We are a highly collaborative team of builders and operators who invent new ways of working in an industry that historically has resisted innovation. Join us!

We’re looking for motivated, detail-oriented individuals to join our Pharmacy Operations team as a Pharmacy Prior Authorization Trainee. This is an exciting opportunity to begin a career in healthcare and gain hands-on training in Prior Authorization (PA) processes, insurance navigation, and pharmacy operations.

No prior authorization experience is required- we will train you.

This is a fully On-site role in Robinson Township, Pittsburgh

What you’ll do:  

• Learn the fundamentals of prior authorization processes and pharmacy workflows
• Assist in initiating prior authorization requests under guidance
• Utilize Knowledge Base materials as a foundation for resolving inquiries
• Follow up on authorization status updates
• Escalate complex or clinical cases to experienced team members
• Develop knowledge of pharmacy benefits, insurance terminology, and compliance standards
• Maintain confidentiality of patient and proprietary information
• As you grow in the role, you’ll take on increased ownership and develop into a fully skilled Prior Authorization agent. Work to resolve common data entry issues with internal stakeholders
• Strive to meet and exceed structured performance targets. 
• Help improve the patient experience by sharing feedback with management to further develop the product, materials and processes 

What makes you a great fit: 

• Strong interest in building a career in healthcare or pharmacy operations
• Customer service, call center, retail, or administrative experience preferred
• Excellent verbal and written communication skills
• Strong attention to detail and ability to follow structured processes
• Comfortable learning new systems and working across multiple tools
• Problem-solving mindset with a willingness to ask questions
• Ability to work collaboratively in a fast-paced team environment
• Passion for helping patients access the medications they need

Requirements

• Desire to work in a customer/patient-centric role
• High school diploma or GED required
• Strong technical aptitude and ability to learn complex new software
• Customer service experience preferred
• Strong verbal and written communication skills
• A passion for providing top-notch patient care

Hours/Location:

• • Shift: 40hours/week  
    
    • Weekly rotating schedules: 12 PM -8 PM EST and 1 PM -9 PM EST shifts
  • Fully Onsite in Pittsburgh, PA (Robinson Township) 

Benefits:

• Health Benefits, 401K
• Holiday pay
• Overtime eligible
• Casual dress code
• Free Snacks
• Free Shuttle service

#BlinkPharmacy

Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. Backed by Hyundai Motor Group, Motional is at the forefront of the physical AI revolution. Motional isn’t just building first-of-its-kind technology; we are transforming transportation to create safer streets and more sustainable mobility options.

The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas later this year.

Mission Summary:

The Autonomy Subsystems team at Motional is focused on designing and evaluating modules of the autonomy software. We are seeking a talented senior engineer to contribute to evaluation of our machine learning subsystems. Our team performs a variety of evaluations of machine learning models, including offline model eval, evaluation of open and closed-loop re-simulations, and assessment of on-road performance. Our goal is to establish metrics that describe autonomy subsystem performance, evaluate against those metrics to validate performance, and provide meaningful insight to ML developers and teams, working closely alongside them to improve the performance of ML models on the car.

We are looking for a highly-technical systems engineer with experience designing and evaluating safety critical systems and machine learning models. Bring your passion for autonomy, robotics, and machine learning to our team to help build a production-ready system and prepare for our scaled robotaxi service deployment.

Responsibilities:

• Define, prototype, and validate advanced metrics that bring new insights to model performance evaluation for autonomy subsystems (mainly focused on perception, prediction, and planning).
• Assess coverage of existing training and testing, then curate test sets that sufficiently capture Motional’s intended deployment operational design domain, across many axes (agent types, interaction types, map/spatial, etc.).
• Evaluate machine learning model performance against these metrics and tests.
• Assess system-level performance and subsystem contribution to system behavior through large-scale data analysis of on-road and simulation events.
• Define performance requirements for autonomy subsystems, and supporting analyses required to establish these targets.
• Coordinate subsystem-level evaluation with other system-level testing efforts across the systems team in support of the safety case and safety claims.
  
