What we are looking for

Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all.

We're searching for a Staff Security Platform Engineer to join our Enterprise Security Engineering team, reporting to the Technical Lead Manager of Security Engineering.

Aurora is scaling its autonomous trucking operations, and we need someone who makes our security tools actually work, not just deployed, but deeply configured, continuously tuned, and fully leveraged. This role is for the practitioner who has spent their career living inside security platforms: the person who knows their EDR better than the vendor's own support team, who can write a SIEM query from memory, and who instinctively knows when an alert is misfiring and exactly why.

This is not a software engineering role. It's a role for an elite security operator — someone with the instincts of a seasoned SOC analyst and the technical depth to own the platforms that power detection, response, and protection at enterprise scale. If you find deep satisfaction in mastering a tool, closing a coverage gap, or hunting down a threat that nobody else noticed, this role was written for you.

In this role you will

• Own the operational health, configuration, and continuous improvement of Aurora's enterprise security platform stack — including EDR/XDR, MDM, SIEM, DLP, IAM/IGA, DNS security, Email security, and PKI — ensuring each tool is tuned, policy-complete, and delivering reliable signal.
• Develop and refine detection rules, correlation logic, and alert policies, reducing noise while ensuring Aurora maintains high-fidelity coverage against real threats.
• Conduct proactive threat hunting across Aurora's security telemetry — forming hypotheses, querying logs, and investigating anomalies before they surface as incidents.
• Serve as the deepest internal expert on Aurora's enterprise security tooling, acting as the escalation point for complex platform issues, misconfigurations, and detection failures.
• Participate in the team's on-call rotation, leading deep-dive investigations into security alerts and incidents and driving triage, containment, and root cause analysis.
• Continuously audit and validate that existing security controls are configured to actually do what they're supposed to do — not just deployed and forgotten.
• Maintain operational runbooks, detection documentation, and platform configuration records, ensuring the team can operate consistently and scale institutional knowledge.

Required qualifications

• 12+ years of hands-on experience in enterprise security operations, security platform administration, or a senior SOC engineering role — with a career built on deep operational ownership of security tooling rather than software development.
• Expert-level proficiency administering and operating at least two enterprise security platforms (e.g., CrowdStrike, SentinelOne, Splunk, Panther, Sentinel, Jamf, Kandji/Iru, Puppet, WorkspaceONE, Intune, Zscaler, Okta, Proofpoint, Wiz, osquery), with strong working knowledge across several others.
• Demonstrated ability to tune and optimize security platforms beyond out-of-the-box configurations — writing custom detection logic, adjusting policy sets, and validating control effectiveness.
• Strong log analysis and threat hunting skills: you know how to build a hypothesis, write the query, follow the thread, and know when to escalate.
• Experience conducting thorough incident investigations — triage, containment, root cause analysis, and post-incident review — and communicating findings clearly to technical and non-technical stakeholders.
• Ability to assess security control effectiveness: not just "is this tool deployed" but "is it configured correctly, covering the right scope, and generating actionable signal."
• Comfort working under pressure in ambiguous, fast-moving situations with competing priorities.

Desirable qualifications

• Scripting ability for automation, log parsing, or workflow improvement (Python, Bash, or similar) — you don't need to be a software engineer, but you can write a script when it saves you an hour.
• Deep familiarity with MITRE ATT&CK as an operational tool for detection gap analysis and threat hunting hypothesis development.
• Experience with AWS security telemetry (CloudTrail, GuardDuty, Security Hub) and integrating cloud signals into a corporate SIEM.
• Familiarity with Zero Trust and identity-centric security models as they apply to policy enforcement in IAM and endpoint platforms.
• Platform-specific certifications such as CrowdStrike Certified Falcon Administrator, Splunk Core Certified Power User, or equivalent — or practitioner certifications like GCIH, GCIA, GCFE, or GCFA.

The base salary range for this position is $171,000 - $247,000 per Year. Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits.

What we are looking for

Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all.

We're searching for a Staff Security Engineer to join our Enterprise Security Engineering team, reporting to the Technical Lead Manager of Security Engineering.

Aurora is scaling its autonomous trucking operations, and the security foundations protecting our employees, devices, internal systems, and data need to scale with us. We're looking for a deeply technical enterprise security engineer who is as comfortable in a code editor as in a security console — you build the systems that make Aurora's security operations possible. This is a role for someone who can architect robust enterprise security solutions, write the code to implement them, and partner across Engineering and IT to make sure security is embedded from the start, not bolted on at the end.

This is not a security operations-only role. It sits squarely in enterprise security engineering, where the work is designing, building, and owning the platforms, integrations, and automation that power Aurora's internal security posture at scale.

In this role you will

• Own the architecture and implementation of Aurora's enterprise security controls — designing the systems and integrations that protect Aurora's endpoints, identities, internal infrastructure, and SaaS environment.
• Design and build Aurora's security telemetry and detection infrastructure, including log pipelines, SIEM integrations, and alerting frameworks — in partnership with the Security Operations Engineer who owns ongoing tuning and rule development.
• Define and enforce enterprise security standards, conducting architecture and design reviews to ensure alignment with Aurora's security posture and risk tolerance.
• Partner with IT, Infrastructure, and Engineering teams to embed security requirements early — shifting left on corporate IT initiatives before they become technical debt.
• Build automation and tooling that extends the capabilities of Aurora's security platforms, reduces manual operational burden, and scales the team's impact.
• Serve as the escalation point for enterprise security incidents requiring engineering-level investigation or remediation, and participate in the team's on-call rotation.
• Translate security strategy into concrete, executable engineering projects with clear milestones and measurable outcomes.

