The Application Modernization Platform team aids developers in making the shift from relational databases to MongoDB, building toolkits that leverage AI to power seamless transitions. 

As a Senior Technical Program Manager, you will own the successful delivery of complex, cross-functional efforts for Application Modernization. You will partner closely with stakeholders across the organization from Engineering, Product, Product Design, and Field-facing teams to develop and execute against our overall product vision and business objectives, uncover solutions to technical challenges, and provide data-driven insights that power strategic decision-making.

This remote position is open to candidates in Eastern and Central timezones.

The right candidate for this role will be:

• Experienced with 5+ years of Software development background, preferably with experience with Java web services and with relational data modelling and SQL
• Familiar with tools and methodologies for code analysis, refactoring, and automated testing
• Able to partner with technical stakeholders in methodically breaking down engineering challenges to deliver against abstract goals
• Able to participate in technical design discussions and summarize trade-offs, outcomes, and next steps
• Skilled at communicating across a diverse set of engineers and stakeholders
• Capable of deep user empathy and adept at balancing product goals and engineering constraints to deliver impactful experiences
• Hyper-organized and capable of coordinating across multiple independent work streams and organizations
• A role model for effective execution practices, driven by an attuned sense of priority and urgency
• Able to leverage their experience in program delivery to influence improvements to our tools, operations, architecture
• Trained in working with project tracking software (JIRA is preferred)
• Familiar with MongoDB or a comparable technology
• Exposed to generative AI and Machine Learning
• Interested in business automation work such as scripting in Python, Google Apps Script and Slack

Position Expectations:

• Leverage technical acumen and analytical skills to drive engineering programs forward and maximize business value delivery
• Recognize patterns in a sea of information and take action accordingly
• Develop a strategic approach to program management that balances hands-on oversight with process-driven operationalization. This involves discerning when to provide direct guidance and when to empower teams by establishing clear frameworks and guidelines for ongoing initiatives
• Build strategic partnerships with Product, Design, and Engineering stakeholders
• Expand knowledge into new domains as called upon
• Embody MongoDB's core values and leadership principles

Success Measures:

• In 3 months you will be ramped on the App Modernization product and engineering roadmap
• In 6 months you will own and drive critical initiatives across the App Modernization portfolio
• In 12 months you will be organizing multiple high impact efforts and integrating improvements to frameworks for managing critical programs at MongoDB

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform, the most widely available, globally distributed database on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and over 60,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB is an equal opportunities employer.

REQ ID: 4263333180

MUST HAVE:

• 5+ years of experience in data engineering within fast-paced, cloud-native environments
• Deep expertise in Python, SQL, Docker, and AWS (S3, Glue, Kinesis, Athena/Presto)
• Experience building and managing scalable ETL pipelines and data lake infrastructure
• Familiarity with distributed systems, Spark, and data quality best practices
• Strong cross-functional collaboration skills to support BI, analytics, and engineering teams

EXPERIENCE: 5+ years of data engineering experience in an AWS-based environment where data powers decision-making across product, marketing, and operations.

YOUR ROLE

Lead scalable data engineering efforts that empower cross-functional teams with reliable, timely, and actionable data, ensuring Launch Potato’s analytics and business intelligence infrastructure fuels strategic growth.

OUTCOMES

• Build and optimize scalable, efficient ETL and data lake processes that proactively catch issues before they impact the business
• Own the ingestion, modeling, and transformation of structured and unstructured data to support reporting and analysis across all business units
• Partner closely with BI and Analytics to deliver clean, query-ready datasets that improve user acquisition, engagement, and revenue growth
• Maintain and enhance database monitoring, anomaly detection, and quality assurance workflows
• Serve as the internal point of contact for reporting infrastructure—delivering ad hoc data analyses and driving consistent data integrity
• Drive adoption and advancement of Looker dashboards by ensuring robust and scalable backend data support
• Contribute to the future of Launch Potato’s data team by mentoring peers and shaping a high-performance, quality-first engineering culture

COMPETENCIES

• Data Engineering Mastery: Demonstrates technical excellence in building data pipelines, troubleshooting distributed systems, and scaling infrastructure using AWS and open-source tools
• Cross-Functional Collaboration: Communicates clearly across technical and non-technical teams, translating business needs into robust data solutions
• Proactive Ownership: Operates with a strong sense of accountability; identifies and solves data issues independently and efficiently
• Quality-Driven Execution: Holds a high bar for data accuracy, auditability, and documentation throughout all systems and workflows
• Strategic Thinking: Anticipates how data infrastructure impacts wider company OKRs and proactively suggests improvements and innovations
• Growth Mindset: Seeks out opportunities to elevate team capabilities, mentor others, and stay ahead of evolving best practices in data engineering

MUST HAVE:

• 5+ years of experience in data engineering within fast-paced, cloud-native environments
• Deep expertise in Python, SQL, Docker, and AWS (S3, Glue, Kinesis, Athena/Presto)
• Experience building and managing scalable ETL pipelines and data lake infrastructure
• Familiarity with distributed systems, Spark, and data quality best practices
• Strong cross-functional collaboration skills to support BI, analytics, and engineering teams

EXPERIENCE: 5+ years of data engineering experience in an AWS-based environment where data powers decision-making across product, marketing, and operations.

