous avez besoin.
• Une culture du travail :
• Vous travaillerez avec des personnes intelligentes, humbles et optimistes à travers le monde.
• Un environnement méritocratique où les idées et les résultats sont publiquement célébrés.

Qui sommes-nous:

MaintainX is the world’s leading mobile-first Asset and Work Intelligence platform for industrial and frontline environments. We’re a modern, IoT-enabled, cloud-based solution that powers maintenance, safety, and operations on physical equipment and facilities.

We help 12,000+ organizations—including Duracell, Univar Solutions, Titan America, McDonald’s, Brenntag, Cintas, Xylem, and Shell—achieve operational excellence and reliability at scale.

Following our $150 million Series D led by Bain Capital Ventures, Bessemer Ventures, August Capital, Amity Ventures, and Ridge Ventures, MaintainX has raised a total of $254 million, valuing the company at $2.5 billion.

As we enter our next phase of growth, we’re investing deeply in AI/ML, LLMs, and Industrial IoT to transform how frontline teams operate—predicting failures before they happen, automating workflows, and embedding intelligence into every asset and procedure.

The Role

We are seeking a highly skilled and motivated Senior Applied Machine Learning Developer to guide the technical direction and architecture of our Predictive Maintenance and Asset Intelligence initiatives.

You’ll combine deep ML expertise with strong software development and leadership skills—mentoring developers, scaling systems, and driving the roadmap for AI-enabled maintenance intelligence across thousands of industrial sites.

This role sits at the intersection of ML architecture, IoT data systems, and product impact, shaping the foundation for MaintainX’s predictive and generative AI strategy.

What you’ll do:

• Lead technical direction for predictive maintenance, anomaly detection, and LLM-powered intelligence across MaintainX products.
• Architect end-to-end ML systems—from data ingestion and feature development to model training, deployment, and monitoring.
• Mentor a growing team of ML and data developers, instilling best practices for experimentation, evaluation, and model lifecycle management.
• Partner with product and software development leaders to align AI roadmap with customer needs and business goals.
• Design reliable data and feedback loops that connect customer telemetry and operator feedback to model retraining.
• Drive performance optimization through techniques like quantization, distillation, and scalable inference serving.
• Work with LLM frameworks (LangChain, LlamaIndex, Hugging Face) to build reasoning systems and agentic workflows for asset and work intelligence.
• Ensure ML infrastructure meets production standards for latency, reliability, explainability, and security.

About you:

• 7+ years of experience in Machine Learning, Data Science, or Applied AI.
• Expertise in Python, and strong familiarity with PyTorch, TensorFlow, and cloud ML stacks (AWS, Databricks, or similar).
• Proven experience deploying production ML systems—not just prototypes—at scale.
• Strong background in LLMs, time-series modeling, and anomaly detection for real-world data.
• Demonstrated ability to lead architectural decisions, mentor developers, and collaborate across product, data, and platform teams.
• Knowledge of MLOps tooling (Docker, Kubernetes, Weights & Biases, MLflow, SageMaker).
• Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field preferred.

Bonus skills: 

• Experience with OCR for extracting structured data from documents.
• Background in time-series modeling for predictive maintenance and anomaly detection.
• Familiarity with Industrial IoT systems (sensors, telemetry, edge computing).
• Experience applying reinforcement learning or agentic architectures for decision-making and control systems.
• Contributions to open-source ML frameworks or research in reliability, explainability, or digital twins.

What’s in it for you:

• Competitive salary and meaningful equity opportunities.
• Healthcare, dental, and vision coverage.
• 401(k) / RRSP enrollment program.
• Take what you need PTO.
• A high impact Culture:
• You’ll work with Smart, Humble Optimists across the globe.
• Meritocratic environment where ideas and outcomes are publicly celebrated.

About us:

We exist to make the lives of frontline and maintenance teams easier by building software that meets their real-world needs. Our product transforms how 80% of the global workforce—those who don’t sit behind a desk—manage their operations, assets, and teams.

MaintainX is committed to creating a diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

MaintainX is the world’s leading mobile-first Asset and Work Intelligence platform for industrial and frontline environments. We’re a modern, IoT-enabled, cloud-based solution that powers maintenance, safety, and operations on physical equipment and facilities.

We help 12,000+ organizations—including Duracell, Univar Solutions, Titan America, McDonald’s, Brenntag, Cintas, Xylem, and Shell—achieve operational excellence and reliability at scale.