  

Qualifications:

• An engineer who has demonstrated strong cross-functional collaborative skills and is highly self-starting. This is a highly cooperative role across systems, autonomy, infrastructure, and other parts of the organization.
• Proven engineer with 5+ years of experience working on high-tech safety critical systems or robotics, ideally from a systems, robotics, or similar background.
• Master’s degree in relevant fields (systems, robotics, ML, CS, etc.) or Bachelor’s degree plus significant relevant in-field experience.
• Skilled Python developer, familiarity with analyzing the large scale data required to train and test ML models.
• Experience designing unique metrics that capture key aspects of system behavior and are used to enhance model development.
  
  

Bonus Skills:

• Experience training machine learning models, familiarity with state-of-the-art ML model architectures.
• Deep experience with a specific aspect of machine learning for autonomous robotics (perception, prediction, or planning).
• Knowledge of C++, especially applied to safety critical systems.
• Experience with systems verification and validation techniques
• Experience with regression testing updates to already-deployed software in a safety-critical environment.
• Experience with automotive standards (E.g: ISO 12207, ISO 26262, ISO 21448)
• Experience with software quality management and assurance activities (E.g: IATF 16949, ISO 9001)
• Thorough understanding of robotics systems including sensors, actuators, mechatronics and software systems
• Work in cross collaboration teams to address problems and find solutions
  
  

Physical Demands:

While performing the duties of this job, the employee is frequently required to sit, talk, or hear. The employee is occasionally required to stand and at times for long periods; walk; use hands to finger, handle, or feel; reach with hands and arms. The employee must occasionally lift and move up to 50 pounds.

Travel Requirements (greater than 20%):
Expected to travel for 1-2 weeks each quarter visiting all of our testing sites - mostly to LV, but also domestically as needed for the business.

Working Environment

The work environment characteristics described here represent those a team member encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. The team member is regularly exposed to mechanical and computer parts. The team member is occasionally exposed to fumes and/or airborne particlesThe noise level in the environment is low to moderate. 

Motional AD LLC is an Equal Employment Opportunity employer and we welcome all qualified applicants.  All applicants will receive impartial consideration without regard to race, sex, color, national origin, age, disability, veteran status, genetic data, religion, gender identity, sexual orientation or other legally protected status.  Motional AD LLC is a Drug-free workplace.

Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. Backed by Hyundai Motor Group, Motional is at the forefront of the physical AI revolution. Motional isn’t just building first-of-its-kind technology; we are transforming transportation to create safer streets and more sustainable mobility options.

The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas later this year.

Mission Summary:

The Autonomy Subsystems team at Motional is focused on designing and evaluating modules of the autonomy software. We are seeking a talented senior engineer to contribute to evaluation of our machine learning subsystems. Our team performs a variety of evaluations of machine learning models, including offline model eval, evaluation of open and closed-loop re-simulations, and assessment of on-road performance. Our goal is to establish metrics that describe autonomy subsystem performance, evaluate against those metrics to validate performance, and provide meaningful insight to ML developers and teams, working closely alongside them to improve the performance of ML models on the car.

We are looking for a highly-technical systems engineer with experience designing and evaluating safety critical systems and machine learning models. Bring your passion for autonomy, robotics, and machine learning to our team to help build a production-ready system and prepare for our scaled robotaxi service deployment.

Responsibilities:

• Define, prototype, and validate advanced metrics that bring new insights to model performance evaluation for autonomy subsystems (mainly focused on perception, prediction, and planning).
• Assess coverage of existing training and testing, then curate test sets that sufficiently capture Motional’s intended deployment operational design domain, across many axes (agent types, interaction types, map/spatial, etc.).
• Evaluate machine learning model performance against these metrics and tests.
• Assess system-level performance and subsystem contribution to system behavior through large-scale data analysis of on-road and simulation events.
• Define performance requirements for autonomy subsystems, and supporting analyses required to establish these targets.
• Coordinate subsystem-level evaluation with other system-level testing efforts across the systems team in support of the safety case and safety claims.
  
  

Qualifications:

• An engineer who has demonstrated strong cross-functional collaborative skills and is highly self-starting. This is a highly cooperative role across systems, autonomy, infrastructure, and other parts of the organization.
• Proven engineer with 5+ years of experience working on high-tech safety critical systems or robotics, ideally from a systems, robotics, or similar background.
• Master’s degree in relevant fields (systems, robotics, ML, CS, etc.) or Bachelor’s degree plus significant relevant in-field experience.
• Skilled Python developer, familiarity with analyzing the large scale data required to train and test ML models.
• Experience designing unique metrics that capture key aspects of system behavior and are used to enhance model development.
  
