MaintainX is the world's leading AI-powered maintenance and asset management platform, serving 13,000+ customers including Duracell, Shell, Cintas, and Brenntag. We raised $150M in Series D funding led by Bessemer Venture Partners and Bain Capital Ventures, bringing our total funding to $254M. We were named to the Forbes 2025 Cloud 100, the definitive ranking of the top 100 private cloud companies in the world. We're growing fast and hiring the engineering talent to match.

We're looking for a Senior Software Engineer to own the constraint-solving engine behind our Scheduling Agent, one of the most strategic bets on our Planning & Scheduling roadmap. The engine is a Python service built on CP-SAT that takes work orders, technicians, and a mix of hard and soft constraints, and produces a feasible weekly schedule that enterprise maintenance teams can trust. Your focus will be evolving the solver to handle increasingly complex, real-world scheduling scenarios and exposing it as tools for GenAI agent workflows.

This is a high-ownership role. You'll have the space to shape the modeling approach, partner closely with product and design on what scheduler users actually need, and ship iteratively against feedback from real enterprise customers.

What you'll do

• Own and evolve the Python optimization service that powers the Scheduling Agent, modeling, solving, and iterating on the constraint formulation as new use cases emerge.
• Design and implement increasingly sophisticated scheduling capabilities: trade and crew constraints, irregular capacity patterns, production downtime windows, multi-site considerations, and reactive re-scheduling.
• Build and maintain API routes and tools that expose the solver to GenAI agent workflows (tool calling, structured input/output).
• Partner with PM and design to translate messy real-world scheduling problems into solver constraints, and push back when "optimal" isn't what users actually want.
• Iterate the solver with real users via design partnerships and pilot deployments. Take feedback from human schedulers seriously and reflect it back into the model.
• Contribute to the surrounding Python service: performance, observability, testing, and reliability of the optimization runtime.
• Help shape how scheduling intelligence integrates with the broader MaintainX product over time, including learning from execution data to improve solver inputs.

About you

• 5+ years of professional software engineering experience, with significant time spent on optimization, constraint programming, or operations research problems shipped to real users.
• Strong fluency with CP-SAT and at least one other optimization paradigm (MILP via Gurobi/CPLEX/HiGHS, metaheuristics, or similar). You've hit the limits of one approach and made informed choices about when to use which.
• Solid Python service engineering: APIs, async, testing, profiling, observability. You can own a production service end-to-end.
• Academic grounding in Operations Research, Industrial Engineering, Computer Science, or a related quantitative field, at minimum a strong undergraduate foundation; advanced degrees are common in this space but not required.
• Track record of iterating optimization systems with real users, you've felt what happens when a human rejects the "optimal" answer and you've redesigned the model in response.
• Product mindset and delivery orientation, you ship, you measure, you iterate. You think about the user outcome, not just the objective function.
• Comfort with ambiguity. You can co-design the constraint data model with the team rather than waiting for a clean spec.
• Familiarity with GenAI tooling (LLM tool calling, structured output, prompt design for constrained generation) is expected.

Nice to have

• Experience at a known, reputable product company shipping optimization or scheduling products at scale.
• Exposure to learning-augmented optimization, using historical execution data to estimate durations, priors, or constraint weights.
• Domain experience in scheduling, workforce management, field service, manufacturing, logistics, or similar resource-constrained planning problems.
• Tech-lead experience or interest in growing into a tech-lead role on this team.

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 Work Culture where:
• You'll work alongside folks across the globe that reflect the MaintainX values: Smart Humble Optimists.
• We believe in meritocracy, where ideas and effort are publicly celebrated.

About us

Our mission is to deliver one platform for maintenance, repair & operations teams to keep the physical world running. We believe the greatest asset in any organization is the people. That's why we built an intuitive, mobile-first solution to help boost productivity and collaboration across teams and locations.

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.

#LI-Remote

We are seeking a machine learning (ML) developer (applied research) to join our team working on the Scientist AI. In this role, you will work closely with ML research scientists and product stakeholders to start applying the Scientist AI to real-world problems.

Key responsibilities

• Accelerate applied research, model training and inference, and iterate on implementing models for real-world applications (that will form the basis of LawZero’s future solutions offerings).
• Design and implement workflows for research experiments across simulated environments and real-world applications.
• Develop datasets, tooling, dashboards, and libraries to adapt, monitor, interpret, and evaluate models in the context of real-world applications.
• Establish, document, and maintain best practices for ML model development workflows.
• Redesign and adapt research ideas and prototypes into robust production-grade tools and solutions.
• Deeply understand customer use-cases to inform training strategies and surface edge cases.
  