Required qualifications

• 12+ years of hands-on experience in enterprise security engineering or corporate information security — specifically securing employee-facing systems, endpoints, identities, and internal infrastructure (not product or application security).
• Proficiency in at least one programming language, used in a security context — writing production-quality automation, integrations, or internal tooling (the team primarily uses Go; Python is also common).
• Experience architecting and integrating enterprise security platforms — designing API integrations, automating workflows, and building tooling that extends platform capabilities across domains such as EDR/XDR, MDM, IAM/IGA, DLP, SaaS security, cloud security, or PKI.
• Experience designing and building security telemetry pipelines and detection infrastructure — log ingestion, normalization, SIEM integration, and alerting architecture.
• Experience leading cross-functional security engineering projects — defining scope, driving execution, and aligning stakeholders across Engineering and IT.
• Track record of conducting security architecture reviews and translating findings into actionable, risk-prioritized remediation plans.
• Experience evaluating security posture and identifying systemic gaps, with a bias toward building durable solutions rather than one-off fixes.

Desirable qualifications

• Experience with Zero Trust architecture and identity-centric security models (BeyondCorp-style or similar).
• Familiarity with NIST CSF, MITRE ATT&CK, and CIS Benchmarks as engineering inputs — used to inform what to build and how to validate it, not just as compliance checkboxes.
• Hands-on AWS security experience (SCPs, GuardDuty, Security Hub, IAM, etc.) and familiarity with integrating cloud security signals into a corporate security platform.
• Experience with applied cryptography and PKI in a production enterprise environment — certificate lifecycle management, CA design, or secrets management.
• Familiarity with securing AI/ML platforms or applications built on LLMs, RAG pipelines, or MCP-based architectures.
• Security certifications such as CISSP, GCED, GREM, or similar (valued but not required).

The base salary range for this position is $171,000 - $247,000 per Year. Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits.

#Mid-Senior 

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.  

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

At Latitude, the Overwatch team builds the systems that run and manage large amounts of computing work across the organization. This work helps engineers train models, test software, and move data reliably at scale. The team owns the platforms that schedule and monitor long-running jobs and keep shared computing resources running efficiently. In addition, the team develops systems that deliver software and data to vehicles and collect logs and telemetry from real-world driving.

What you’ll do: 

• Build and improve systems that orchestrate, schedule, run, and monitor large numbers of ML jobs used across the company for training automated driving features
• Design reliable software that handles failures, retries, and high load while keeping results accurate and timely
• Build and maintain backend services and APIs that support job submission and reliable execution of long-running work
• Investigate production issues, identify root causes, and implement fixes that prevent repeat failures
• Review designs and code to ensure solutions are simple, reliable, and scalable over time
• Collaborate closely with infrastructure, machine learning and data science teams to ensure computing systems are reliable and being optimally used
• Build with scale and maintainability in mind, while rapidly iterating on new ideas

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 4+ years of relevant experience (or Master's degree and 2+ years of relevant experience, or PhD)
• Experience in designing high-scale distributed systems that meet user requirements
• Prior experience building or operating large-scale job scheduling or workload orchestration platforms, such as container-based or batch computing systems used to manage shared compute resources
• Expertise in back-end languages (e.g., Kotlin, Java, Go, Python) and frameworks
• Experience designing and building distributed systems using modern backend patterns for reliability, coordination, and asynchronous processing
• At least 6+ years of experience in a related field where actively coding & designing 80% of the time
• Comfort working in a fast-moving environment with evolving requirements, where ideas are tested quickly and real user impact guides decisions

Nice to have: 

• Exposure to performance tuning, cost optimization, or capacity planning for compute-intensive workloads
• Experience working on large-scale computing platforms that manage shared resources and competing workloads

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 15 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $179,200 - $268,800 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.  

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The Autonomy Analytics team is responsible for evaluating the performance of our autonomous software using data, statistics, and machine learning. Combining data from real-world testing with our extensive simulations, our team builds the metrics and analytics that ensure our product is safe, reliable, and delights our customers. 

Our team is spread geographically with some members on-site in Pittsburgh, Palo Alto, and Dearborn. Others work fully remotely.

What you’ll do: 

• Translate loosely defined questions into clear requirements, metric definitions, and analytical deliverables
• Partner closely with software development teams, test engineering, product, and analytics partners to understand needs and deliver trusted data products
• Build and maintain scalable data models in Google BigQuery and similar cloud data platforms to support reporting, analysis, and operational decision-making
• Develop dashboards and lightweight data applications in Apache Superset, Plotly Dash, or similar tools for technical and business stakeholders
• Investigate source data, identify quality issues, and implement durable fixes that improve accuracy, consistency, and reliability
• Create reusable datasets, testing practices, and documentation that strengthen trust in data and improve self-service access
• Balance near-term dashboard development with long-term data modeling work to support both speed and scale
• Communicate findings, assumptions, and tradeoffs clearly to internal stakeholders and external platform or data partners as needed

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 4+ years of relevant experience (or Master's degree and 2+ years of relevant experience, or PhD)
• Experience building and maintaining analytics-ready data models in Google BigQuery or a similar cloud data warehouse
• Experience using dbt or a similar data transformation framework to develop tested, documented, and maintainable data pipelines
• Experience designing dashboards and analytical applications in Apache Superset, Tableau, or similar reporting and visualization tools
• Strong SQL skills, including complex transformations, data validation, and query optimization
• Experience with Python for data analysis and application development, including the ability to contribute to and maintain Plotly Dash dashboards
• Demonstrated ability to succeed in ambiguous environments by defining requirements, shaping metrics, and creating structure from incomplete problem statements
• Experience partnering with software development, test engineering, product, or similar cross-functional teams to deliver measurable outcomes
• Strong foundation in data modeling, dashboard design, documentation, and quality assurance practices
• Strong written and verbal communication skills with the ability to explain analytical concepts and findings to varied audiences

Nice to have: 

• Experience supporting autonomous vehicle development or other complex hardware and software products
• Experience working with telemetry, operational, or product usage data at scale
• Experience building shared metrics and self-service reporting solutions for engineering and product organizations
• Experience with front-end technologies such as HTML, CSS, JavaScript, or React to support customization and enhancement of data applications and dashboards

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 15 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $179,200 - $268,800 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

The Team

This role can sit in our NYC HQ on a hybrid basis, or it can be fully remote while working from a location based in either Eastern or Central time zones. We are looking for an experienced Senior Engineer for our SRE, Atlas team to support, maintain and grow the Atlas platform. As a senior SRE, you will be expected to be able to design & build complex systems, operate with autonomy and act as owner for everything you do. 

