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

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

About this role

As a Staff Machine Learning Platform Engineer, you will help design, improve, and operate a scalable ML platform to accelerate model training, deployment, and governance. You are the technical bridge between data science and production engineering.  You’ll be joining a small but deeply critical team that scales Faire’s ability to support tens of thousands of local businesses in a constantly narrowing retail landscape.

What You Will Do

• Design and operate ML infrastructure, including workspaces, clusters, jobs, and workflows
• Productionize ML workloads using Spark, Delta Lake, MLflow, and Databricks Workflows
• Teach data scientists how to utilize our ML platform to advance development from notebook to production for our most critical models
• Implement Unity Catalog for data governance, lineage, access control, and secure multi-tenant usage
• Build CI/CD pipelines for ML using Terraform and Git-based workflows (e.g., GitHub Actions)
• Optimize performance, reliability, and cost across training and inference workloads
• Configure Identity and Access Management (IAM) and Role Based Authentication Controls (RBAC) for sensitive data sets
• Establish observability for data quality, model performance, and platform health
• Build and maintain ML Platform technical documentation

What it takes

• 8+ years of experience building production ML or data platforms
• A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field
• Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow.
• Proficiency in Python, SQL, and distributed systems concepts
• Experience with cloud platforms and infrastructure-as-code
• Solid understanding of MLOps best practices: CI/CD, monitoring, reproducibility, and security
• Experience supporting multiple ML teams in a shared platform environment
• Are an active owner of orphaned problems and are willing to assimilate whatever knowledge you’re missing to get the job done

Tech Stack

Faire uses a modern cloud based tech stack.  For this role, you’ll want to be proficient with the following:

Salary Range

Canada: the pay range for this role is $216,000 to $297,000 per year. 

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

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.

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.

Upwork Inc.’s (Nasdaq: UPWK) family of companies connects businesses with global, AI-enabled talent across every contingent work type including freelance, fractional, and payrolled. This portfolio includes the Upwork Marketplace, which connects businesses with on-demand access to highly skilled talent across the globe, and Lifted, which provides a purpose-built solution for enterprise organizations to source, contract, manage, and pay talent across the full spectrum of contingent work. From Fortune 100 enterprises to entrepreneurs, businesses rely on Upwork Inc. to find and hire expert talent, leverage AI-powered work solutions, and drive business transformation. With access to professionals spanning more than 10,000 skills across AI & machine learning, software development, sales & marketing, customer support, finance & accounting, and more, the Upwork family of companies enables businesses of all sizes to scale, innovate, and transform their workforces for the age of AI and beyond.

Since its founding, Upwork Inc. has facilitated more than $30 billion in total transactions and services as it fulfills its purpose to create opportunity in every era of work. Learn more about the Upwork Marketplace at Upwork.com and follow us on LinkedIn, Facebook, Instagram, TikTok, and X; and learn more about Lifted at Go-Lifted and follow on LinkedIn.

Sr. Lead AI Research Scientist, AI Evaluation and Reliability

The AI Foundations team leads core research and development across the training, evaluation, and deployment of AI systems that power Uma, Upwork’s flagship AI model, and other customer-facing generative AI capabilities. As a Sr. Lead AI Research Scientist focused on AI Evaluation and Reliability, you will drive high-impact research initiatives that improve the trustworthiness, robustness, and real-world performance of AI systems operating at marketplace scale.

At the Sr. Lead level, this role combines deep technical expertise with cross-functional leadership. You will identify and lead research efforts that address systemic reliability challenges, partner closely with engineering and product teams to translate research into production outcomes, and help shape how Upwork evaluates AI performance in real work scenarios. Your work will support AI systems embedded in retrieval-based workflows, agentic architectures, and human plus AI collaboration patterns, while contributing to Upwork’s broader AI research strategy and external presence.

Responsibilities:

• Lead applied research initiatives focused on AI evaluation, reliability, and robustness, defining success metrics tied to customer impact and production readiness.