  

Skills and qualifications

• A degree in a relevant computer science field (e.g., computer science, computer engineering, software engineering) is required, along with an advanced degree (MSc or higher) in machine learning or equivalent work experience.
• 3+ years of industry experience in designing and implementing complex machine learning workflows on high performance computing devices using PyTorch, TensorFlow, or JAX.
• Ability to collaborate effectively with cross-functional teams, document best practices, and stay updated with the latest advancements in ML and software development.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and workload managers (e.g., Ray, SLURM)
• Familiarity with containerization tools (e.g., gRPC, Docker, Kubernetes).
• Familiarity with data infrastructures and platforms (e.g., vector databases).
• Experience building new ML products from scratch, or based on research prototypes.
• Experience working alongside researchers is a bonus.

What we offer

• The opportunity to contribute to a unique mission with a major impact.
• Comprehensive health benefits.
• A minimum of 20 days vacation per year upon start.
• A minimum retirement savings employer contribution of 4%.
• Generous flexible benefits designed to contribute to your well-being.
• A team of passionate experts in their field.
• A collaborative and inclusive work environment with offices in the heart of Little Italy, in the trendy Mile-Ex district, close to public transportation.

We are seeking an AI Safety Researcher with an ML focus, to join our team working on the Scientist AI agenda. In this role, you will contribute to our plan using your expertise in AI safety and your knowledge and experience of machine learning.

Key responsibilities

• Help clarify which AI safety research questions are central to the Scientist AI agenda.
• Analyze proposed solutions to these research questions and develop novel solution ideas.
• Propose, design, and implement novel ML algorithms and experiments aimed at solving or testing complex AI safety problems.
• Scrutinize the Scientist AI agenda and its fit into a bigger strategic picture for mitigating AI risk, in order to guide and prioritize experiments to answer critical safety questions efficiently.

Required skills and qualifications

• PhD in a relevant field is preferred but not required if the candidate demonstrates exceptional abilities or equivalent experience.
• Research experience in technical AI safety, ideally with a focus on AGI safety.
• Experience proposing, designing and running ML experiments.
• Proficiency with ML frameworks such as PyTorch or Tensorflow.
• Excitement to contribute to and shape the Scientist AI agenda. 
• Ability to work in a collaborative environment and contribute to collective research goals.
• Ability to discuss complex findings with team members from diverse technical backgrounds.

Preferred skills and qualifications

• Experience in some of the following AI safety research areas: agent foundations, AI control, frontier AI safety, eliciting latent knowledge, externalised reasoning, ambitious alignment schemes.
• Machine learning research publications in top-tier venues (e.g., NeurIPS, ICML, ICLR, etc.).
• Experience fine tuning and post training language models.

What we offer

• The opportunity to contribute to a unique mission with a major impact
• Comprehensive health benefits
• A minimum of 20 days vacation per year upon start
• A minimum retirement savings employer contribution of 4%
• Generous flexible benefits designed to contribute to your well-being
• A team of passionate experts in their field
• A collaborative and inclusive work environment with offices in the heart of Little Italy, in the trendy Mile-Ex district, close to public transportation

We are seeking a Machine Learning (ML) Research Scientist to join our team working on a novel AI safety research agenda. In this role, you will develop and evaluate probabilistic inference methods, with a focus on amortized inference, translating theoretical insights into practical implementations.

Key responsibilities

• Develop amortized inference methods suitable for high-dimensional discrete and continuous distributions.
• Develop parameter- and structure-learning methods for large probabilistic graphical models that benefit from amortized probabilistic inference.
• Design evaluation strategies for methods that rely on probabilistic inference.
• Collaborate with mathematicians on theory related to learning and inference in probabilistic models.
• Translate theoretical proposals into high quality implementations in a programming language such as Python.
• Analyze and interpret experimental results to steer future research directions.
• Communicate complex findings effectively to various stakeholders.
  