The SRE Atlas team works alongside the various Atlas software engineering teams to provide expertise about running systems at scale, build new tooling and automation and perform essential maintenance of the Atlas fleet. 

This is an SRE team, which means you can expect a highly hands-on approach, tackling the technical challenges of implementing large scale solutions that have the ability to impact our customer’s most crucial workloads. 

Role Overview

We are seeking a talented Site Reliability Engineer (SRE) with a strong infrastructure background. This role requires engineers to have a customer-first mindset to ensure that everything we do results in a stronger product and a better experience for all Atlas customers. 

The ideal candidate should

• Have 5+ years of experience running critical systems at scale
• Value efficiency in processes and operations, and display a preference for automation over manual processes (“allergic to ops work”)
• Be familiar with a major cloud provider (AWS, Azure, or GCP) and possess the ability to build and operate systems in a multi-cloud environment
• A strong understanding of how to run a large scale Linux environment, including low level fundamentals 
• Firm grasp of at least one modern programming language, beyond basic scripting (Go, Ruby, Python)
• Solid understanding of web and network protocols and standards (HTTP, TLS, DNS, etc)

Special Requirements:

• Be a US Citizen

Expectations

• Participate in the development of a reliable and resilient multi-cloud platform that hosts business critical applications for a wide & varied range of customer applications
• Collaborate with service-owning teams to provide internal support, solve technical challenges and adapt or build tooling to solve novel use cases in a generic fashion
• Participate in a 24/7 on-call rotation to swiftly resolve issues related to any disruption of our customer facing Atlas fleet, ensuring minimal disruption and high availability

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 nearly 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, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Req ID: 426187

MongoDB’s Storage Layer Services (SLS) team is re-architecting the MongoDB cloud storage layer and sits at the heart of our next-generation cloud storage architecture. This relatively new team is building performant, multi-tenant distributed storage services that both enhance today’s Atlas storage stack and enable more customer workloads to run more efficiently.

You will partner with the teams building these storage services to define SLOs, shape capacity plans, and ensure the reliability, durability, and operational safety of the storage layer that underpins Atlas. You’ll join a small, senior team of SREs as founding members of this organization, playing a crucial role in executing on a multi-year roadmap for MongoDB’s cloud storage architecture.

This role can be based out of our Boston, New York City, Raleigh, Miami, Pittsburgh or remotely in the United States while physically based in an Eastern or Central time zone location. 

The ideal candidate should

• Have 6+ years of experience working on software development and operating distributed systems
• Proficiency in Python, Go, or a similar language
• Have operated or supported stateful storage or database systems at scale, and are comfortable with durability, consistency, and recovery trade-offs.
• Possess a customer-focused mindset
• Value efficiency in processes and operations
• Prefer automation over manual processes. We are a small team of software engineers with a strong bias towards software solutions to avoid toil
• Experience using and extending containerization technologies, particularly Kubernetes, to enhance application agility, optimize resource utilization, and accelerate time-to-market
• Expertise in cloud infrastructure platforms, including AWS, Google Cloud Platform (GCP), or Azure
• Understanding of Linux operating system internals and networking concepts (e.g., TCP/IP, DNS, TLS, routing)

Responsibilities 

• Work on our multi-tenant distributed storage systems, balancing long-term strategic infrastructure goals with immediate engineering needs
• Build for reliability, making services and infrastructure available, resilient, fault-tolerant, and self-healing
• Identify and configure key metrics to detect incidents and quantify service health, availability, and performance
• Participate in a 24/7 on-call rotation to resolve issues involving the storage infrastructure
• Become an expert in infrastructure performance, helping us optimize from the application level all the way to the kernel

Strong candidates may also have experience with:

• Leading major architectural shifts, such as moving from legacy storage stacks to new multi-tenant storage architectures, including planning and executing large-scale data and workload migrations with tight availability and durability requirements
• Managing and scaling infrastructure across multi-cloud environments (AWS, GCP, or Azure)
• Designing secure, multi-tenant runtime environments at scale

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, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Req ID: 1273396252

What we are looking for

Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all.

We're searching for a Staff Client Platform Engineer to join our Enterprise Site Reliability (SRE) team to lead the management of our enterprise Windows, Mac, and Ubuntu clients. The team is responsible for designing, implementing, and maintaining scalable and secure client device infrastructure. They also automate device deployment, configuration, and management processes.

This role will contribute to delivering the benefits of self-driving by leading the development of tailored client device solutions that address Aurora-wide and team-specific requirements for software and hardware development and manufacturing.

In this role you will

• Guide the approach and execution of the Client Platform Engineering Team
• Plan, design, and implement scalable solutions to manage Aurora's physical and virtual client platforms (Macs, Windows, Ubuntu) to ensure they receive security patches, configuration, and software package updates
• Partner with IT Support, InfoSec, and Developer Experience on strategy and implementation of client platform management solutions
• Handle escalations from IT Support, with an emphasis on mentoring and empowering IT Support to address similar problems
• Participate in the Enterprise SRE 24/7 on-call rotation

Required qualifications

• Experience of at least 8 years in Client Platform engineering and at least 3 years in leading engineering solutions (design to implementation) at scale.
• Expertise with client device lifecycle management, including experience across multiple client operating systems and various hardware platforms.
• Experience with MacOS, Windows, and Linux system administration.
• Experience deploying configuration management tools such as Intune, Kandji, Puppet, Ansible, or Chef.
• Experience with automation and tool development, preferably using Powershell, Go, or Python.
• Strong change management skills, including rollout planning and communication, and collaborating with stakeholders.
• Understanding of client endpoint security concepts and experience applying security hardening to client devices, such as CIS Benchmarks.
• Outstanding written and verbal communication skills that drive executional impact at scale. 
• Experience with automating testing for imaging, provisioning, and configuration management.