• Design and validate methods to measure and mitigate AI reliability risks, including uncertainty estimation, hallucination detection, and identification of model failure modes.

• Partner cross-functionally with engineering, data science, and product teams to integrate research outcomes into customer-facing AI systems and workflows.

• Own research projects end to end, from problem framing and hypothesis development through experimentation, prototyping, and synthesis of results.

• Influence technical direction across teams by surfacing insights, proposing scalable solutions, and aligning stakeholders on priorities and tradeoffs.

• Mentor researchers and engineers through technical guidance, feedback, and collaborative leadership on shared initiatives.

• Contribute to Upwork’s external research footprint through publications, presentations, and engagement with the broader AI research community.

What it takes to catch our eye:

• Proven experience leading applied AI research that balances scientific rigor with real-world deployment constraints and business impact.

• A strong record of research contribution through publications, internal innovation, or demonstrable influence on production AI systems.

• Deep proficiency with Python and modern deep learning frameworks such as PyTorch, with hands-on experience evaluating and improving large-scale models.

• An adaptive approach to integrating AI tools into research and development workflows to accelerate experimentation, improve evaluation quality, and share best practices with others.

• A collaborative, growth-oriented mindset with the ability to mentor peers, communicate complex ideas clearly, and thrive in a fast-evolving, bottom-up environment.
  
  

Come change how the world works.

Upwork is establishing an operational hub in Toronto, Canada. The new office is expected to be fully operational by Q4 2026. This role will require 3 days in office once we have an office open.

This position will initially be employed through a partner to ensure a seamless hiring process while we establish the hub. Once the hub is established, there may be opportunities to transition to employment with Upwork, depending on business needs and other requirements. While employed by the partner, you’ll work as part of Upwork’s team, with access to our resources, culture, and growth opportunities.

Our partner will offer competitive benefits. When Upwork’s hub is established, we will be excited to offer employment and benefits directly as business needs require. 

Upwork is committed to building a diverse, inclusive, and equitable workforce. Employment decisions are made without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or any other status protected by applicable law.

We use BrightHire, an AI-enabled tool, to record interviews and summarize interview transcripts. The tool allows the interviewer to focus on the discussion and does not score or evaluate candidates or make recommendations. The interview transcripts are reviewed, and decisions are only made by humans. Candidates who prefer not to have their interview recorded through BrightHire can opt out when the interview is scheduled.

To learn more about how Upwork processes and protects your personal information as part of the application process, please review our Global Job Applicant Privacy Notice and the Applicant Privacy Addendum (Canada).

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. 

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

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.

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 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:

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

The Inference ML Engineering team at Cerebras Systems is dedicated to enabling our fast generative inference solution through simple APIs powered by a distributed runtime that runs on large clusters of our own hardware. Our mission is to empower enterprises, developers, and researchers to unlock the full potential of our platform, leveraging its performance, scalability, and flexibility. The team works closely with cross-functional groups, including compiler developers, cluster orchestrators, ML scientists, cloud architects, and product teams, to deliver high-impact solutions that redefine the boundaries of ML performance and usability.

As a Senior Software Engineer on the Inference ML Engineering team, you will play a key role in designing and implementing APIs, ML features, and tools that enable running state-of-the-art generative AI models on our custom hardware. You will architect solutions that enable seamless model translation and execution, ensuring high throughput and low latency, while maintaining ease of use. Your responsibilities will include leading technical initiatives, collaborating with other engineering teams to enhance the developer experience, enabling key ML features at scale, maintaining our speed advantage, achieving high throughput, and supporting a wide range of ML workloads. This role offers an opportunity to shape the evolution of our ML ecosystem while tackling complex technical challenges at the intersection of machine learning, software, and hardware.