Skills and qualifications

• Advanced degree in a relevant field (e.g., Computer Science, Mathematics). A PhD is preferred but not required if the candidate demonstrates exceptional abilities.
• A minimum of 3 years of experience in deep learning research.
• Expertise in probabilistic inference is required, in addition to expertise in one or more of the following:
  • Bayesian inference
  • Sampling-based approximate inference methods
  • Amortized inference methods (including variational inference methods or Generative Flow Networks)
  • Parameter- and/or structure-learning in probabilistic graphical models (including causal models)
  • Reinforcement learning
  • Optimal control
• Strong background in mathematics.
• Proven experience in developing and implementing machine learning models.
• Proficiency in programming languages such as Python, and experience with  ML frameworks like PyTorch or TensorFlow.
• Excellent analytical and problem-solving skills, with a demonstrated ability to think critically about complex systems.
• Strong communication skills, both written and verbal, with the ability to explain complex ideas to diverse audiences.
• Track record of contributing to high-quality research in probabilistic inference or related fields.
• Ability to work collaboratively in a team environment while also being self-motivated and independent.

What we offer

• The opportunity to contribute to a unique mission with a major impact.
• Comprehensive health benefits.
• A minimum of 20 days vacation per year upon start.
• A minimum retirement savings employer contribution of 4%.
• Generous flexible benefits designed to contribute to your well-being.
• A team of passionate experts in their field.
• A collaborative and inclusive work environment with offices in the heart of Little Italy, in the trendy Mile-Ex district, close to public transportation.

We are seeking a Machine Learning (ML) Research Scientist to join our team working on a novel AI safety agenda. In this role, you will design and implement innovative ML models aimed at solving advanced AI safety problems.

Key responsibilities

• Propose, design, and implement novel ML models tailored to solve complex AI safety problems.
• Collaborate with mathematicians and other specialized research scientists to integrate theoretical advancements into practical ML algorithms and models.
• Adapt and fine-tune existing frontier models to enhance their applicability to specific settings.
• Design and implement experimental protocols and evaluation frameworks to validate hypotheses and ensure robust and reproducible results, including at the scale and complexity of frontier models.
• Analyze and interpret experimental data in order to steer future research questions and objectives.
• Benchmark and optimize model performance and efficiency along with ML engineers to ensure the optimal usage of computing resources over the course of long experiments.
• Communicate findings and synchronize work with other researchers and engineers.

Required skills and qualifications

• Advanced degree in a relevant field (e.g., Computer Science, Mathematics). A PhD is preferred but not required if the candidate demonstrates exceptional abilities.
• 3-5 years of experience in deep learning research projects, in particular with frontier models.
• Proven experience in the training, adaptation, and/or fine-tuning of frontier models across a variety of complex scenarios that involve, for example, transfer learning, domain adaptation, or meta learning.
• Expertise in the integration and use of ML libraries such as PyTorch, TensorFlow, or JAX for the development, training, and evaluation of ML models, ideally in distributed computing environments.
• Strong experience in Python software development, in the use of version control, collaboration tools, and experiment management and tracking tools.
• Strong communication skills, both written and verbal, with the ability to exchange ideas with researchers across different disciplines.
• Ability to work collaboratively in a team environment while also being self-motivated and independent.

Preferred skills and qualifications

• Experience analyzing the behavior of frontier models in safety-critical contexts.
• Experience in the design and development of model alignment strategies or benchmarks.
• Experience with natural language processing or probabilistic graphical models.
• A track record of contributing to high-quality research projects in deep learning.

What we offer

• The opportunity to contribute to a unique mission with a major impact.
• Comprehensive health benefits.
• A minimum of 20 days vacation per year upon start.
• A minimum retirement savings employer contribution of 4%.
• Generous flexible benefits designed to contribute to your well-being.
• A team of passionate experts in their field.
• A collaborative and inclusive work environment with offices in the heart of Little Italy, in the trendy Mile-Ex district, close to public transportation.

Shape the future of enterprise AI technology

Do you want to turn cutting-edge natural language processing into products impacting millions of users?

As a Senior II Applied Scientist on Coveo’s Knowledge AI team, you will help build the language technologies at the core of our generative artificial intelligence platform.

You’ll take on a technical leadership role, influencing our NLP vision while designing, evaluating, and deploying advanced solutions such as retrieval augmented generation pipelines, information retrieval, semantic search, question answering, and agentic systems, always with real-world constraints in mind.