Following our $150 million Series D led by Bain Capital Ventures, Bessemer Ventures, August Capital, Amity Ventures, and Ridge Ventures, MaintainX has raised a total of $254 million, valuing the company at $2.5 billion.

As we enter our next phase of growth, we’re investing deeply in AI/ML, LLMs, and Industrial IoT to transform how frontline teams operate—predicting failures before they happen, automating workflows, and embedding intelligence into every asset and procedure.

What you’ll do:

• Design, develop and optimize machine learning models for fault detection and classification end-to-end e.g. data and training modeling choices to evaluation strategies and production constraints.
• Perform EDA on vibration, OT and time-series data to uncover insights and identify patterns indicative of faults or anomalies.
• Conduct experiments and evaluation of various algorithms on time-series modeling, signal processing, and statistical methods, to optimize model performance.
• Partner with PMs in product feature discovery and roadmap prioritization through validating product hypotheses, designing success metrics and quantifying end user impact
• Collaborate with domain experts to validate findings and ensure alignment with real-world applications.
• Engage with your community of peers to challenge the status quo, improve our shared ways of working, and influence overall architecture decisions, continuing to foster our culture of Applied Science excellence
• On-call duties

About you:

• Master’s or Ph.D. in Computer Science, Data Science, Mechanical Engineering, Electrical Engineering, or a related field with a focus on condition monitoring or machine learning applications.
• 5+ years of proven programming skills using standard ML tools such as Python, PyTorch, Tensorflow etc.
• Strong foundational knowledge in machine learning, data science, and statistics
• Familiarity with time-series modeling techniques and feature engineering.
• Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.

Bonus skills: 

• Hands-on experience developing models for OT and vibration analysis, condition monitoring, and fault detection or classification.
• Familiarity with signal processing techniques (e.g., Fourier transforms, wavelet analysis) and their application to OT and vibration data.

What’s in it for you:

• Competitive salary and meaningful equity opportunities.
• Healthcare, dental, and vision coverage.
• 401(k) / RRSP enrollment program.
• Take what you need PTO.
• A high impact Culture:
• You’ll work with Smart, Humble Optimists across the globe.
• Meritocratic environment where ideas and outcomes are publicly celebrated.

About us:

We exist to make the lives of frontline and maintenance teams easier by building software that meets their real-world needs. Our product transforms how 80% of the global workforce—those who don’t sit behind a desk—manage their operations, assets, and teams.

MaintainX is committed to creating a diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

About the Role
We are seeking a versatile and experienced engineer to join our SOTA Training Platform team. This team is responsible to rapidly bring up state-of-the-art open-source models (like LLaMA, Qwen, etc) or customer-provided proprietary models on our Cerebras CSX systems. Success in this role requires a system-minded generalist who thrives in fast-paced bringup environments and is comfortable working across the entire Cerebras software stack.
Your work will play a critical role in achieving unprecedented levels of performance, efficiency, and scalability for AI applications.

• Contribute to the end-to-end bring up of ML models on Cerebras CSX systems.
• Work across the stack: model architecture translation, graph lowering, compiler optimizations, runtime integration, and performance tuning.
• Debug performance and correctness issues spanning model code, compiler IRs, runtime behavior, and hardware utilization.
• Propose and prototype improvements across tools, APIs, or automation flows to accelerate future bring ups.
• Study emerging training and post-training algorithms and map to Cerebras software architecture and hardware.

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field.
• 5+ years of relevant industry experience (internship/co-op experience included)
• Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc.
• Strong debugging skills across performance, numerical accuracy, and runtime integration.
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and familiarity with model internals (e.g., attention, MoE, diffusion).
• Proficiency in C/C++ programming and experience with low-level optimization.
• Proven experience in compiler development, particularly with LLVM and/or MLIR.
• Strong background in optimization techniques, particularly those involving NP-hard problems.
• Familiarity with large scale ML systems and state of the art algorithms, including model training and reinforcement learning.

• Competitive salary and benefits package.
• Opportunities for professional growth and career advancement.
• A dynamic and innovative work environment.
• The chance to work on cutting-edge technologies and make a significant impact on the future of AI.