Responsibilities

• Drive and provide technical guidance to a team of software engineers working on complex machine learning integration projects.
• Design and implement ML features (e.g., structured outputs, biased sampling, predicted outputs) that improve performance of generative AI models at inference time.
• Design and implement high-throughput, low-latency multimodal inference models that support delivery of image, audio, and video inputs and outputs.
• Maintain our scalable serving backend for handling many concurrent requests per minute.
• Scale our inference service by implementing detailed observability throughout the entire stack.
• Analyze and improve latency, throughput, memory usage, and compute efficiency on the service and the implementation of various features.
• Optimize software to accelerate generative LLM inference by achieving high throughput and low latency.
• Stay up-to-date with advancements in machine learning and deep learning, and apply state-of-the-art techniques to enhance our solutions.
• Evaluate trade-offs between different approaches, clearly articulate design choices, and develop detailed proposals for implementing new features.
• Uncover, scope, and prioritize significant areas of technical debt across the software stack to ensure continued high quality of the inference service.
• Build and maintain robust automated test suites to ensure software quality, performance, and reliability.
• Contribute to an agile team environment by delivering high-quality software and adhering to agile development practices.
• Lead cross-functional initiative across the company to deliver high-quality inference solutions.

Skills and Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Mathematics, or a related field.
• 8+ years of experience in large-scale software engineering, with a focus on deep learning or related domains.
• Proficiency in Python for building and maintaining scalable systems.
• Advanced proficiency in C++, with an emphasis on multi-threaded programming, performance optimization, and system-level development.
• Demonstrated experience driving cross-functional projects.
• Experience building and scaling large-scale inference systems for LLMs or multimodal models.
• Familiarity with LLM serving frameworks, such as vLLM, SGLang, and TensorRT-LLM.
• Solid understanding of software architectural patterns for large-scale, high-performance applications.
• Hands-on experience with ML frameworks, such as PyTorch, and a strong understanding of their underlying architectures.
• Strong problem-solving skills, with the ability to balance technical depth with practical implementation constraints.
• Exceptional communication and presentation skills, with the ability to work both independently and collaboratively across multidisciplinary teams.

• Focus on CPU and memory subsystem optimizations for our Runtime software driver, enabling faster key cloud and ML training/inference workloads across modern x86 machines that form the backbone of our AI accelerator.
• Develop and enhance algorithms for efficient data movement, local data processing, job submission, and synchronization between various software and hardware components.
• Optimize our workloads using advanced CPU features like AVX instructions, prefetch mechanisms, and cache optimization techniques.
• Perform performance profiling and characterization using tools such as AMD uprof, and reduce OS level overheads.
• Influence the design of Cerebras' next-generation AI architectures and software stack by analyzing the integration of advanced CPU features and their impact on system performance and computational efficiency.
• Engage directly with the AI and ML developer community to understand their needs and solve contemporary challenges with innovative solutions.
• Collaborate with multiple teams within Cerebras, including architecture, research, and product management, to elevate our computational platform and influence future designs.

• BS, MS, or PhD in Computer Science, Computer Engineering, or a related field.
• 5+ years of relevant experience in performance engineering, particularly in optimizing algorithms and software design.
• Strong proficiency in C/C++ and familiarity with Python or other scripting languages.
• Demonstrated experience with memory subsystem optimizations and system-level performance tuning.
• Experience with distributed systems is highly desirable, as it is crucial to optimizing the performance of our Runtime software across multiple x86 hosts.
• Familiarity with compiler technologies (e.g., LLVM, MLIR) and with PyTorch and other ML frameworks.

About The Role

We are building the next generation of large-scale AI systems that power training and inference workloads at unprecedented scale and efficiency.

You will design and develop high-performance distributed software that orchestrates massive compute and data pipelines across heterogeneous clusters. Your work will push the limits of concurrency, throughput, and scalability—enabling efficient execution of models at massive scale. This role sits at the intersection of systems engineering and machine learning performance, demanding both architectural depth and low-level implementation skills. You will help shape how models are executed and optimized end-to-end, from data ingestion to distributed execution, across cutting-edge hardware platforms.

We’re hiring for runtime roles across both Training and Inference.