  

Bonus Skills:

• Experience training machine learning models, familiarity with state-of-the-art ML model architectures.
• Deep experience with a specific aspect of machine learning for autonomous robotics (perception, prediction, or planning).
• Knowledge of C++, especially applied to safety critical systems.
• Experience with systems verification and validation techniques
• Experience with regression testing updates to already-deployed software in a safety-critical environment.
• Experience with automotive standards (E.g: ISO 12207, ISO 26262, ISO 21448)
• Experience with software quality management and assurance activities (E.g: IATF 16949, ISO 9001)
• Thorough understanding of robotics systems including sensors, actuators, mechatronics and software systems
• Work in cross collaboration teams to address problems and find solutions
  
  

Physical Demands:

While performing the duties of this job, the employee is frequently required to sit, talk, or hear. The employee is occasionally required to stand and at times for long periods; walk; use hands to finger, handle, or feel; reach with hands and arms. The employee must occasionally lift and move up to 50 pounds.

Travel Requirements (greater than 20%):
Expected to travel for 1-2 weeks each quarter visiting all of our testing sites - mostly to LV, but also domestically as needed for the business.

Working Environment

The work environment characteristics described here represent those a team member encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. The team member is regularly exposed to mechanical and computer parts. The team member is occasionally exposed to fumes and/or airborne particlesThe noise level in the environment is low to moderate. 

Motional AD LLC is an Equal Employment Opportunity employer and we welcome all qualified applicants.  All applicants will receive impartial consideration without regard to race, sex, color, national origin, age, disability, veteran status, genetic data, religion, gender identity, sexual orientation or other legally protected status.  Motional AD LLC is a Drug-free workplace.

Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. Backed by Hyundai Motor Group, Motional is at the forefront of the physical AI revolution. Motional isn’t just building first-of-its-kind technology; we are transforming transportation to create safer streets and more sustainable mobility options.

The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas later this year.

Mission Summary:

The Autonomy Subsystems team at Motional is focused on designing and evaluating modules of the autonomy software. We are seeking a talented senior engineer to contribute to evaluation of our machine learning subsystems. Our team performs a variety of evaluations of machine learning models, including offline model eval, evaluation of open and closed-loop re-simulations, and assessment of on-road performance. Our goal is to establish metrics that describe autonomy subsystem performance, evaluate against those metrics to validate performance, and provide meaningful insight to ML developers and teams, working closely alongside them to improve the performance of ML models on the car.

We are looking for a highly-technical systems engineer with experience designing and evaluating safety critical systems and machine learning models. Bring your passion for autonomy, robotics, and machine learning to our team to help build a production-ready system and prepare for our scaled robotaxi service deployment.

Responsibilities:

• Define, prototype, and validate advanced metrics that bring new insights to model performance evaluation for autonomy subsystems (mainly focused on perception, prediction, and planning).
• Assess coverage of existing training and testing, then curate test sets that sufficiently capture Motional’s intended deployment operational design domain, across many axes (agent types, interaction types, map/spatial, etc.).
• Evaluate machine learning model performance against these metrics and tests.
• Assess system-level performance and subsystem contribution to system behavior through large-scale data analysis of on-road and simulation events.
• Define performance requirements for autonomy subsystems, and supporting analyses required to establish these targets.
• Coordinate subsystem-level evaluation with other system-level testing efforts across the systems team in support of the safety case and safety claims.
  
  

Qualifications:

• An engineer who has demonstrated strong cross-functional collaborative skills and is highly self-starting. This is a highly cooperative role across systems, autonomy, infrastructure, and other parts of the organization.
• Proven engineer with 5+ years of experience working on high-tech safety critical systems or robotics, ideally from a systems, robotics, or similar background.
• Master’s degree in relevant fields (systems, robotics, ML, CS, etc.) or Bachelor’s degree plus significant relevant in-field experience.
• Skilled Python developer, familiarity with analyzing the large scale data required to train and test ML models.
• Experience designing unique metrics that capture key aspects of system behavior and are used to enhance model development.
  
