Meet the Team: 
As a Machine Learning Engineer II – Learned Behaviors, you will help develop and deploy  behavior models that power decision-making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will contribute to learned behavior modules that enable safe, efficient, and human-like driving in real-world freight operations. 
 
This role focuses on building, validating, and improving machine learning models and infrastructure that support learned behavior systems within the autonomy stack. 
 
What You’ll Do 

• Develop and train machine learning models for learned behavior systems, including approaches such as behavior cloning, imitation learning, and reinforcement learning. 

• Implement production-quality ML code to support model training, evaluation, and inference within the autonomy stack. 

• Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across scenarios. 

• Contribute to model training pipelines and data workflows, curating behavior datasets from simulation, fleet logs, and on-vehicle data. 

• Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across diverse driving environments. 

• Help integrate learned behavior models into simulation and testing workflows, enabling faster iteration and more comprehensive validation. 

• Support the development of tooling and infrastructure that improves experimentation speed, reproducibility, and model iteration. 

• Contribute to technical discussions around model architecture and training strategies within the team. 

 
 
 
 
What You’ll Need to Succeed 

• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience. 

• Experience applying machine learning techniques such as imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments. 

• Strong programming skills in Python and PyTorch, with experience writing production-quality ML code. 

• Experience training and evaluating machine learning models using large datasets and scalable compute environments. 

• Understanding of ML architectures used in autonomy systems, such as transformers, graph neural networks, or sequence models. 

• Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines. 

• Ability to collaborate with cross-functional teams to integrate ML models into larger software systems. 

 
 
Bonus Points! 

• Experience working in autonomous driving, robotics, or simulation-based training environments. 

• Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray). 

• Experience working with simulation environments or large-scale behavior datasets. 

• Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems. 

• Experience deploying ML models into production or real-world robotics systems. 

Hiring Range for Job Opening  

US Pay Range 

$153,200 - $183,300 USD 

Job ID: 102515

Meet the Team: 
As a Machine Learning Engineer II – Scene Model, you will help develop and deploy machine learning models that enable autonomous trucks to understand their surrounding environment. Our team focuses on building multi-modal perception systems in bird’s-eye-view (BEV) that fuse information from LiDAR, cameras, radar, and map inputs to produce a unified representation of the scene.  

Working closely with teams across perception, prediction, planning, and platform infrastructure, you will contribute to models that detect objects, understand road structure, and generate spatial temporal representations used by downstream autonomy systems.  

This role focuses on developing and improving deep learning models, training pipelines, and data workflows that power scene understanding within the autonomy stack. 

What You’ll Do 

• Develop and train machine learning models for scene understanding, including tasks such as object detection, road and lane prediction, semantic voxel grid classification, occupancy prediction, and map understanding in bird’s-eye-view (BEV) space. 

• Implement production-quality ML code to support model training, evaluation, and inference within the perception stack. 

• Analyze model performance, identify failure modes, and propose improvements to increase robustness across diverse driving environments and conditions. 

• Identify and interpret objects, lanes, obstacles, and weather conditions in the driving environment. 

• Apply data science techniques to analyze model performance, understand data distributions, and identify corner cases. 

• Contribute to multi-modal perception systems, combining signals from LiDAR, cameras, radar, and map sources into unified scene representations. 

• Work with large-scale datasets from simulation, fleet logs, and on-vehicle data to curate training data and improve model performance. 

• Collaborate with data, deployment, and infrastructure teams to evaluate perception models and ensure reliable performance in real-world driving scenarios. 

• Help integrate perception models into the autonomy stack and testing pipelines, enabling faster experimentation and iteration. 

• Contribute to tooling and infrastructure that improves training efficiency, experiment tracking, and reproducibility. 

• Participate in technical discussions around model architectures, sensor fusion strategies, and training approaches within the team.  

What You’ll Need to Succeed 

• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience. 

• Strong understanding of computer-vision, and machine learning basics. 

• Experience applying machine learning techniques such as imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments. 

• Strong programming skills in Python and PyTorch, with experience writing production-quality ML code. 

• Experience training and evaluating machine learning models using large datasets and scalable compute environments. 

• Understanding of ML architectures used in autonomy systems, such as transformers, graph neural networks, or sequence models. 

• Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines. 

• Ability to collaborate with cross-functional teams to integrate ML models into larger software systems. 

• Good technical communication skills, written and verbal. 

• A positive team-player mindset.  

Bonus Points! 

• PhD in machine learning or data science. 

• Experience working in autonomous driving, robotics, or simulation-based training environments. 

• Experience with distributed training frameworks or large-scale ML infrastructure (e.g., Ray, Anyscale). 

• Experience working with simulation environments or large-scale behavior datasets. 

• Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems. 

• Experience deploying ML models into production or real-world robotics systems. 

• Experience with multi-modal sensor fusion, including LiDAR, cameras, radar, or map inputs. 

• Experience working with BEV representations, occupancy grids, or 3D scene representations. 