Responsibilities

• Design and implement distributed runtime components to efficiently manage large-scale execution workloads.
• Develop and optimize high-performance data and communication pipelines that fully utilize CPU, memory, storage, and network resources.
• Enable scalable execution across multiple compute nodes, ensuring high concurrency and minimal bottlenecks.
• Collaborate closely with ML and compiler teams to integrate new model architectures, training regimes, and hardware-specific optimizations.
• Diagnose and resolve complex performance issues across the software stack using profiling and instrumentation tools.
• Contribute to overall system design, architecture reviews, and roadmap planning for large-scale AI workloads.

Skills & Qualifications

• 3+ years of experience developing high-performance or distributed system software.
• Strong programming skills in C/C++, with expertise in multi-threading, memory management, and performance optimization.
• Experience with distributed systems, networking, or inter-process communication.
• Solid understanding of data structures, concurrency, and system-level resource management (CPU, I/O, and memory).
• Proven ability to debug, profile, and optimize code across scales—from threads to clusters.
• Bachelor’s, Master’s, or equivalent experience in Computer Science, Electrical Engineering, or related field.

Preferred Skills & Qualifications

• Familiarity with machine learning training or inference pipelines, especially distributed training and large-model scaling.
• Exposure to Python and PyTorch, particularly in the context of model training or performance tuning.
• Experience with compiler internals, custom hardware interfaces, or low-level protocol design.
• Prior work on high-performance clusters, HPC systems, or custom hardware/software co-design.
• Deep curiosity about how to unlock new levels of performance for large-scale AI workloads.

About The Team 

Cerebras builds wafer-scale AI processors—single chips delivering tens of PB/s of memory bandwidth and a dataflow architecture that accelerates at a granularity no multi-device system can match. The Advanced Technology Group (ATG) is Cerebras’ pathfinding organization. We work ahead of product to explore new architectures, demonstrate breakthrough performance on scientific and AI workloads, and shape the technical roadmap for future Cerebras hardware and software. Our work regularly appears at top-tier venues (Supercomputing, SIAM, IEEE, and NeurIPS) and directly influences the design of next-generation wafer-scale systems. 

About The Role 

Most AI research today is shaped by the constraints of existing hardware. This role starts from the other direction: what would you build if the architecture let you rethink the fundamentals? You will design and develop AI models and training methodologies on wafer-scale hardware, working at the level of optimization theory, model architecture, and statistical foundations rather than assembling existing components. 

The ATG sits at the intersection of AI, computational science, and computer architecture, and your work will draw on all three. You will collaborate closely with Cerebras’ ASIC, compiler, kernel, and AI teams as well as external partners at universities and national laboratories. 

What You Will Do 

• Design AI models and training methods from first principles, leveraging architectural properties of wafer-scale hardware that are unavailable on conventional platforms. 
• Investigate how techniques from computational science—numerical methods, PDE solvers, simulation—can inform and advance AI model design, and explore hybrid workflows that couple simulation and learning. 
• Develop a deep understanding of the hardware substrate and use it to guide algorithmic choices: model structure, optimization strategy, memory access patterns, numerical precision. 
• Publish findings and present at top-tier venues (NeurIPS, ICML, ICLR, etc.); represent Cerebras in the broader AI/ML research community. 
• Inform the design of future Cerebras hardware and software by identifying the computational patterns that matter most for next-generation AI workloads. 

What We Are Looking For 

• PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field preferred; exceptional candidates without a graduate degree who demonstrate equivalent depth through published research, significant open-source contributions, or a strong industry track record are encouraged to apply. 
• Mathematical maturity: comfort with the theory behind gradient methods, loss landscapes, generalization, and the relationship between model structure and data statistics. 
• Track record of published research at top-tier AI or computational science venues. 
• Proficiency in Python and PyTorch; comfort with C or other low-level languages is a strong signal. 
• Excellent communication and interpersonal skills: able to present complex technical material to both ML and systems audiences, and to collaborate effectively in a fast-paced, small-team environment. 