YOUR ROLE

Lead scalable data engineering efforts that empower cross-functional teams with reliable, timely, and actionable data, ensuring Launch Potato’s analytics and business intelligence infrastructure fuels strategic growth.

OUTCOMES

• Build and optimize scalable, efficient ETL and data lake processes that proactively catch issues before they impact the business
• Own the ingestion, modeling, and transformation of structured and unstructured data to support reporting and analysis across all business units
• Partner closely with BI and Analytics to deliver clean, query-ready datasets that improve user acquisition, engagement, and revenue growth
• Maintain and enhance database monitoring, anomaly detection, and quality assurance workflows
• Serve as the internal point of contact for reporting infrastructure—delivering ad hoc data analyses and driving consistent data integrity
• Drive adoption and advancement of Looker dashboards by ensuring robust and scalable backend data support
• Contribute to the future of Launch Potato’s data team by mentoring peers and shaping a high-performance, quality-first engineering culture

COMPETENCIES

• Data Engineering Mastery: Demonstrates technical excellence in building data pipelines, troubleshooting distributed systems, and scaling infrastructure using AWS and open-source tools
• Cross-Functional Collaboration: Communicates clearly across technical and non-technical teams, translating business needs into robust data solutions
• Proactive Ownership: Operates with a strong sense of accountability; identifies and solves data issues independently and efficiently
• Quality-Driven Execution: Holds a high bar for data accuracy, auditability, and documentation throughout all systems and workflows
• Strategic Thinking: Anticipates how data infrastructure impacts wider company OKRs and proactively suggests improvements and innovations
• Growth Mindset: Seeks out opportunities to elevate team capabilities, mentor others, and stay ahead of evolving best practices in data engineering

What we’re looking for

The Machine Learning Platform team (MLP) is seeking a Senior Software Engineer II to design and implement the secure, highly scalable, and high-performance pipelines that govern the end-to-end lifecycle of ML models.

You will work at the intersection of Data Science and DevOps, building the "connective tissue" that empowers our product portfolio to leverage technologies like Generative AI, Natural Language Processing (NLP), and real-time classification. We are looking for a subject matter expert who will drive innovation and deliver results that meet the dynamic needs of a high-growth AI environment. You will report to the Senior Engineering Manager on the MLP Solutions team.

What you’ll be working on

• Build and maintain robust ML systems using Python (Pandas/NumPy, PyTorch/transformers) to support efficient ML operations.
• Design ML cloud infrastructure using AWS services, ensuring the platform is built for reuse, scalability, and high-volume throughput.
• Collaborate with application engineers to integrate, test, and monitor ML model services across SurveyMonkey’s microservices architecture.
• Act as a consultant to internal feature teams, educating and influencing decisions regarding ML tooling and infrastructure.
• Incorporate telemetry into the platform for complex failure mode analysis, ensuring real-time reliability for millions of daily survey responses.
• Serve as a technical leader and mentor, championing high standards in coding, documentation, and architectural best practices to foster the professional growth of junior engineers on the MLP team.

We’d love to hear from people with

• 8+ years of professional ML development experience with a proven track record of designing and implementing ML infrastructure in AWS.
• Mastery of ML concepts (supervised/unsupervised learning) and deep understanding of Large Language Models (LLMs) and NLP.
• Expert-level Python skills and experience with high-scale data processing (PySpark, etc.).
• Comfortable with Unix/Linux systems and experienced in deploying end-to-end solutions for real-time applications (spam detection, personalization, ranking).
• Bachelor’s Degree in Computer Science, Data Science, Software Engineering, Mathematics, Statistics, or a related quantitative field.

The base pay provided for this position ranges from CAD $126,400.00/ year - $158,000.00/ year depending on the geographic market and assuming a full-time schedule. Actual base pay is based on a number of factors including market location, job-related knowledge, education or training, skills, and experience.

Bonuses may also be offered as part of the total compensation package, in addition to a competitive benefits package including medical, dental, vision, life, and disability insurance; RRSP matching; flexible spending & health savings account; paid holidays; paid time off; employee assistance program; and other company benefits. 