  

Bonus Skills:

• Experience training machine learning models, familiarity with state-of-the-art ML model architectures.
• Deep experience with a specific aspect of machine learning for autonomous robotics (perception, prediction, or planning).
• Knowledge of C++, especially applied to safety critical systems.
• Experience with systems verification and validation techniques
• Experience with regression testing updates to already-deployed software in a safety-critical environment.
• Experience with automotive standards (E.g: ISO 12207, ISO 26262, ISO 21448)
• Experience with software quality management and assurance activities (E.g: IATF 16949, ISO 9001)
• Thorough understanding of robotics systems including sensors, actuators, mechatronics and software systems
• Work in cross collaboration teams to address problems and find solutions
  
  

Physical Demands:

While performing the duties of this job, the employee is frequently required to sit, talk, or hear. The employee is occasionally required to stand and at times for long periods; walk; use hands to finger, handle, or feel; reach with hands and arms. The employee must occasionally lift and move up to 50 pounds.

Travel Requirements (greater than 20%):
Expected to travel for 1-2 weeks each quarter visiting all of our testing sites - mostly to LV, but also domestically as needed for the business.

Working Environment

The work environment characteristics described here represent those a team member encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. The team member is regularly exposed to mechanical and computer parts. The team member is occasionally exposed to fumes and/or airborne particlesThe noise level in the environment is low to moderate. 

Motional AD LLC is an Equal Employment Opportunity employer and we welcome all qualified applicants.  All applicants will receive impartial consideration without regard to race, sex, color, national origin, age, disability, veteran status, genetic data, religion, gender identity, sexual orientation or other legally protected status.  Motional AD LLC is a Drug-free workplace.

Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. Backed by Hyundai Motor Group, Motional is at the forefront of the physical AI revolution. Motional isn’t just building first-of-its-kind technology; we are transforming transportation to create safer streets and more sustainable mobility options.

The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas later this year.

Mission Summary:

The Autonomy Subsystems team at Motional is focused on designing and evaluating modules of the autonomy software. We are seeking a talented senior engineer to contribute to evaluation of our machine learning subsystems. Our team performs a variety of evaluations of machine learning models, including offline model eval, evaluation of open and closed-loop re-simulations, and assessment of on-road performance. Our goal is to establish metrics that describe autonomy subsystem performance, evaluate against those metrics to validate performance, and provide meaningful insight to ML developers and teams, working closely alongside them to improve the performance of ML models on the car.

We are looking for a highly-technical systems engineer with experience designing and evaluating safety critical systems and machine learning models. Bring your passion for autonomy, robotics, and machine learning to our team to help build a production-ready system and prepare for our scaled robotaxi service deployment.

Responsibilities:

• Define, prototype, and validate advanced metrics that bring new insights to model performance evaluation for autonomy subsystems (mainly focused on perception, prediction, and planning).
• Assess coverage of existing training and testing, then curate test sets that sufficiently capture Motional’s intended deployment operational design domain, across many axes (agent types, interaction types, map/spatial, etc.).
• Evaluate machine learning model performance against these metrics and tests.
• Assess system-level performance and subsystem contribution to system behavior through large-scale data analysis of on-road and simulation events.
• Define performance requirements for autonomy subsystems, and supporting analyses required to establish these targets.
• Coordinate subsystem-level evaluation with other system-level testing efforts across the systems team in support of the safety case and safety claims.
  
  

Qualifications:

• An engineer who has demonstrated strong cross-functional collaborative skills and is highly self-starting. This is a highly cooperative role across systems, autonomy, infrastructure, and other parts of the organization.
• Proven engineer with 5+ years of experience working on high-tech safety critical systems or robotics, ideally from a systems, robotics, or similar background.
• Master’s degree in relevant fields (systems, robotics, ML, CS, etc.) or Bachelor’s degree plus significant relevant in-field experience.
• Skilled Python developer, familiarity with analyzing the large scale data required to train and test ML models.
• Experience designing unique metrics that capture key aspects of system behavior and are used to enhance model development.
  