About the Role
We are seeking a versatile and experienced engineer to join our Inference Core Model Bringup team. This team is responsible to rapidly bring up state-of-the-art open-source models (like LLaMA, Qwen, etc) or customer-provided proprietary models on our Cerebras CSX systems. Success in this role requires a system-minded generalist who thrives in fast-paced bringup environments and is comfortable working across the entire Cerebras software stack.
Your work will play a critical role in achieving unprecedented levels of performance, efficiency, and scalability for AI applications.

• Contribute to the end-to-end bring up of ML models on Cerebras CSX systems.
• Work across the stack: model architecture translation, graph lowering, compiler optimizations, runtime integration, and performance tuning.
• Debug performance and correctness issues spanning model code, compiler IRs, runtime behavior, and hardware utilization.
• Propose and prototype improvements across tools, APIs, or automation flows to accelerate future bring ups.

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field.
• Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc.
• Strong debugging skills across performance, numerical accuracy, and runtime integration.
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and familiarity with model internals (e.g., attention, MoE, diffusion).
• Proficiency in C/C++ programming and experience with low-level optimization.
• Proven experience in compiler development, particularly with LLVM and/or MLIR.
• Strong background in optimization techniques, particularly those involving NP-hard problems.

• Competitive salary and benefits package.
• Opportunities for professional growth and career advancement.
• A dynamic and innovative work environment.
• The chance to work on cutting-edge technologies and make a significant impact on the future of AI.

About The Role

We are seeking a seasoned Technical Leader to build and scale the foundational compute infrastructure that powers our state-of-the-art AI models—from multimodal training data pipelines to high-throughput, low-latency video generation.

Responsibilities

You will be the core engineer responsible for building the robust, efficient, and scalable platform that enables our research and production teams to rapidly iterate on HeyGen's generative video models. Your contributions will directly impact model performance, developer productivity, and the final quality of every AI-generated video.

• Optimize GPU Utilization: Design and implement mechanisms to aggressively optimize GPU and cluster utilization across thousands of devices for inference, training, data processing and large-scale deployment of our state-of-art video generation models.

• Develop Large-Scale AI Job Framework: Build highly scalable, reliable frameworks for launching and managing massive, heterogeneous compute jobs, including multi-modal high-volume data ingestion/processing, distributed model training, and continuous evaluation/benchmarking.

• Enhance Observability: Develop world-class observability, tracing, and visualization tools for our compute cluster to ensure reliability, diagnose performance bottlenecks (e.g., memory, bandwidth, communication).

• Accelerate Pipelines: Collaborate closely with AI researchers and AI engineers to integrate innovative acceleration techniques (e.g., custom CUDA kernels, distributed training libraries) into production-ready, scalable training and inference pipelines.

• Infrastructure Management: Champion the adoption and optimization of modern cloud and container technologies (Kubernetes, Ray) for elastic, cost-efficient scaling of our distributed systems.

Minimum Requirements

We are looking for a highly motivated engineer with deep experience operating and optimizing AI infrastructure at scale.

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

• 5+ years of full-time industry experience in large-scale MLOps, AI infrastructure, or HPC systems.

• Experience with data frameworks and standards like Ray, Apache Spark, LanceDB

• Strong proficiency in Python and a high-performance language such as C++  for developing core infrastructure components.

• Deep understanding and hands-on experience with modern orchestration and distributed computing frameworks such as Kubernetes and Ray.

• Experience with core ML frameworks such as PyTorch, TensorFlow, or JAX.

Preferred Qualifications

• Master's or PhD in Computer Science or a related technical field.

• Demonstrated Tech Lead experience, driving projects from conceptual design through to production deployment across cross-functional teams.

• Prior experience building infrastructure specifically for Generative AI models (e.g., diffusion models, GANs, or large language models) where cost and latency are critical.

• Proven background in building and operating large-scale data infrastructure (e.g., Ray, Apache Spark) to manage petabytes of multi-modal data (video, audio, text).

• Expertise in GPU acceleration and deep familiarity with low-level compute programming, including CUDA, NCCL, or similar technologies for efficient inter-GPU communication.

What HeyGen Offers

• Competitive salary and benefits package.
• Dynamic and inclusive work environment.
• Opportunities for professional growth and advancement.
• Collaborative culture that values innovation and creativity.
• Access to the latest technologies and tools.

HeyGen is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

We are seeking a seasoned Software Engineer to build and scale the foundational compute infrastructure that powers our state-of-the-art AI models—from multimodal training data pipelines to high-throughput, low-latency video generation.