This opening is for an existing vacancy.

SurveyMonkey believes in-person collaboration is valuable for building relationships, fostering community, and enhancing our speed and execution in problem-solving and decision-making. As such, you will be required to work from our Ottawa office up to 1 day per week.

#LI-Hybrid

Location: Ottawa, ON

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

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

The impact you will have:

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

What we look for:

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

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

Overview

AEC firms and infrastructure owner/operators increasingly demand seamless integration between GIS data and BIM models to improve all aspects of asset design, construction, and operation. The global demand for housing and the need for roads, energy, and water to support them requires innovative workflows that help owners, engineers, planners, and stakeholders collaborate. Esri is working in partnership with Autodesk and other firms to help our customers design, build, and operate the sustainable infrastructure of the future more rapidly, at lower cost, and in compliance with complex regulatory landscapes. 

Providing this integration requires innovative software engineering to ensure reliable, secure, seamless, and scalable access to this information in ways that are user- and industry-focused while adapting to evolving global standards for BIM and GIS, such as IFC, 3D Tiles, and Esri i3s. In this role, you will be the crucial resource in the design and implementation of this integration. Join us in helping define the future of GIS-AEC integration in collaboration with the product owner, product engineers, and software engineering peers on a multi-disciplinary team. 

Responsibilities

• Design and implement:
  • Integration between the ArcGIS geospatial cloud and cloud-based BIM systems such as Autodesk Docs, Autodesk Platform Services, Autodesk Forma, and other information systems
  • Public and internal RESTful APIs to integrate the above platforms
  • Typescript components and widgets compatible with ArcGIS Experience Builder
  • Geoprocessing tools to automate the linking and data migration of BIM data to ArcGIS
• Communicate with industry developers on best practices for extending ArcGIS to the AEC and BIM industries
• Design real-time and batch data pipelines for synchronizing versioned BIM data with GIS
• Construct complex micro-service pipelines for large BIM datasets 

Requirements

• 5+ years of software product development experience
• Demonstrated experience developing cloud infrastructure, backend services, and data pipelines
• Exceptional understanding at least two of the following languages and tools: TypeScript, JavaScript, NodeJS, Bootstrap, React, Angular, and other responsive web frameworks or extensive experience with C++, .NET and/or Python
• Knowledge of Agile software engineering processes and proficiency with git.
• Excellent problem-solving and debugging skills
• Bachelor's degree in computer science or computer engineering, or related field 

Recommended Qualifications

• Experience with 2D and 3D computational geometry
• Scalable full-stack software engineering experience
• Awareness of Autodesk Platform Services, ObjectArx, or Revit APIs
• Awareness of ArcGIS web application development
• Familiarity with TypeScript, React, Angular, or other component library or framework
• Experience with cloud services such as Amazon AWS, Microsoft Azure
• Experience implementing effective unit and integration tests 

Esri's total rewards strategy includes group supplemental insurance benefits, retirement plans, accrued vacation, holidays, competitive compensation, and opportunities for personal and professional growth.  Compensation decisions take into account many factors including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.  A reasonable estimate of the salary range for this role is CAD $94,500 - $138,600.

#LI-RC2

#LI-Onsite

The mission of Speechify is to make sure that reading is never a barrier to learning.

Over 50 million people use Speechify’s text-to-speech products to turn whatever they’re reading – PDFs, books, Google Docs, news articles, websites – into audio, so they can read faster, read more, and remember more. Speechify’s text-to-speech reading products include its iOS app, Android App, Mac App, Chrome Extension, and Web App. Google recently named Speechify the Chrome Extension of the Year and Apple named Speechify its 2025 Design Award winner for Inclusivity.  

Today, nearly 200 people around the globe work on Speechify in a 100% distributed setting – Speechify has no office. These include frontend and backend engineers, AI research scientists, and others from Amazon, Microsoft, and Google, leading PhD programs like Stanford, high growth startups like Stripe, Vercel, Bolt, and many founders of their own companies.

Overview

We're looking to hire for our Data side of our AI team at Speechify. This role is responsible for all aspects of data collection to support our model training operations. We are able to build high-quality datasets at petabyte-scale and low cost through a tight integration of infrastructure, engineering, and research work. We are looking for a skilled Software Engineer to join us.

What You’ll Do

• Be scrappy to find new sources of audio data and bring it into our ingestion pipeline
• Operate and extend the cloud infrastructure for our ingestion pipeline, currently running on GCP and managed with Terraform.
• Collaborate closely with our Scientists to shift the cost/throughput/quality frontier, delivering richer data at bigger scale and lower cost to power our next-generation models.
• Collaborate with others on the AI Team and Speechify Leadership to craft the AI Team’s dataset roadmap to power Speechify’s next-generation consumer and enterprise products.