  

Bonus Skills:

• Experience training machine learning models, familiarity with state-of-the-art ML model architectures.
• Deep experience with a specific aspect of machine learning for autonomous robotics (perception, prediction, or planning).
• Knowledge of C++, especially applied to safety critical systems.
• Experience with systems verification and validation techniques
• Experience with regression testing updates to already-deployed software in a safety-critical environment.
• Experience with automotive standards (E.g: ISO 12207, ISO 26262, ISO 21448)
• Experience with software quality management and assurance activities (E.g: IATF 16949, ISO 9001)
• Thorough understanding of robotics systems including sensors, actuators, mechatronics and software systems
• Work in cross collaboration teams to address problems and find solutions
  
  

Physical Demands:

While performing the duties of this job, the employee is frequently required to sit, talk, or hear. The employee is occasionally required to stand and at times for long periods; walk; use hands to finger, handle, or feel; reach with hands and arms. The employee must occasionally lift and move up to 50 pounds.

Travel Requirements (greater than 20%):
Expected to travel for 1-2 weeks each quarter visiting all of our testing sites - mostly to LV, but also domestically as needed for the business.

Working Environment

The work environment characteristics described here represent those a team member encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. The team member is regularly exposed to mechanical and computer parts. The team member is occasionally exposed to fumes and/or airborne particlesThe noise level in the environment is low to moderate. 

Motional AD LLC is an Equal Employment Opportunity employer and we welcome all qualified applicants.  All applicants will receive impartial consideration without regard to race, sex, color, national origin, age, disability, veteran status, genetic data, religion, gender identity, sexual orientation or other legally protected status.  Motional AD LLC is a Drug-free workplace.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Open to remote candidates as well as local candidates in New York, Pittsburgh, or St. Louis

The Role: 

The Patient Services Operations team is at the forefront of our mission to deliver affordable, world-class experiences for every customer. As Senior Manager, Knowledge Management (KM) & Communications, you will own the strategy, architecture, and execution of how information is created, maintained, and delivered across Patient Services — at scale. This is not a traditional KM role. We are looking for a forward-thinking leader who treats knowledge infrastructure as a product, with Artificial Intelligence (AI) and automation at its core. You will drive the evolution of how frontline teams access and apply information — moving from static documentation toward intelligent, dynamic knowledge systems that surface the right information at the right moment. In a highly regulated healthcare environment, you will ensure teams are equipped with clear, compliant, and current knowledge while leading internal communications that drive change, alignment, and continuous improvement.

What You'll Do:

Knowledge Management Strategy & AI Innovation

• Define and own the long-term vision for a modern, AI-enabled knowledge management ecosystem across Patient Services
• Lead the evaluation, implementation, and governance of AI-powered knowledge tools — including Large Language Model (LLM)-assisted content generation, intelligent search, and automated content freshness workflows
• Build a knowledge infrastructure that proactively surfaces relevant information to frontline agents in real time, reducing handle time and improving accuracy
• Partner with Product and Technology teams to integrate knowledge systems into agent-facing tools, chatbots, and customer-facing self-service experiences
• Continuously assess the emerging AI/ML landscape for opportunities to improve content creation, retrieval, personalization, and compliance monitoring

Content Development & Editorial Leadership

• Set and uphold a high editorial bar for all knowledge content — Standard Operating Procedures (SOPs), communications, training materials, and compliance documentation — ensuring content is clear, accurate, empathetic, and user-focused
• Lead a team of content developers and knowledge specialists, setting clear goals, coaching for growth, and building a culture of quality and ownership
• Own the full content lifecycle: creation, review, approval, publication, versioning, and retirement
• Develop scalable frameworks and governance models for content consistency across business units and geographies

Internal Communications

• Oversee all internal communications to Patient Services teams, including operational changes, policy updates, product launches, and leadership messaging
• Design communication strategies that drive comprehension, adoption, and alignment at scale across a distributed frontline workforce
• Measure communication effectiveness and iterate based on data and team feedback

Cross-Functional Execution & Program Management

• Lead knowledge and communications workstreams for new client and product launches — from requirements gathering through go-live and post-launch optimization
• Partner closely with Operations, Compliance, Legal, Training, Product, and Technology to ensure knowledge content meets regulatory standards and operational realities
• Define, track, and report on team Key Performance Indicators (KPIs) — content accuracy rates, time-to-publish, agent utilization, self-service deflection, and others — to drive continuous improvement
• Build scalable mechanisms to ensure global consistency and quality as the business grows