As one of our Senior II Applied Scientists, you will:

• Design, develop, and optimize modern language-based artificial intelligence solutions, from data and modeling choices to evaluation strategies and production constraints.
• Apply and serve transformer-based language models at scale, delivering reliable, real-time experiences to enterprise users.
• Collaborate closely with machine learning developers and the machine learning platform team to accelerate research and innovation cycles.
• Act as a technical reference within the team, guiding architectural decisions and promoting best practices in applied science.
• Mentor applied scientists, supporting their growth and helping raise the overall level of expertise within the group.
• Contribute to research and development processes that foster efficiency, rigor, and a culture of applied science excellence.
• Share our innovations externally through technical blog posts, presentations, or other thought leadership content.

Here is what will qualify you for the role:

• A PhD, or a Master’s degree with equivalent research experience, in a relevant field such as machine learning, computer science, mathematics, or physics.
• At least 8 years of industry experience applying modern natural language processing techniques, including deep learning and large language models, to real-world systems at scale.
• Strong experience designing models and systems that balance performance, user experience, and production realities.
• Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.

What will make you stand out:

• Experience in a technical leadership capacity, including mentoring, hiring, roadmap definition, or scientific project planning.
• Research or applied experience in areas closely related to Coveo’s business, such as information retrieval, neural search, semantic search, or question answering.
• Exposure to more traditional lexical or linguistics-based natural language processing techniques.
• International work experience and a demonstrated ability to collaborate across cultures and perspectives.

Do you think you can bring this role to life? Or add your own color?
You don’t need to check every single box; passion goes a long way and we appreciate that skillsets are transferable.

Send us your application, we want to hear from you!

Join the Coveolife!

We encourage all qualified candidates to apply regardless of, for example, age, gender, disability, gaps in CV, national or ethnic background.
Coveo is committed to providing accessible employment practices. If you require accommodation due to a disability at any point during the recruitment process, please contact HR@Coveo.com to discuss your needs.

Shape the future of enterprise AI technology

Do you want to turn cutting-edge natural language processing into products impacting millions of users?

As a Senior II Applied Scientist on Coveo’s Knowledge AI team, you will help build the language technologies at the core of our generative artificial intelligence platform.

You’ll take on a technical leadership role, influencing our NLP vision while designing, evaluating, and deploying advanced solutions such as retrieval augmented generation pipelines, information retrieval, semantic search, question answering, and agentic systems, always with real-world constraints in mind.

As one of our Senior II Applied Scientists, you will:

• Design, develop, and optimize modern language-based artificial intelligence solutions, from data and modeling choices to evaluation strategies and production constraints.
• Apply and serve transformer-based language models at scale, delivering reliable, real-time experiences to enterprise users.
• Collaborate closely with machine learning developers and the machine learning platform team to accelerate research and innovation cycles.
• Act as a technical reference within the team, guiding architectural decisions and promoting best practices in applied science.
• Mentor applied scientists, supporting their growth and helping raise the overall level of expertise within the group.
• Contribute to research and development processes that foster efficiency, rigor, and a culture of applied science excellence.
• Share our innovations externally through technical blog posts, presentations, or other thought leadership content.

Here is what will qualify you for the role:

• A PhD, or a Master’s degree with equivalent research experience, in a relevant field such as machine learning, computer science, mathematics, or physics.
• At least 8 years of industry experience applying modern natural language processing techniques, including deep learning and large language models, to real-world systems at scale.
• Strong experience designing models and systems that balance performance, user experience, and production realities.
• Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.

What will make you stand out:

• Experience in a technical leadership capacity, including mentoring, hiring, roadmap definition, or scientific project planning.
• Research or applied experience in areas closely related to Coveo’s business, such as information retrieval, neural search, semantic search, or question answering.
• Exposure to more traditional lexical or linguistics-based natural language processing techniques.
• International work experience and a demonstrated ability to collaborate across cultures and perspectives.

Do you think you can bring this role to life? Or add your own color?
You don’t need to check every single box; passion goes a long way and we appreciate that skillsets are transferable.

Send us your application, we want to hear from you!

Join the Coveolife!

We encourage all qualified candidates to apply regardless of, for example, age, gender, disability, gaps in CV, national or ethnic background.
Coveo is committed to providing accessible employment practices. If you require accommodation due to a disability at any point during the recruitment process, please contact HR@Coveo.com to discuss your needs.