Why This Opportunity Is Exciting And Unique 

• You will have direct access to hardware that changes what’s algorithmically possible. Tens of PB/s of memory bandwidth and fine-grained dataflow execution open design spaces that don’t exist on GPU clusters. 
• You will work alongside researchers in computational science, computer architecture, and performance engineering. The synthesis across these fields is central to ATG’s approach. 
• Your research will influence silicon - ATG’s findings directly shape the design of future Cerebras chips and systems. 

We are hiring for multiple positions across experience levels. If this work resonates, we encourage you to apply. 

About The Role

As an Applied Machine Learning Research Scientist at Cerebras, you will play a key role in turning modern machine learning techniques into scalable, high-performance systems. This role sits at the intersection of modeling and systems focused not on publishing new algorithms, but on understanding how they work and making them run effectively at scale. Your work will directly impact how large language models (LLMs) are trained, optimized, and deployed on one of the most advanced AI platforms in the world. 

You will work closely with researchers and senior engineers to implement and improve workflows for LLM pretraining, fine-tuning, and reinforcement learning-based post-training. This includes building training pipelines, debugging complex system behaviors, improving model quality, and iterating on data and evaluation strategies. Your contributions will help translate cutting-edge ML ideas into reliable, production-ready systems that solve real-world problems. 

This role is ideal for candidates who enjoy hands-on engineering, want to build deep intuition for ML systems, and are excited about working on LLMs and reinforcement learning in practice, not just in theory. 

Responsibilities 

• Apply post-training techniques (e.g. RLVR, RLHF, GRPO etc.) techniques to improve model performance.
• Build and maintain evaluation pipelines to measure model performance across tasks and domains.
• Debug issues across the ML stack, including data pipelines, training jobs, model outputs and mixed or lower precision computation.
• Collaborate with researchers to translate ML ideas into efficient, scalable implementation.
• Design, implement, and scale ML pipelines across all stages of LLM development (pretraining, fine-tuning, alignment).
• Work with large datasets, including dataset generation, filtering, and synthetic data approaches.
• Optimize training and inference workflows for performance, efficiency, and reliability.
• Contribute high-quality, maintainable code to shared ML infrastructure. 

Skills & Qualifications    

•  Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 0 - 5 years of experience (including internships, research, or industry experience) working with machine learning systems; we are hiring multiple positions for various levels. 
• Strong programming skills in Python.
• Experience with ML frameworks such as PyTorch.
• Solid understanding of machine learning fundamentals.
• Familiarity with deep learning architectures, particularly transformers.
• Ability to read and understand modern ML papers and implement key ideas.

Preferred Skills & Qualifications 

• Experience working with large language models (training, fine-tuning, and evaluation).
• Familiarity with reinforcement learning concepts.
• Experience with distributed training frameworks (e.g., FSDP, Megatron).
• Experience working with large-scale datasets and data pipelines.
• Experience debugging or optimizing ML systems for performance.
  • Contributions to meaningful codebases, projects, or open-source systems 

About the Role

The Inference ML Engineering team at Cerebras builds the runtime, APIs, and systems that power the fastest generative AI inference platform in the world.

As an Engineering Manager, Inference ML Runtime, you will lead a team responsible for designing and scaling the systems that enable seamless execution of state-of-the-art AI models on Cerebras hardware. You will operate at the intersection of machine learning, distributed systems, and high-performance runtime engineering, translating cutting-edge research into production-ready infrastructure to serve a variety of text-only and multimodal models.

This role combines technical leadership, people management, and execution ownership, with direct impact on Cerebras’ core inference platform.

What You’ll Do

Technical Leadership

• Own the architecture and evolution of the ML inference runtime and serving systems.
• Guide the design of:
• high-throughput, low-latency inference pipelines;
• multimodal model execution (text, image, audio, video);
• scalable serving infrastructure for concurrent workloads.
• Partner with cloud, compiler, core runtime, hardware, and ML teams to optimize end-to-end performance.