A successful applicant will fit the following criteria:

• Bachelor's degree or equivalent experience
• 7+ years of experience in knowledge management, content strategy, or related fields, with at least 3+ years managing and developing people
• Demonstrated experience leading AI or technology-forward initiatives in a knowledge, content, or operations context — including hands-on work with LLM tools, AI-assisted authoring, or intelligent search platforms
• Proven track record owning KM strategy, governance, and content lifecycle for large-scale frontline or customer service operations
• Strong editorial and written communication skills, with experience leading internal communications through complex operational or policy change
• Experience operating in regulated industries (healthcare, pharmacy, insurance, or similar), with a rigorous focus on accuracy and compliance
• Comfort working with data — defining KPIs, analyzing content performance, and translating insights into actionable improvements
• Experience with enterprise KM platforms, CMS tools, or contact center knowledge bases (e.g., Guru, Confluence, Salesforce Knowledge, or similar)
• A builder's mindset: you see knowledge infrastructure as a product, not a filing system, and you're energized by the opportunity to modernize how teams work

Onsite & Hybrid Work Requirements (Mandatory):

• Open to remote candidates as well as local candidates in New York, Pittsburgh, or St. Louis
• Remote candidates: Regular onsite travel is required. During the first 60 days, travel onsite twice per month for four consecutive days (Mon–Thu). After 60 days, travel onsite once per month for four consecutive days (Mon–Thu)
• Local candidates: Hybrid schedule with a minimum of three (3) days per week in office

About the Role:  

Veeam, following its acquisition of Securiti AI - the leader in AI-powered data security posture management (DSPM) - is seeking a Senior Sales Engineer to drive technical leadership in our sales team, focusing on the Securiti AI portfolio. 

You will guide customers from needs assessment to solution design, delivering hands-on demos and proof-of-concepts to showcase value. Success in this role requires strong technical skills and the ability to build trust with clients.

You’re already a technical leader trusted at executive levels when the conversation shifts from “we need to secure our data” to “we need to enable safe, compliant AI at scale.” 

You’re already an expert on data/AI security and a proven enterprise sales engineer. Now it’s time to join the home of Data and AI Trust. 

You’ve already seen our new release of Agent Commander, but you haven’t seen what’s coming next. After the acquisition of Securiti AI, Veeam is even further into the forefront of data resilience, protection, trust, and the acceleration of safe AI at scale.

Are you ready for an exciting new challenge at enterprise scale?

What You’ll Do: 

• Act as the primary technical contact for Securiti AI solutions during the sales cycleServe as the primary technical data protection expert for Securiti AI and Veeam solutions throughout the pre-sales cycle.
  
• Lead discovery to translate risk, architecture, and business goals into a practical solution approach.
  
• Design and deliver tailored demos and hands-on proof-of-concepts that prove value in the customer’s environment.
  
• Drive technical conversations on AI Trust, Data Discovery, DSPM, Data Access & Governance, and Compliance (including GDPR, HIPAA, PCI-DSS).
  
• Provide solution guidance across AWS, Azure, and GCP, including cloud security best practices and identity/access concepts.
  
• Partner cross-functionally with sales, product, engineering, and customer success to ensure clean technical handoff and customer confidence post-sale.
  
• Hone your professional development through hands-on experimentation, team collaboration, robust onboarding, and more.
  
• Work ~80% remote, with client-facing travel usually 1-2 short trips per month.

What You’ll Bring (required): 

• Extensive enterprise sales experience in data protection, cloud security, networks, and AI.
  
• Proven ability to quickly develop trust and credibility with both technical and business stakeholders.
  
• Strong technical leadership foundation in enterprise data protection, cloud security, networks, and AI.
  
• Hands-on experience with AWS, Azure, or GCP (multi-cloud exposure strongly valued).
  
• Deep working knowledge of data privacy/security and compliance programs, especially GDPR, HIPAA, and PCI-DSS, plus data governance and access controls.
  
• Ability to build and run compelling demos and POCs while earning trust as a customer-facing technical lead.
  
• Practical technical fluency with areas such as Linux, Kubernetes/containerized environments, and modern cloud/data architectures.
  