Desirable qualifications

• Experience working within cloud environments such as AWS, GCP, or Azure.
• Experience in environments deploying containerized workflows, using tools such as Docker or Kubernetes.
• Familiarity with SRE concepts such as SLIs, SLOs, SLAs, incident response, and best practices and partnering with teams on them.
• Experience with Infrastructure as Code (IaC) tools such as Terraform, Pulumi, or CloudFormation

The base salary range for this position is  $163,000 - $236,500 per Year. Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits.

Position Overview

Woolpert is hiring a Senior Software Engineer to join our dynamic Atlas Labs team supporting our Geospatial sector. This position reports to the Director of Product and AI. Atlas Labs oversees technology development for Woolpert’s Geospatial sector, including various types of AI model development, R&D, software infrastructure, and user-facing products.

We are unable to provide visa sponsorship for this position.  Applicants must already possess work authorization in the country the job is located.

What You Will Do:

• Contribute to the planning, research, design, architecture, development, and testing of Atlas Labs software features. You’ll write code daily, definitely in Python and possibly in C++ or JavaScript.
• Work as part of a cross-functional Scrum team with domain experts in AI/ML, Geospatial Data, Software Engineering, and GIS.
• Collaborate with internal and external users to ensure the correctness, quality, and availability of Atlas Labs software products.

What You Will Bring:

• Meaningful employment experience working on commercial software products
• Experience writing production code in Python, preferably dealing with large datasets
• Demonstrated experience or academic output in a relevant domain:
• Geospatial Data Processing
• Computer Vision
• AI & Machine Learning
• LiDAR
• Robotics/Self-Driving/Autonomy
• General familiarity with relevant technologies:
• Linux, Docker, and GitHub
• AI/ML frameworks (Tensorflow, Pytorch)
• Cloud platforms (Azure, AWS, Google)

Nice to Have:

• Familiarity with GIS applications like ESRI, QGIS, Google Earth
• Familiarity with orchestration frameworks like Kubernetes, AirFlow, MLFlow
• Programming experience in C++ or JavaScript
• Experience with Agile processes like Scrum

Benefits You Will Love

Certified as a Great Place to Work, Woolpert is one of the fastest-growing architecture, engineering, and geospatial companies in the world. With over 75 offices and countless projects around the globe, you’ll work on meaningful assignments that improve quality of life around the world. In addition to offering competitive pay, we provide employees with a robust set of unique benefits, including:

• Freedom to Work program: Set your own schedule and location (as appropriate).
• Principal program: Earn the opportunity to become an owner of the firm.
• Flexible paid time off/vacation: Take time off when it makes sense for you and your team.
• Career development: Explore a wide range of learning and growth opportunities within and across industries.
• Health, life, vision, and dental insurance: Cover all your medical bases.
• Paid sick time, retirement plan, fitness reimbursement, pet insurance, identity protection, technology stipend, employee assistance program, and more!

#LI-Hybrid

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The hardware sensing team is responsible for the benchmarking, characterizing, and nomination and integration of all sensors. We work cross functionally with systems, perceptions, vehicle integration to ensure the sensors meet Latitude’s ODD requirements, perception requirements, and that it is integrable and reliable in Ford’s series vehicles. We are expected to understand sensing from a first principle approach and to ensure those principles are designable and manufacturable.  We keep a breadth of all sensing technology, sensing vendors, and integration technologies. Members of the team are expected to be highly multidisciplinary, as successful sensor integration requires expert understanding and comfortability of concepts across many disciplines–from hardware and software.

What you’ll do: 

• Lead technical development of radar or lidar sensing systems from early concept, benchmarking, and sourcing through production launch
• Define sensor performance targets, requirements, evaluation criteria, and validation plans based on vehicle, perception, and ODD needs
• Drive sensor benchmarking and technical down-selection across candidate suppliers and technologies
• Lead supplier technical engagement, including requirements reviews, data reviews, architecture discussions, issue resolution, and design maturity assessments
• Guide architecture and tradeoff decisions related to sensing performance, environmental robustness, packaging, thermal constraints, power, diagnostics, manufacturability, and cost
• Work cross-functionally with systems, perception, firmware, validation, vehicle integration, manufacturing, and quality teams to ensure the sensor meets downstream functional and program needs
• Review test data from supplier, bench, track, and vehicle testing; identify performance gaps; determine root cause; and drive corrective actions
• Assess whether supplier evidence is technically credible by reviewing test conditions, assumptions, filtering, sample quality, boundary cases, repeatability, and alignment to sensor physics and system requirements
• Lead technical reviews and challenge unsupported claims, weak correlation, incomplete validation, and conclusions that do not match observed data or first-principles behavior
• Establish technical plans, milestones, risks, and mitigation strategies to keep sensor programs on track toward nomination and launch
• Communicate technical status, open issues, and tradeoffs clearly to engineering leadership and cross-functional partners