The Role

This is a full-ownership data engineering role at the center of Medeloop's AI platform. You won't be maintaining pipelines someone else built,  you'll be architecting the data backbone that powers AI agents doing real operations at scale. You'll work directly with data scientists, AI engineers, and product teams to turn raw, complex healthcare data into the clean, structured, semantically-rich foundation our AI scientists depend on. Your work shows up in customer products and research outcomes, not internal dashboards that no one reads.Candidates who currently perform these tasks exclusively through manual processes are unlikely to be suitable for this role. We require an individual who has already adopted and integrated AI techniques to enhance operational velocity, rather than one who is contemplating future experimentation.If you want to build something that genuinely changes how medical research gets done, this is the role.

What You'll Own

• The healthcare data lake: curating, extending, and evolving it through new concepts, derived variables, and data models that directly inform our AI engines and customer products
• AI-native data workflows: designing and operating AI-powered pipelines (using tools like Claude Code and agent frameworks) to automate harmonization, cleaning, quality checks, and summarization at scale
• NLP and semantic infrastructure: building pipelines for entity extraction, concept normalization, embedding-based retrieval, and semantic search that power the AI Scientist platform
• Novel data extraction approaches: experimenting with and building new methodologies for working with unstructured clinical data, not just applying existing playbooks
• Research-grade data products: delivering analytical samples, cohorts, and final datasets that withstand scientific scrutiny and are actively used by researchers and customers
• Data governance and observability protocols: including access controls, PHI/PII handling, data classification, compliance, monitoring, alerting, data freshness, and comprehensive documentation to enable self-service capabilities.

What We're Looking For

• 3+ years of relevant data engineering or data management within an analytics-driven organization, with end-to-end ownership from raw ingestion to final data product
• Deep hands-on experience with healthcare CDMs (OMOP, FHIR, PCORnet) — designing or extending them, not just querying
• Knowledge of medical ontologies: UMLS, SNOMED CT, RxNorm
• Experience with big data, data pipelines and tooling that support retrieval-augmented generation (RAG), vector integrations, embedding workflows, and other AI/ML workloads. Experience in big data tooling such as Spark, Iceberg, EMR
• Fluent in Python and SQL; comfortable across structured and unstructured data
• Proven NLP experience: semantic search, entity recognition, concept normalization, embedding pipelines
• Strong grasp of inferential statistics and cohort methodology to be a real partner to data scientists and customers (as part of onboarding)
• Experience contributing to an AI/ML product, especially in automated research or scientific discovery
• Experience mentoring other engineers and providing technical leadership

Bonus Points

• Multi-cloud experience (AWS, Azure, GCP)
• Authorship or contribution to peer-reviewed publications or technical reports

Why Medeloop

• Ownership from day one: small team, high-trust, no layers between your work and its impact
• Technically ambitious: you'll build AI-powered workflows, not just support them
• Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter
• Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Research Scientist, AI-powered scientific discovery

Location: Montreal, Canada

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Snapshot

We are looking for a Research Scientist to join our team in Montreal dedicated to AI for Scientific Discovery. Specifically, the team studies systems that combine code execution and retrieval tools with natural-language scientific knowledge to accelerate new scientific discoveries. Research includes, but is not limited to general purpose algorithms that leverage Large Language Model (LLMs) for efficient search and exploration, LLM fine-tuning with Reinforcement Learning, and open-ended tasks for large scale AI-powered empirical research.  

About us

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

The role

Research Scientists at Google DeepMind lead our efforts in developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence.

In this role, responsibilities will include making key contributions into the latest research developed in Google DeepMind Scientific Discovery effort, such as:

Key responsibilities

• Design, implement and evaluate models, agents and software prototypes of large foundational models.
• Push the boundary of state of the art RL methods and machine learning optimization methods to build autonomous scientific discovery systems. 
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals.
• Work with external collaborators and maintain relationships with relevant research labs and key individuals as appropriate.
• Work in collaboration with our Responsible AI teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

About you

You are a passionate and talented researcher with a strong foundation and a proven ability to conduct impactful research in AI. You embrace change and thrive under ambiguity. You have a collaborative mindset and are excited to work as part of a team to tackle ambitious research challenges. You are passionate about seeing your research translated into real-world products that improve the scientific discovery process of users. You are eager to see your research contribute to real-world applications and are driven by a desire to create positive change through AI.

• PhD in Computer Science, Artificial Intelligence, or a related field.
• Strong publication record in top-tier machine learning conferences or journals.
• Solid understanding of deep learning, natural language processing, and/or reinforcement learning.
• Experience with Large Language Models, preferably in the context of code synthesis.
• Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.
• A real passion for AI!