Team Leadership

• Build, manage, and grow a team of ML systems and infrastructure engineers.
• Provide technical direction, mentorship, and career development.
• Foster a culture of ownership, velocity, and engineering excellence.
• Recruit top talent in ML systems, distributed systems, and runtime engineering.

Execution & Delivery

• Drive execution of complex, cross-functional initiatives across:
• ML engineering;
• compiler/runtime teams;
• cloud and infrastructure teams.
• Own delivery of features such as:
• advanced inference capabilities (structured outputs, sampling strategies);
• heterogeneous model types, including test and multimodal;
• performance optimization (latency, throughput, memory efficiency);
• observability and reliability across the inference stack.
• Ensure high-quality releases through strong testing, validation, and operational rigor.

Platform & Performance Ownership

• Scale Cerebras’ inference platform to handle large volumes of concurrent requests at very fast speed
• Drive improvements in:
• latency;
• throughput;
• compute efficiency.
• Identify and prioritize technical debt and system bottlenecks.
• Maintain Cerebras’ industry-leading inference speed advantage.

Cross-Functional Collaboration

• Partner with:
• ML researchers (model enablement);
• compiler teams (model execution optimization);
• cloud/platform teams (deployment and scaling).
• Act as a bridge between research, infrastructure, and production systems.

What You Bring

Required

• 8+ years of experience in:
• large-scale software engineering;
• ML systems or distributed systems.
• 2+ years of engineering management experience.
• Strong programming skills in:
• Python (production systems);
• C++ (performance-critical systems).
• Experience building and scaling large-scale inference systems (LLMs or multimodal).
• Experience working with cloud infrastructures and following best-practices for building scalable microservices and applications.

Preferred

• Experience with:
• LLM serving frameworks (e.g., vLLM, TensorRT-LLM, SGLang);
• PyTorch and deep learning frameworks;
• distributed systems and high-performance computing.
• Familiarity with:
• ML runtime systems;
• model execution pipelines;
• performance optimization for AI workloads.

Why This Role Matters

This team is central to Cerebras’ mission of delivering the fastest AI inference in the world. Your work will directly enable real-time AI applications and unlock new capabilities across enterprise and frontier AI use cases.

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

About this Role

The Ads Data team is building the next generation of advertising products for the wholesale industry. As a key member of this team, you’ll help drive the ML algorithm strategy and system design behind one of the most critical levers for customer value and company growth—Search Ads. You’ll lead the advancement of real-time systems that decide which ads to show for a query, where to place them, and how to optimize for relevance, marketplace health, and advertiser outcomes. This role mirrors many of the technical expectations of Faire’s organic Search roles (modern NLP/LLMs, query understanding, real-time ranking), while operating in an ads environment with auctions, budgets, and pacing constraints.

You’ll operate at the forefront of algorithms—combining large language models, natural language processing, query understanding, deep learning, and structured behavioral data to deliver highly relevant sponsored results for any given query.

What You'll Do

• Own and evolve the Search Ads relevance stack—spanning query understanding, targeting, candidate generation, multi-stage ranking, and calibration—while meeting stringent latency and reliability goals.
• Design and productionize ML models that improve sponsored-result relevance and personalization, using a blend of unstructured signals (text/images where applicable), LLM-based representations, and structured marketplace features.
• Partner closely with engineering and product to connect relevance improvements to ads marketplace outcomes (e.g., conversion, long-term retailer experience, advertiser ROI), using rigorous offline evaluation and online experiments.
• As an early member of the Ads Data team, help define its roadmap and technical culture, leveraging deep product intuition to shape what ads at Faire should be—not just how they’re built.
• Work in a fast-paced, collaborative environment with team members who’ve shipped ML at top tech companies (e.g. Uber, Airbnb, Meta, Amazon, Pinterest).