An Ideal Candidate Should Have

• BS/MS/PhD in Computer Science or a related field.
• 5+ years of industry experience in software development.
• Proficiency with bash/Python scripting in Linux environments
• Proficiency in Docker and Infrastructure-as-Code concepts and professional experience with at least one major Cloud Provider (we use GCP)
• Experience with web crawlers, large-scale data processing workflows is a plus
• Ability to handle multiple tasks and adapt to changing priorities.
• Strong communication skills, both written and verbal.

What we offer

• A fast-growing environment where you can help shape the company and product.
• An entrepreneurial-minded team that supports risk, intuition, and hustle.
• A hands-off management approach so you can focus and do your best work.
• An opportunity to make a big impact in a transformative industry.
• Competitive salaries, a friendly and laid-back atmosphere, and a commitment to building a great asynchronous culture.
• Opportunity to work on a life-changing product that millions of people use.
• Build products that directly impact and support people with learning differences like dyslexia, ADD, low vision, concussions, autism, and more.
• Work in one of the fastest-growing sectors of tech, the intersection of artificial intelligence and audio.

Think you’re a good fit for this job? 

Tell us more about yourself and why you're interested in the role when you apply.
And don’t forget to include links to your portfolio and LinkedIn.

Not looking but know someone who would make a great fit? 

Refer them! 

Speechify is committed to a diverse and inclusive workplace. 

Speechify does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

About the Role

As a Context Engineer at CapIntel, you'll sit at the intersection of AI infrastructure and engineering. You will be responsible for how large language models are integrated into our core platform and how our engineering team adopts agentic workflows. This is a hands-on, production-focused role, not a research one. You'll build the systems that make our AI features reliable, accurate, and scalable for the wealth management enterprises that depend on us.

You'll be embedded in development teams working closely with engineers, product managers, and domain experts across the organization to design and deliver LLM-powered capabilities that directly enhance the advisor and client experience. As one of the first practitioners in this discipline at CapIntel, you'll also help define what context engineering looks like here: setting patterns and practices the broader team can build on.

This role is ideal for someone who thinks in systems, cares about production reliability over demo-day performance, and is energized by working in a discipline that is evolving quickly.

What You'll Do

• Design and implement LLM-powered features into our core application via model APIs (e.g. Anthropic, OpenAI, Cohere), with a focus on reliability and production-readiness
• Architect and maintain retrieval-augmented generation (RAG) pipelines, connecting language models to internal knowledge bases, databases, and live data sources
• Manage context window strategy, determining what information enters the model, when, in what format, and at what level of compression to optimise for accuracy, cost, and latency
• Design and implement agentic workflows enabling the platform to handle multi-step, autonomous tasks
• Build guardrail and output validation layers that constrain model behaviour and ensure AI features act within well-defined, compliant boundaries
• Develop reusable agent primitives, prompt templates, and workflow components that other engineers can build on independently
• Build evaluation frameworks to measure context effectiveness, output quality, and agent reliability in production
• Monitor deployed AI systems for failure patterns and implement mitigation strategies, feeding learnings back into continuous improvement cycles
• Collaborate with Product, Product Engineering, Implementation, and Data teams to translate business requirements, and proof of concepts into production AI system specifications
• Act as an internal practitioner and resource helping upskill the broader engineering team on context engineering principles and agentic best practices

What We're Looking For

• 5+ years of professional software engineering experience, with at least 1–2 years working with LLMs in a production context
• Strong experience with Python or Node and building API-integrated backend services
• Hands-on experience with an orchestration or execution framework
• Working knowledge of RAG architecture, vector databases (e.g. Pinecone, pgVector, AWS OpenSearch), and semantic search
• Familiarity with context management techniques: summarisation, chunking, session splitting, and memory strategies
• Experience building or consuming REST APIs and integrating with third-party services
• Comfortable collaborating with cross-functional teams in a fast-paced, high-growth environment
• Strong problem-solving instincts and a willingness to learn and adapt as the field evolves

Nice to Have

• Experience with the Model Context Protocol (MCP) or similar tool-integration standards
• Familiarity with LLMOps practices: tracing, observability (e.g. LangSmith, Datadog), and model versioning
• Exposure to multi-agent architectures and orchestration patterns
• Knowledge of AI output validation, context safety, and governance considerations particularly relevant in regulated industries like financial services
• Familiarity with AWS or cloud-based infrastructure and containerised deployments (Docker, Kubernetes)
• Ability to communicate technical concepts clearly to both technical and non-technical partners