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 7+ years of relevant experience (or Master's degree and 5+ years of relevant experience, or PhD and 2+ years of relevant experience)
• 8+ years of experience in automotive sensing, with deep hands-on expertise in either radar or lidar and demonstrated technical leadership on complex hardware programs
• Strong first-principles understanding of the relevant sensing modality:
  • Radar: RF fundamentals, range/Doppler/angle estimation, resolution, interference, multipath, ghosting, calibration, synchronization, and environmental effects
  • Lidar: time-of-flight or FMCW fundamentals, ranging accuracy, reflectivity effects, point cloud quality, contamination, weather impacts, optical alignment, calibration, and environmental robustness
• Strong scripting and analysis capability in python, including hands-on use of NumPy, pandas, Jupyter, and similar tools to parse logs, evaluate sensor performance, trend issues, and build repeatable analysis workflows
• Ability to work directly with sensor data and debug artifacts, including supplier logs, raw captures, decoded outputs, and vehicle test datasets, and determine whether the data is complete, valid, and physically credible
• Practical experience with day-to-day engineering tools such as Wireshark/PCAP analysis, CAN tools (CANoe/CANalyzer or similar), ROS/rosbag or equivalent logging frameworks, point cloud or radar visualization tools, and common automotive data interfaces such as CAN, CAN FD, and automotive Ethernet
• Ability to independently judge whether supplier test results are correct by evaluating test setup, calibration state, ground truth method, statistical significance, repeatability, boundary conditions, applied filters, KPI definitions, and consistency with known sensor behavior
• Ability to lead and critically assess technical design reviews (TDRs) by identifying weak assumptions, incomplete validation, inconsistent requirements traceability, misleading metrics, and conclusions that are not supported by the underlying data
• Experience translating system and perception needs into sensor requirements, validation methods, and supplier deliverables
• Experience working across hardware, firmware, systems, perception, validation, manufacturing, and supplier teams to drive root cause and resolution
• Strong technical judgment and communication skills, with the ability to make and defend tradeoffs across performance, cost, timing, integration, and reliability

Nice to have: 

• Master’s degree or PhD in Electrical Engineering, Physics, Robotics, Computer Science, or a related field
• Experience leading supplier nomination, sourcing, or technical selection for radar or lidar hardware
• Experience bringing radar or lidar systems into automotive production
• Familiarity with DV/PV, DFMEA, APQP, PPAP, functional safety, automotive quality processes, and production release expectations
• Working knowledge of perception, tracking, calibration, and downstream use of radar or lidar data in automated driving systems
• Experience supporting multiple vehicle platforms or series-production programs

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 15 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $191,920 - $287,880 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The hardware sensing team is responsible for the benchmarking, characterizing, and nomination and integration of all sensors. We work cross functionally with systems, perceptions, vehicle integration to ensure the sensors meet Latitude’s ODD requirements, perception requirements, and that it is integrable and reliable in Ford’s series vehicles. We are expected to understand sensing from a first principle approach and to ensure those principles are designable and manufacturable.  We keep a breadth of all sensing technology, sensing vendors, and integration technologies. Members of the team are expected to be highly multidisciplinary, as successful sensor integration requires expert understanding and comfortability of concepts across many disciplines–from hardware and software.

What you’ll do: 

• Lead technical development of radar or lidar sensing systems from early concept, benchmarking, and sourcing through production launch
• Define sensor performance targets, requirements, evaluation criteria, and validation plans based on vehicle, perception, and ODD needs
• Drive sensor benchmarking and technical down-selection across candidate suppliers and technologies
• Lead supplier technical engagement, including requirements reviews, data reviews, architecture discussions, issue resolution, and design maturity assessments
• Guide architecture and tradeoff decisions related to sensing performance, environmental robustness, packaging, thermal constraints, power, diagnostics, manufacturability, and cost
• Work cross-functionally with systems, perception, firmware, validation, vehicle integration, manufacturing, and quality teams to ensure the sensor meets downstream functional and program needs
• Review test data from supplier, bench, track, and vehicle testing; identify performance gaps; determine root cause; and drive corrective actions
• Assess whether supplier evidence is technically credible by reviewing test conditions, assumptions, filtering, sample quality, boundary cases, repeatability, and alignment to sensor physics and system requirements
• Lead technical reviews and challenge unsupported claims, weak correlation, incomplete validation, and conclusions that do not match observed data or first-principles behavior
• Establish technical plans, milestones, risks, and mitigation strategies to keep sensor programs on track toward nomination and launch
• Communicate technical status, open issues, and tradeoffs clearly to engineering leadership and cross-functional partners

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 7+ years of relevant experience (or Master's degree and 5+ years of relevant experience, or PhD and 2+ years of relevant experience)
• 8+ years of experience in automotive sensing, with deep hands-on expertise in either radar or lidar and demonstrated technical leadership on complex hardware programs
• Strong first-principles understanding of the relevant sensing modality:
  • Radar: RF fundamentals, range/Doppler/angle estimation, resolution, interference, multipath, ghosting, calibration, synchronization, and environmental effects
  • Lidar: time-of-flight or FMCW fundamentals, ranging accuracy, reflectivity effects, point cloud quality, contamination, weather impacts, optical alignment, calibration, and environmental robustness
• Strong scripting and analysis capability in python, including hands-on use of NumPy, pandas, Jupyter, and similar tools to parse logs, evaluate sensor performance, trend issues, and build repeatable analysis workflows
• Ability to work directly with sensor data and debug artifacts, including supplier logs, raw captures, decoded outputs, and vehicle test datasets, and determine whether the data is complete, valid, and physically credible
• Practical experience with day-to-day engineering tools such as Wireshark/PCAP analysis, CAN tools (CANoe/CANalyzer or similar), ROS/rosbag or equivalent logging frameworks, point cloud or radar visualization tools, and common automotive data interfaces such as CAN, CAN FD, and automotive Ethernet
• Ability to independently judge whether supplier test results are correct by evaluating test setup, calibration state, ground truth method, statistical significance, repeatability, boundary conditions, applied filters, KPI definitions, and consistency with known sensor behavior
• Ability to lead and critically assess technical design reviews (TDRs) by identifying weak assumptions, incomplete validation, inconsistent requirements traceability, misleading metrics, and conclusions that are not supported by the underlying data
• Experience translating system and perception needs into sensor requirements, validation methods, and supplier deliverables
• Experience working across hardware, firmware, systems, perception, validation, manufacturing, and supplier teams to drive root cause and resolution
• Strong technical judgment and communication skills, with the ability to make and defend tradeoffs across performance, cost, timing, integration, and reliability