Responsibilities

You will be the core engineer responsible for building the robust, efficient, and scalable platform that enables our research and production teams to rapidly iterate on HeyGen's generative video models. Your contributions will directly impact model performance, developer productivity, and the final quality of every AI-generated video.

• Optimize GPU Utilization: Design and implement mechanisms to aggressively optimize GPU and cluster utilization across thousands of devices for inference, training, data processing and large-scale deployment of our state-of-art video generation models.

• Develop Large-Scale AI Job Framework: Build highly scalable, reliable frameworks for launching and managing massive, heterogeneous compute jobs, including multi-modal high-volume data ingestion/processing, distributed model training, and continuous evaluation/benchmarking.

• Enhance Observability: Develop world-class observability, tracing, and visualization tools for our compute cluster to ensure reliability, diagnose performance bottlenecks (e.g., memory, bandwidth, communication).

• Accelerate Pipelines: Collaborate closely with AI researchers and AI engineers to integrate innovative acceleration techniques (e.g., custom CUDA kernels, distributed training libraries) into production-ready, scalable training and inference pipelines.

• Infrastructure Management: Champion the adoption and optimization of modern cloud and container technologies (Kubernetes, Ray) for elastic, cost-efficient scaling of our distributed systems.

Minimum Requirements

We are looking for a highly motivated engineer with deep experience operating and optimizing AI infrastructure at scale.

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

• 5+ years of full-time industry experience in large-scale MLOps, AI infrastructure, or HPC systems.

• Experience with data frameworks and standards like Ray, Apache Spark, LanceDB

• Strong proficiency in Python and a high-performance language such as C++  for developing core infrastructure components.

• Deep understanding and hands-on experience with modern orchestration and distributed computing frameworks such as Kubernetes and Ray.

• Experience with core ML frameworks such as PyTorch, TensorFlow, or JAX.

Preferred Qualifications

• Master's or PhD in Computer Science or a related technical field.

• Demonstrated Tech Lead experience, driving projects from conceptual design through to production deployment across cross-functional teams.

• Prior experience building infrastructure specifically for Generative AI models (e.g., diffusion models, GANs, or large language models) where cost and latency are critical.

• Proven background in building and operating large-scale data infrastructure (e.g., Ray, Apache Spark) to manage petabytes of multi-modal data (video, audio, text).

• Expertise in GPU acceleration and deep familiarity with low-level compute programming, including CUDA, NCCL, or similar technologies for efficient inter-GPU communication.

What HeyGen Offers

• Competitive salary and benefits package.
• Dynamic and inclusive work environment.
• Opportunities for professional growth and advancement.
• Collaborative culture that values innovation and creativity.
• Access to the latest technologies and tools.

HeyGen is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Position Summary:

At HeyGen, we are at the forefront of developing applications powered by our cutting-edge AI research. As a Data Infrastructure Engineer, you will lead the development of fundamental data systems and infrastructure. These systems are essential for powering our innovative applications, including Avatar IV, Photo Avatar, Instant Avatar, Interactive Avatar, and Video Translation. Your role will be crucial in enhancing the efficiency and scalability of these systems, which are vital to HeyGen's success.

Key Responsibilities:

• Design, build, and maintain the data infrastructure and systems needed to support our AI applications. Examples include
• Large scale data acquisition
• Multi-modal data processing framework and applications
• Storage and computation efficiency
• AI model evaluation and productionization infrastructure
• Collaborate with data scientists and machine learning engineers to understand their computational and data needs and provide efficient solutions.
• Stay up-to-date with the latest industry trends in data infrastructure technologies and advocate for best practices and continuous improvement.
• Assist in budget planning and management of cloud resources and other infrastructure expenses.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• Proven experience in managing infrastructure for large-scale AI or machine learning projects
• Excellent problem-solving skills and the ability to work independently or as part of a team.
• Proficiency in Python
• Experience optimizing computational workflows
• Familiarity with AI and machine learning frameworks like TensorFlow or PyTorch.

Preferred Qualifications:

• Experience with GPU computing
• Experience with distributed data processing system
• Experience building large scale batch inference system
• Prior experience in a startup or fast-paced tech environment.

What HeyGen Offers

• Competitive salary and benefits package.
• Dynamic and inclusive work environment.
• Opportunities for professional growth and advancement.
• Collaborative culture that values innovation and creativity.
• Access to the latest technologies and tools.

HeyGen is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.