Qualifications

• 4+ years of experience building and shipping ML systems in production, with meaningful experience in search, recommendation, or ads ranking/retrieval.
• Hands-on experience with modern deep learning tooling (e.g., PyTorch) and familiarity with vector search / embedding-based retrieval concepts (e.g., Faiss, ScaNN, Pinecone).
• A strong track record of productionizing models that blend LLMs (e.g., BERT / GPT-class) with structured features to drive relevance and personalization.
• Product-focused mindset and a bias toward execution—moving quickly from research to measurable user and business impact.
• Strong communication skills and the ability to work with others in a closely collaborative team environment

Great to Haves

• Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields
• Ability to quickly implement state of the art algorithms from an academic paper

Salary Range

San Francisco: the pay range for this role is $196,000 to $269,500 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

About this Role

The Ads Data team is building the next generation of advertising products for the wholesale industry. As a key member of this team, you’ll help drive the ML algorithm strategy and system design behind one of the most critical levers for customer value and company growth—Search Ads. You’ll lead the advancement of real-time systems that decide which ads to show for a query, where to place them, and how to optimize for relevance, marketplace health, and advertiser outcomes. This role mirrors many of the technical expectations of Faire’s organic Search roles (modern NLP/LLMs, query understanding, real-time ranking), while operating in an ads environment with auctions, budgets, and pacing constraints.

You’ll operate at the forefront of algorithms—combining large language models, natural language processing, query understanding, deep learning, and structured behavioral data to deliver highly relevant sponsored results for any given query.

What You'll Do

• Own and evolve the Search Ads relevance stack—spanning query understanding, targeting, candidate generation, multi-stage ranking, and calibration—while meeting stringent latency and reliability goals.
• Design and productionize ML models that improve sponsored-result relevance and personalization, using a blend of unstructured signals (text/images where applicable), LLM-based representations, and structured marketplace features.
• Partner closely with engineering and product to connect relevance improvements to ads marketplace outcomes (e.g., conversion, long-term retailer experience, advertiser ROI), using rigorous offline evaluation and online experiments.
• As an early member of the Ads Data team, help define its roadmap and technical culture, leveraging deep product intuition to shape what ads at Faire should be—not just how they’re built.
• Work in a fast-paced, collaborative environment with team members who’ve shipped ML at top tech companies (e.g. Uber, Airbnb, Meta, Amazon, Pinterest).

Qualifications

• 2+ years of experience building and shipping ML systems in production, with meaningful experience in search, recommendation, or ads ranking/retrieval.
• Hands-on experience with modern deep learning tooling (e.g., PyTorch) and familiarity with vector search / embedding-based retrieval concepts (e.g., Faiss, ScaNN, Pinecone).
• A strong track record of productionizing models that blend LLMs (e.g., BERT / GPT-class) with structured features to drive relevance and personalization.
• Product-focused mindset and a bias toward execution—moving quickly from research to measurable user and business impact.
• Strong communication skills and the ability to work with others in a closely collaborative team environment

Great to Haves

• Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields
• Ability to quickly implement state of the art algorithms from an academic paper

Salary Range

San Francisco: the pay range for this role is $165,500 to $227,500 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

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.

About this role

As a Senior Applied AI/ML Scientist on the Search ranking team, you will help shape the technical vision, machine-learning algorithm strategy, and system design behind one of our most important growth levers: Search (the primary tool used by customers on any e-commerce site). You will advance real-time search and recommendation systems that power next-generation shopping experiences.

You’ll work at the frontier of algorithms, combining query understanding, deep learning, transformer-based sequential modeling, graph neural networks, and structured behavioral data to return hyper-relevant, personalized products and brands for every user query.

This is a rare chance to influence the end-to-end personalized discovery experience at Faire within a high-scale, deeply multi-modal environment, while collaborating closely with a talented team of scientists and engineers.