Nice to have: 

• Master’s degree or PhD in Electrical Engineering, Physics, Robotics, Computer Science, or a related field
• Experience leading supplier nomination, sourcing, or technical selection for radar or lidar hardware
• Experience bringing radar or lidar systems into automotive production
• Familiarity with DV/PV, DFMEA, APQP, PPAP, functional safety, automotive quality processes, and production release expectations
• Working knowledge of perception, tracking, calibration, and downstream use of radar or lidar data in automated driving systems
• Experience supporting multiple vehicle platforms or series-production programs

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 15 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $191,920 - $287,880 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The hardware sensing team is responsible for the benchmarking, characterizing, and nomination and integration of all sensors. We work cross functionally with systems, perceptions, vehicle integration to ensure the sensors meet Latitude’s ODD requirements, perception requirements, and that it is integrable and reliable in Ford’s series vehicles. We are expected to understand sensing from a first principle approach and to ensure those principles are designable and manufacturable.  We keep a breadth of all sensing technology, sensing vendors, and integration technologies. Members of the team are expected to be highly multidisciplinary, as successful sensor integration requires expert understanding and comfortability of concepts across many disciplines–from hardware and software.

What you’ll do: 

• Test Strategy & Compliance: Lead the development of Design Verification (DV) and Production Validation (PV) plans. Ensure all hardware-level testing aligns with Ford automotive standards (CETP) and international standards such as ISO 16750 and ISO 26262
• Sensor Validation: Execute and oversee comprehensive testing for sensing modalities including Camera (optics/image quality), Radar (RF performance), Lidar (point cloud accuracy), IMU, and GPS/GNSS
• Reliability Engineering: Conduct environmental and mechanical stress testing (thermal cycling, vibration/shock, humidity, ingress protection, and EMC/EMI) to determine Mean Time To Failure (MTTF) and ensure 10-year/150,000-mile durability
• Vendor & Lab Management: Act as the primary technical point of contact for Tier 1 suppliers and external test labs. Manage the statement of work (SOW), audit lab setups, and ensure external testing is executed to Ford’s precision requirements
• Test Automation & Scripting: Develop and maintain automated test scripts (Python, MATLAB, or Bash) to streamline data acquisition, sensor calibration, and post-processing of large datasets
• Data Analysis & Reporting: Review and interpret complex test results. Perform root cause analysis (RCA) on hardware failures and work closely with design teams to implement hardware revisions
• Cross-Functional Leadership: Mentor junior engineers and collaborate with Systems Engineering, Perception Teams, and Manufacturing to ensure sensor hardware integrates seamlessly into the vehicle platform

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 4+ years of relevant experience (or Master's degree and 2+ years of relevant experience, or PhD)
• Minimum of seven years of professional experience in hardware testing or reliability engineering.
• Extensive experience testing sensing technologies such as cameras, radar, lidar
• Proficiency in writing scripts using Python or similar languages for data analysis and automation.
• Demonstrated ability to manage projects involving external laboratories and global suppliers.
• Expertise in applying standardized automotive engineering test procedures for environmental and mechanical durability.
• Strong analytical skills for identifying the root cause of hardware malfunctions in automotive electronics.

Nice to have: 

• Direct experience in hardware product development for automotive with a focus on sensing products
• Direct experience/exposure to product development of silicon products such as transceivers, imagers, MCUs
• Manufacturing or industrialization of electronics products

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 15 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $160,000 - $240,000 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

THE ROLE:

Are you passionate about driving innovation in the data storage industry? Are you skilled in crafting technical solutions that exceed customer expectations? If so, we have an exciting opportunity for you! Everpure (formerly Pure Storage), a leader in the data storage and flash technology space, is seeking a talented and motivated Senior Pre-Sales Systems Engineer to join our dynamic team. 

As a Senior Pre-Sales Systems Engineer, you will play a crucial role in understanding our customers' unique challenges and tailoring Everpure solutions to meet their specific needs. Collaborating closely with the sales team, you will act as a technical expert during the sales process, helping to showcase the value of our products and services.

WHAT YOU’LL DO:

• Develop an exhaustive understanding of what drives a customer’s business and what motivates their decision making
• Connect the dots from technology solutions, inclusive of the Everpure portfolio and others from the ecosystem, to measurable customer business outcomes
• Partner closely with account managers, specialists and channel partners to create a seamless and holistic customer experience and strategy to drive revenue growth and net new business
• Delight customers and teammates with your technical leadership and domain expertise on storage products, distributed storage architectures, file systems, and competitive storage offerings in the DAS, NAS and SAN product spaces
• Take control of evaluations,  benchmarks and system configurations
• Build and deliver technical product and architecture presentations with vigor to customers and lock in the technical win with clients and prospective accounts
• Capture requirements and translate Customer’s business needs into innovative solutions to accelerate their growth and capacity
• Seek out and spur new opportunities for interoperability, functional, and performance testing/validation
• Promote mutual roadmap and planning exchanges
• Liaise with product and technical marketing to produce materials (i.e. whitepaper, web presentations) that support overall alliance plans and objectives
• Stay current with the competition and ensure that materials accurately reflect the latest, differentiated solution positioning
• Move with market trends and competitive landscape to promote thought leadership within our organization
• Lead by example living the Everpure Values:  Persistence, Creativity, Teamwork, Ownership and Customer-first  

WHAT YOU BRING:

• Pre-sales experience is required
• 5 or more years of relevant experience, mixed among technology, design, and communications required. Experience supporting the sales of enterprise storage, networking, systems, cloud, or software to enterprise accounts preferred
• Deep understanding of the architecture, design and implementation of multi-tiered client/server and web-based computing solutions
• Must have a familiarity with modern web architecture a plus (LAMP stack, scale-out vs scale-up architectures, AWS well-architected framework, microservices architecture)
• Prior experience in targeting new prospects and converting them into clients
• Contribute significant portions and/or lead responses to RFPs, RFQs and RFIs
• Superior knowledge of current and emerging storage architectures (FC, iSCSI, NAS, OSD, SAN)
• Previous experience with Linux, NFS file systems and Linux distributed network environment
• Installation/configuration of distributed computing, multiprocessing, virtual memory subsystem, storage subsystems architecture, shared memory architectures, cache architectures, windowing systems
• Strong knowledge of digital transformation to a Cloud Native architecture, On-Prem, Public Cloud, Provisioning Automation, and Containers
• Knowledge of VMware, Local Area Networking, Ethernet, TCP/IP and general networking
• Familiarity with AWS, Azure, GCP, containers, k8s and microservices architectures a plus
• Curiosity and passion around technology and explaining and showing new technology platforms and concepts to customers
• Familiarity with storage-intensive applications and their requirements such as structured databases (SQL, Oracle) is highly desirable
• Excellent verbal and written interpersonal skills

#LI-REMOTE

#LI-JL4

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

About the Role:

As a Senior Software Engineer on the Tracking Team, you will drive the delivery of both classical and machine learning solutions to core tracking challenges within our self-driving system. A central function of the tracking team is the real-time, temporally consistent estimation of the local scene (including actors, obstacles, and map features) based on a history of sensor data. In this role, you will be instrumental in the design and delivery of algorithms and models that run onboard the AV, evaluating the perception system's performance against production requirements, and contributing to the long-term technical vision of the team.

Responsibilities:

• Work in a team to deliver state-of-the-art closed- and open- world tracking systems for L4 self-driving vehicles.
• Contribute to the technical direction of the tracking team and broader perception org.
• Write robust software for real-time, resource-constrained, safety-critical applications.
• Drive highly cross-functional features and work streams from problem statement to delivery.
• Proactively identify limitations with the existing perception system and drive improvements.
• Root-cause issues that arise onboard, and provide solutions.
• Align stakeholders with strong presentation and communication skills.
• Lead challenging technical topics and build consensus.

Qualifications: 

• Experience architecting, training, and deploying ML models into real-world environments.
• Track record of driving applied research projects from start to completion, including conception, problem definition, experimentation, iteration, and finally publication or productization.
• Experience delivering perception/tracking solutions for real-time robotic applications.
• Knowledge and experience employing advanced probabilistic algorithms to solve problems involving uncertain inputs and imperfect models.
• Strong experience in software engineering and algorithm design.
• Fluency in Python and C++.

About the Role:

The Dynamic World Perception team is responsible for real-time detection of road actors and properties. This includes developing occupancy networks and other perception algorithms to accurately identify and classify all dynamic elements within the driving environment. We're looking for a Staff or Senior Engineer to lead the development, maintenance, and enhancement of our long-range detection capabilities, ensuring the required safety and performance standards are met or exceeded.

Responsibilities:

• Lead initiatives in the team to deliver state-of-the-art closed-and open-world detection capabilities.
• Contribute to expansion and fusion of existing detection models to support improving performance and extending performance at range.
• Contribute to improvement of model frameworks, architecture, and data pipelines.
• Develop robust software for onboard vehicle deployment, optimizing for real-time, resource-constrained, and safety-critical environments.
• Drive highly cross-functional features and workstreams from problem statement to delivery.
• Contribute to the technical direction of the team and broader perception org.
• Align stakeholders with strong presentation and communication skills.
• Lead challenging technical topics and build consensus.

Qualifications: 

• Extensive experience architecting, training, and deploying deep learning models into real-world environments.
• Track record of driving applied research projects from start to completion, including conception, problem definition, experimentation, iteration, and finally publication or productization.
• Experience delivering detection, segmentation, and/or tracking solutions for real-time robotic applications. Prior experience in sensor fusion is highly desirable.
• Strong experience in software engineering and machine learning algorithm design.
• Fluency in Python.
• Experience with C++.

About the Role:

As a Labeling Infrastructure Backend Engineer, you will design, build, and scale the mission-critical systems that power our data flywheel. You will be responsible for the high-performance architecture that manages massive datasets, orchestrates complex human-in-the-loop workflows, and integrates seamlessly with our machine learning pipelines. Your work ensures that our labeling operations are fast, reliable, and capable of handling the next order of magnitude in data volume.

• Architect Scalable Systems: Design and maintain robust, distributed backend services capable of managing millions of labeling tasks and high-throughput data streams with minimal latency.
• Optimize Data Pipelines: Build and refine processes to ingest raw data from diverse sources and deliver high-quality labeled outputs to model training environments.
• ML Integration: Implement "Active Learning" and "Model-in-the-loop" features, enabling automated pre-labeling and intelligent task prioritization to maximize human efficiency.
• API Development: Develop and document clean, performant APIs that serve as the bridge between our front-end labeling tools, third-party vendors, and internal ML platforms.
• Infrastructure & DevOps: Manage cloud-native infrastructure to ensure 99.9+% availability, focusing on observability, automated scaling, and cost-efficiency.
• Cross-Functional Collaboration: Partner with AI team and Product Managers to translate complex data requirements into technical specifications and durable backend solutions.

Responsibilities:

• Play a key role in designing and building the next generation of Stack’s Labeling Infrastructure.
• Implement robust architecture to track the lifecycle of every log from unlabeled to production-ready.
• Build scalable backend services for automated QA to and AI-assisted labeling plugins.
• Write high quality Python and SQL.