• Strong expertise with DSPM, DLP, data discovery approaches (e.g., patterns/keywords/dictionaries/regex/exact matching) and with common cloud/data systems (e.g., object storage and enterprise databases/warehouses).

Preferred (strong differentiators): 

• Experience applying or discussing GenAI security in real enterprise contexts, including RAG (Retrieval-Augmented Generation) concepts and vector databases / indexing approaches.
  
• Exposure to CASB / SASE / DLP technologies and how customers operationalize them.
  
• Relevant certifications (CISSP, CISM, AWS/Azure/GCP).
  

#LI-JC3

About The Role

Join the Autonomy team as a Senior Software Engineer focused on Navigation. You will be a core contributor, driving the design and deployment of real-time motion planning and navigation systems that empower our humanoid robots to operate robustly and autonomously in complex logistics and manufacturing environments. This is a high-impact role essential to scaling our commercial deployments, improving the efficiency of autonomous loco-manipulation behaviors, and achieving software readiness for the next generation robot platforms.

About The Work

• Design, implement, and deploy 3D motion planning algorithms for locomotion, with an emphasis on whole-body collision-aware motion execution in real-time.
• Own the core components of our navigation stack, specifically the local planning maps, terrain models, and grid map representations used for path and motion planning with collision avoidance.
• Advance our locomotion capabilities by developing and maintaining a 3D footstep path planner aiming to significantly reduce navigation cycle times.
• Define and implement the necessary navigation features and route planning algorithms to enable coordinated movement and resource sharing within multi-agent robot fleets.
• Drive the maturity of our release processes by designing, implementing, and maintaining robust regression testing pipelines for motion planning and navigation modules.
• Collaborate with the AI and Controls teams to integrate locomotion behaviors with RL policies and support whole-body control.
• Integrate and debug planning algorithms on real-world hardware, owning the transition from simulation environments (e.g., Gazebo, MuJoCo, Isaac Sim) to physical robots.

About You

• 5+ years of professional experience in robotics, specifically developing and deploying real-time navigation and motion planning systems for autonomous mobile platforms (humanoids, quadrupeds, autonomous vehicles).
• Expertise in 3D/volumetric map representations (e.g., octomaps, voxel grids) for local path planning and collision avoidance.
• Deep technical understanding of locomotion-specific path and motion planning algorithms, including sampling-based planners (RRT/PRM), optimization-based methods (MPC/LQR), and hybrid A*.
• Expert proficiency in modern C++ (C++17/20), with a proven track record of writing high-performance, multithreaded code for robotics applications.
• Experience with common robotics frameworks (e.g., ROS/ROS2, DDS) and hands-on experience with modern optimization libraries relevant to motion planning (e.g., Ceres, IPOPT, OSQP).
• Proven ability to systematically test and debug systems on physical robots, and integrate perceived environment data (LiDAR, camera, depth sensing) into the planner.

Bonus Qualifications

• Experience training and deploying Reinforcement Learning (RL) agents for complex locomotion behaviors.
• Hands-on experience implementing Model Predictive Control (MPC) or similar optimization-based control techniques for dynamic robot locomotion.
• Familiarity with perception pipelines and the integration of perceived environment data into the planning stack.
• Experience with GPU-accelerated spatial data structures (e.g., NVBlox, specialized CUDA implementations) for high-throughput, low-latency map updates and querying.
• Strong foundational knowledge of robot kinematics, dynamics, controls, and state estimation (e.g., EKF, particle filters).
• Experience with multi-robot coordination/route planning and abiding by boundary constraints in a workcell map.
• Experience with multi-robot mapping and localization, including map persistence and sharing capabilities.
• Publications in top-tier robotics conferences (ICRA, RSS, IROS, CoRL).

This a hybrid position based out of one of our Salem, Pittsburgh, or Fremont offices.

The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future.

About Fetch

At Fetch, we’re dedicated to helping pets live their healthiest and happiest lives. Our comprehensive insurance coverage is designed with modern pet parents in mind, and we’re proud to support the animal shelter community. We believe in ensuring pets receive the best care possible and are committed to making that vision a reality every day.

Fetch is a high-growth Warburg Pincus portfolio company with an expanding team of over 350 pet-loving employees working together to shape the future of pet health and wellness.