Hiring Range for Job Opening  

US Pay Range 

$153,200 - $183,300 USD 

Job ID: 102517

Meet the Team: 
As a Machine Learning Engineer II – End-to-End, you will help develop and deploy End-to-End models that power both perception and decision-making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will contribute to End-to-End models that enable safe, efficient, and human-like driving in real-world freight operations. 
 
This role focuses on building, validating, and improving machine learning models and infrastructure that support End-to-End systems within the autonomy stack. 
 
What You’ll Do 

• Develop and train machine learning models for End-to-End percetion and planning, including approaches such as imitation learning and reinforcement learning. 

• Implement production-quality ML code to support model training, evaluation, and inference within the autonomy stack. 

• Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across scenarios. 

• Contribute to model training pipelines and data workflows, curating datasets from simulation, fleet logs, and on-vehicle data. 

• Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate End-to-End models across diverse driving environments. 

• Help integrate End-to-End models into simulation and testing workflows, enabling faster iteration and more comprehensive validation. 

• Support the development of tooling and infrastructure that improve experimentation speed, reproducibility, and model iteration. 

• Contribute to technical discussions around model architecture and training strategies within the team. 

 
 
 
 
What You’ll Need to Succeed 

• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience. 

• Experience applying machine learning techniques such as computer vision, imitation learning, or reinforcement learning, to robotics, autonomous systems, or complex control environments. 

• Strong programming skills in Python and PyTorch, with experience writing production-quality ML code. 

• Experience training and evaluating machine learning models using large datasets and scalable compute environments. 

• Understanding of ML architectures used in End-to-End systems, such as BEV models, Transformers, VLA, or diffusion models. 

• Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines. 

• Ability to collaborate with cross-functional teams to integrate ML models into larger software systems. 

 
 
Bonus Points! 

• Experience working in autonomous driving, robotics, or simulation-based training environments. 

• Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray). 

• Experience with VLA or Neural Rendering. 

• Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems. 

• Experience deploying ML models into production or real-world robotics systems. 

Hiring Range for Job Opening  

US Pay Range 

$153,200 - $183,300 USD 

Job ID: 102514

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 are looking for Full-Stack Developers to join our Connected Data team in the Engineering Organization. This role has openings at Senior+ level positions.

The Connected Data team’s mission is to connect our customers’ siloed operational data to MaintainX — through integrations, sensor management, and the APIs that make it all possible. We own the systems that ingest, normalize, and surface the data that powers MaintainX’s automations and asset intelligence, including our Connectors (Ignition, Kepware, MQTT/Sparkplug B), our On-Premise Agent, and the Automation and Alerting engine. The team operates as multiple squads and works closely with our largest enterprise customers to bring their equipment data online.

What You’ll Do:

• Build and evolve our integrations platform — Design, develop, and maintain the connectors and data pipelines that bring customer data into MaintainX from industrial systems (Ignition, Kepware, MQTT brokers, OPC UA servers, SCADA, historians).
• Ship IoT and sensor experiences end-to-end — Implement backend ingestion, transformation, and storage for high-volume sensor telemetry, and the frontend surfaces customers rely on to monitor device health, manage fleets, and trust their data.
• Own services that move real data, at scale — Contribute to the reliability, performance, and observability of the team’s core services with a strong operational mindset.
• Collaborate across the product — Partner with Product Managers, Designers, and engineers across MaintainX to turn unstructured, real-world industrial data into intuitive product experiences.
• Write high-quality, maintainable code — Deliver clean, testable C#, TypeScript, and React code across backend services, the on-premise agent, and customer-facing UI.
• Raise the engineering bar — Participate in code reviews, contribute to architectural discussions, and help drive the long-term health of our platform through technical pitches, POCs, and bug bashes.

You’ll work across our stack (C#, TypeScript, React, React Native, Node.js, and GraphQL) — Web, Mobile, and On-Premise applications. Connected Data is one of the most polyglot teams at MaintainX: our connectors and the on-premise agent are written in C#, our web and mobile surfaces are TypeScript, React, and React Native, our service layer uses Node.js, and our APIs expose both REST (our public API) and GraphQL (to the rest of the product). You will work meaningfully in all of them.

• You’ll work with the team to build, maintain and optimize product features that focus on how our users get their operational data into MaintainX and act on it.
• You’ll actively own team features or systems and define their long-term health, while also improving the health of surrounding systems.
• You’ll mentor other engineers and review code.
• You’ll improve engineering standards, tooling and processes & bring best practices in planning, developing, testing and deploying industry compliant code.

About You:

• 5+ years of professional experience in full-stack software development.
• Strong programming skills in C# and TypeScript, with experience building production-grade applications in both — or strong experience in one and a genuine appetite to get to production quality in the other.
• Solid experience with React, Node.js, and consuming or building GraphQL APIs.
• Experience designing, building, or integrating against REST APIs and understanding the tradeoffs (versioning, pagination, rate limiting, idempotency, backwards compatibility).
• Comfortable with relational databases and data modeling for high-throughput, event-driven workloads.
• Proficient at writing understandable, maintainable, and testable code, with an eye towards reliability and observability.
• Strong communicator: explaining complex technical concepts to designers, support, product, and other engineers is no problem for you.
• You thrive in a fast-paced, customer-facing environment where the data you work with comes from real factories, real equipment, and real people on the frontline.