What you'll do

• Build our next-generation Search ranking algorithms by integrating the latest advances in deep learning and machine learning to personalize the retailer discovery journey at Faire
• Leverage LLM to extract multimodal signals (text, visual) to better profile users and their intents. 
• Partner closely with teams across Faire to experiment and improve the ML models for search ranking and beyond.
• Design and productionize natural-language search and discovery systems so that intelligent agents can generate relevant and personalized collections, explain search results, and assist retailers with browsing, filtering, and evaluation.
• Share best practices regarding deep learning model development, agent-workflow evaluation, and MLOps, and help teammates level up through code reviews and technical guidance.

You're a great fit if you have...

• 5+ years of industry experience building large-scale ML models with business impact and shipping ML solutions to production, including 3+ years in search, recommendation, or ads ranking
• A Master’s or PhD in Computer Science, Statistics, or a related STEM field.
• Strong programming skills (Python, Java, or equivalent) and hands-on experience with deep-learning libraries (e.g., PyTorch) and big data technologies (e.g., Spark).
• Deep understanding of machine learning best practices (e.g., training/serving, imbalanced data, A/B testing, feature engineering, and feature/model selection) and algorithms (e.g., user modeling, deep learning, and reinforcement learning) with applications in search, recommendation, and advertising domains.
• A product-focused mindset and a bias toward execution—moving quickly from research papers to prototypes and production.
• Excellent written and verbal communication skills and strong cross-functional influence that raise the technical bar beyond your immediate team.

Bonus points for...

• Contributions to open-source ML libraries or peer-reviewed publications in ML/AI.
• Industry experience developing and productizing LLM-based applications and systems in the search domain.
• Industry experience building search and recommendation systems for e-commerce or two-sided marketplaces.
• Experience using AI tools (e.g., Cursor, Claude Code, Codex) for code development and daily productivity.
• Familiarity with Kotlin

Salary Range

California: the pay range for this role is $192,000 to $264,000 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

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.

Driving Product Innovation with Advanced Computer Vision Technology

HeyGen is a dynamic startup at the forefront of revolutionizing video content with state-of-the-art artificial intelligence technology. With a robust track record of growth and a proven business model, we are rapidly carving out a leadership position in the AI-powered video creation industry. We are in search of passionate Research Scientists with a specialization in Computer Vision to propel our mission forward. If you are eager to deploy your advanced skills to disrupt and redefine the video creation and interaction experience, your next big opportunity awaits with us.

Key Responsibilities:

• Applied Innovation and Product Development: Harness your knowledge of generative models, including GANs, Diffusion models, and other multi-modal models, to propel our video creation tools forward. Your role involves translating state-of-the-art research into innovative features that directly enhance our user experience and set our products apart.

• Real-World Solutions: Channel your research capabilities to develop scalable solutions that have a significant impact on our platform, making the video creation process more intuitive, powerful, and accessible to our users.

• Collaborative Engineering and Growth: Engage with a diverse team where collaborative efforts lead to extraordinary outcomes. Embrace an environment that promotes learning, mentorship, and teamwork, furthering both personal development and the advancement of our technology.

Qualifications:

• A Master's or PhD in Computer Science, Engineering, or a related field
• 1-3 years of experience in machine learning and computer vision, demonstrating practical application of generative models and a passion for product-centric innovation.
• Proficiency in Python, with openness to other programming languages and tools as required by project demands.
• Experience with deep learning frameworks such as PyTorch, and a capacity for quickly mastering new technologies.
• A collaborative spirit with a proven track record in a team-based research or development setting.
• A lifelong learner with outstanding problem-solving skills and the ability to communicate effectively in a diverse team environment.

What HeyGen Offers:

• Competitive Salary and Benefits: We offer a salary commensurate with your experience and a comprehensive benefits package that addresses a variety of needs.
• Innovative and Inclusive Work Environment: Our workplace is designed to inspire innovation through collaboration, respect, and mutual support.
• Opportunities for Advancement: We are committed to your professional development, offering extensive opportunities for learning, mentorship, and career growth.
• Dedication to Diversity: HeyGen is an equal opportunity employer, championing a diverse and inclusive team that ensures equal access to opportunities for all members.