About the Industry

The pet insurance industry is more important than ever, offering peace of mind and financial protection for pet owners. The sector is expanding quickly, fueled by growing awareness of the need for accessible, high-quality veterinary care. With advances in veterinary medicine, pets now have access to the most effective treatments available, making pet insurance an essential component of modern pet care.

Job Overview 

Property & Casualty License Preferred 

We are currently seeking a Pet Insurance Sales Associate to join our amazing team at the Humane Animal Rescue of Pittsburgh (North Side Campus) in Pittsburgh, PA. Successful candidates will be goal-oriented, focused on converting opportunities into sales, while delighting customers. You are someone that has a passion for furry friends and wants to help their human companions understand the value of protecting them with the most comprehensive pet insurance available. You will have a customer-first mindset, listening to the customer’s needs and working with them to create the best outcome.

REQUIREMENTS

• Representing Fetch as a confident, friendly, trusted advisor to sell pet insurance policies to prospective pet parents at our partner location in the Pittsburg area
• Communicating competitive advantages of Fetch; demonstrating a deep understanding of Fetch and competitor pet health insurance products
• Turning prospects into loyal clients, raving fans, and repeat customers
• Accurately recording prospect and customer information in Fetch CRM
• Establishing productive, professional relationships with key personnel at our partner locations to assist in meeting performance objectives and partner expectations monthly
• Proactively assessing partner needs on an ongoing basis through onsite focus and follow-up meetings
• Collaborating with leadership to identify opportunities for new business
• Communicating with your peers via chat to identify challenges and successes
• Virtually attending monthly company meetings or check-ins as required
• Managing expense budget and submitting completed reports monthly
• Managing all administrative tasks and responsibilities relative to the partnership
• Living up to Fetch’s commitment to continuously exceed customer expectations

SKILLS

• Ability to structure your work week during peak hours a must (Friday-Sunday) when it is most lucrative for the agent
• Active Property & Casualty (P&C) license or willing to obtain a P&C license at the expense of the Company within 30 days of employment (subject to state requirements)
• Proven self-starter with 3-5 years of in an animal care role
• Energized by being an industry pioneer
• Passion for prospecting new sales opportunities on a daily basis (must enjoy speaking with people face to face)
• Familiar with animal health or animal welfare a plus, but not required
• Ability to think and act independently within a fast-paced sales cycle
• Proven success in building relationships using a consultative, solution-focused approach
• Demonstrated customer service skills and the ability to understand Fetch's customers’ needs
• Must be willing to travel to various industry events as required
• Excellent verbal, interpersonal and written communication skills
• Excellent team player; proven ability to apply innovative ideas and critical thinking

Professional Traits that are not unique to this position, but necessary for Fetch employees:

• Exhibits excellent business judgment
• Positive attitude
• Sets the bar high for team standards 
• Is action and results-oriented and self-reliant

Compensation

• The pay range for this position is $55,000 - $90,000 on a full-time basis
• Along with base salary, your position may qualify for additional bonuses
• This position is eligible for the Company’s bonus plan(s)

Benefits & Perks

At Fetch, we recognize the importance of work-life balance and prioritize our employees' mental health and well-being, ensuring everyone can flourish both professionally and personally. Not just pets, we want our employees to live their best lives too — here at Fetch, you have access to the valuable benefits listed below. 

• Comprehensive Medical, dental, and vision plan for you and your family
• Health Savings Accounts (HSAs) and Flexible Spending Accounts (FSAs) are available 
• Highly competitive 401(k) matching 
• Generous 20-day PTO Policy, with rollover options. Earn an additional day of PTO each year on your anniversary with Fetch, for a maximum of 30 days
• Paid company (9) holidays, including (1) floating holiday
• Fetch Pet Insurance discount - up to 50% off, up to $1,000 savings/year
• Educational Assistance Program
• Fetch Discount Perks Program
• Volunteering - earn up to 8 hours per calendar year at nonprofit organizations
• NYC Office Amenities: Pet-friendly environment, free lunch, snacks, and additional amenities; transit accessible
• Employee Referral Incentive
• Tuition Assistance
• Commuter Benefits
• Employee Assistance Program (EAP)