Nice to Have:

• Experience with industrial / OT protocols and platforms: MQTT, Sparkplug B, OPC UA, Modbus, Ignition, Kepware, or similar SCADA / historian / PLC ecosystems.
• Experience building IoT data pipelines — ingestion, time-series storage, anomaly detection, or device fleet management.
• Experience building or maintaining on-premise applications or hybrid cloud/edge deployments.
• Experience with cloud providers (AWS / Azure / GCP) and service deployment — particularly AWS IoT, Timestream, or SiteWise.
• Experience with Docker, Kubernetes, and modern CI/CD pipelines.
• Experience with observability tooling such as Datadog, and with running production services against meaningful SLIs/SLOs.
• Experience with LLM-powered features (extraction, mapping, summarization) in production integrations.

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:

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.

ROLE: XM ACCOUNT TEAM LEAD (BILINGUAL) 
TEAM: ACCOUNTS
LOCATION: QUEBEC

COMPANY OVERVIEW
Salt is a North American marketing agency that creates connected experiences through creative, digital & media innovation.

Our mission is to “Earn The World’s Attention” and Salt is built to find, develop, execute, and amplify the ideas that are worthy of our clients’ audiences. We’ve structured our agency to do what’s right for our clients – to connect different perspectives, to work across mediums, and to focus on delivering meaningful, effective results.

We’re committed to living up to the values in our name. “Salt of The Earth” means we value collaborative, humble, hard-working people here. We’re looking for people who are as smart as they are kind because we believe the right talent and the right culture help us do what’s right for our clients.

ROLE OVERVIEW
We are hiring an experienced in-field regional field manager to support a leading Canadian coffee brand and serve as an XM Account Team Lead. This role is focused exclusively on field execution, leadership and commercial vehicle operations.

You will lead mobile activations, manage drivers and brand ambassadors on site, and oversee the safe operation of commercial assets across your region. This is a hands on leadership position for someone confident operating large vehicles, managing technical equipment, and representing the brand in live environments.

You will operate with a high level of independence while maintaining structured communication and reporting to the Senior Manager.

CORE RESPONSIBILITIES

Field Leadership & Execution

• Lead the full execution of mobile and tent based activations from setup to teardown
• Supervise brand ambassadors, and on site team leads
• Act as the primary escalation point for operational or personnel issues in the field
• Ensure all activations meet brand, safety, and quality standards

Fleet & Commercial Vehicle Operations

• Confidently operate 18 to 35 ft commercial box trucks or similar large vehicles and portable generators
• Safely maneuver vehicles and trailers in urban, event, and high traffic environments
• Conduct vehicle inspections and maintain logbooks in compliance with provincial regulations
• Travel regionally and support multi location programs as required
• Maintain basic mechanical awareness and troubleshoot minor vehicle issues when needed
• Follow all safety procedures when working with fuel, power, and mobile equipment systems

Asset & Inventory Oversight

• Ensure mobile assets are clean, stocked, and event ready
• Support inventory tracking across vehicles and storage locations
• Coordinate asset movement and basic maintenance requirements

Reporting & Communication

• Deliver daily field and post event reports
• Track performance metrics including attendance, sampling, and team execution
• Maintain proactive communication with internal leadership regarding program updates

EXPERIENCE & SKILLS

Leadership and Field Management

• Previous experience leading or supervising teams, ideally within experiential, events, or field marketing
• Proven ability to train, coach, and manage brand ambassadors in fast paced environments

Operations and Logistics

• Strong experience managing event operations, mobile assets, or large scale field programs
• Comfortable with physical work including lifting, loading, and extended standing
• Highly organized, with the ability to manage schedules, staffing needs, and regional coverage
• Comfortable working independently and owning responsibilities end to end

Communication and Relationship Building

• Excellent verbal and written communication skills
• Confident acting as the main point of contact for vendors, infield staff and internal management
• Strong relationship builder with a collaborative and solutions focused mindset

Driving, Reporting, and Compliance

• Valid full G driver’s license
• Experience driving commercial vehicle
• Willingness to complete daily travel and driving logs
• Ability to provide a clean driver’s abstract if selected

Adaptability and Availability

• Flexible and available for extensive regional travel
• Available ASAP through the end of December 2026
• Comfortable working remotely from the field

WHAT WE STAND FOR

• Salt of the Earth: We lead with humility, honesty, and heart—always.
• Do What’s Right: No shortcuts. We choose integrity over ease, every time.
• Earn Your Salt: We roll up our sleeves, chase results, and celebrate the journey.
• Make Things Better: We’re solution-seekers, always pushing for smarter ways forward.
• Seek Different Perspectives: Diversity fuels our thinking. Inclusion drives our impact.

The anticipated annual base salary range for this position is $50-55K. Individual compensation within this range is determined based on job-related factors such as experience, skills, level of responsibility, internal equity, and business needs. This range represents the expected base salary for this role and does not include other elements of SALT total rewards program.