Qualifications: 

• Proven track record of building scalable, reliable infrastructure in a fast-paced environment.
• Ability to collaborate effectively across teams.
• Strong development experience with Python and SQL.
• Prior experience with Trino, Flyte / Airflow, and Kubernetes are a plus.
• Prior experience with ML Ops workflows is a plus.
• Prior experience building and managing data platforms for multimodal ML needs is a plus.
• Prior experience with agentic workflows is a plus. 
• Prior experience in autonomous vehicles (AV) is a plus.

Company Description

Govini transforms Defense Acquisition from an outdated manual process to a software-driven strategic advantage for the United States. Our flagship product, Ark, supports Supply Chain, Science and Technology, Production, Sustainment, Logistics, and Modernization teams with AI-enabled Applications and best-in-class data to more rapidly imagine, develop, and field the capabilities we need. Today, the national security community and every branch of the military rely on Govini to enable faster and more informed Acquisition decisions.

Job Description

We are seeking an experienced Lead AI Engineer to lead our Agentic AI team as we scale our agentic offerings across all levels of the US government. Over the past year, we have seen the rapid adoption of our agent, Ace. We expect Ace to be handling much more complex tasks and workflows end-to-end and/or in cooperation with a human user as time goes on. The team is striving to make Ace an even more effective agent, focusing on planning, reliable execution over longer time horizon tasks, scaled tool use, Ace Skills, memory, and inter-agent coordination.

In order to do this job well, we are looking for someone who can demonstrate a project built on LLMs that showcases your skill at getting them to do complex tasks.

• Experience leading agentic system development from ideation through production, including technical design, architecture decisions, and day-to-day leadership of Data Scientists and Machine Learning Engineers
• Experience integrating and working with LLMs, with a strong understanding of their capabilities and limitations
• Experience integrating and working with agent frameworks like Claude Agent SDK or OpenAI Agent SDK
• Experience building observability, evaluation, and feedback loops for agent behavior (telemetry, prompt evaluation, regression testing, and reliability metrics).
• Experience working in highly ambiguous environments, and operating with urgency
• Lead Agile ceremonies and technical planning for the Agentic AI team

Strong Candidates have:

• Experience designing and deploying complex agentic systems using LLMs
• Hands-on work with multi-agent coordination, routing, and tool orchestration
• Passion for building autonomous and semi-autonomous systems that solve real customer problems

This role is a full-time position located out of our office in Pittsburgh, PA. 

This role may require up to 10% travel

Scope of Responsibilities 

• Designs and builds multi-agent systems with tool use, memory, routing, and planning
• Develops Agent Skills and tools as modular, composable services that interact with backend systems, models, and data processing
• Assist with automated evaluation of agents, skills, and prompts across the entire product lifecycle
• Work with our Product and Implementation organizations to find solutions to our most vexing challenges applying agents to our customer problems

Qualifications

• U.S. Citizenship is required

Required Skills:

• Bachelor's, Master’s, or Doctorate in Computer Science, Computer Engineering, Data Science, or a related field
• Minimum 3 years of experience building and deploying ML or LLM-powered systems in production environments
• Minimum 2  years of experience leading or mentoring engineering teams in production environments
• Practical experience in building, developing, and productionizing machine learning systems
• Advanced software skills in Python
• Experience with common LLM algorithms and implementations
• Hands-on experience with AWS and cloud infrastructure
• A strong desire to learn and investigate new technologies
• Familiarity with Git source control management
• Ability to work collaboratively with little supervision
• A burning desire to work in a challenging fast-paced tech environment

Desired Skills:

• Current possession of a U.S. security clearance, or the ability to obtain one with our sponsorship
• Experience in or exposure to the nuances of a startup or other entrepreneurial environment
• Experience with Claude Code and GenAI coding best practices.

We firmly believe that past performance is the best indicator of future performance.  If you thrive while building solutions to complex problems, are a self-starter, and are passionate about making an impact in global security, we’re eager to hear from you.

Govini is an Equal Opportunity Employer.  All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.

Our mission at Duolingo is to develop the best education in the world and make it universally available. It’s a big mission, and that’s where you come in!

At Duolingo, you’ll join a team that cares about finding innovative solutions to complex technical problems, running countless experiments (300+ at a time!) with our massive user base to make data-driven decisions, and educating our users and employees alike. You’ll have limitless learning opportunities, mentorship and collaboration with world-class minds, and a variety of projects with large scopes — while doing work that’s both fun and meaningful. 

Join our life-changing mission to develop education for our half a billion (and growing!) learners around the world.

About the role...

As a Platform Engineer on the Compute team, you’ll improve how engineers build and run services, helping ensure our compute platform is reliable at scale for our learners and fellow Duo engineers.

The Compute team builds and operates the core compute platform that powers our applications. We build paved roads (self-service platform primitives, templates, and tooling) and we operate them so product engineers can focus on shipping features with confidence.

This role is based in our Pittsburgh office and requires in-office presence Monday through Thursday.

🧠  You will...

• Build and operate core compute primitives that power production workloads based on Kubernetes, across different regions and cloud providers. 
• Improve the delivery pipeline from commit to production by evolving our GitOps and deployment patterns, making rollouts safer and faster.
• Raise the operational bar: define SLOs, build dashboards/alerts, write runbooks, and participate in on-call/incident response to keep the platform dependable.
• Enable effective self-service by turning platform capabilities into reusable abstractions.

✅  You have...

• Strong problem-solving skills and experience delivering and shipping pragmatic solutions in production environments.
• Experience with distributed systems fundamentals (networking, service-to-service communication, failure modes, caching/storage concepts).
• Experience working on production systems (through industry roles, internships, or substantial open-source work).
• Experience with one or more CI/CD tools (Jenkins, Argo CD, GitHub Actions) and infrastructure management tools (Terraform, CloudFormation)
• Clear written and verbal communication skills—comfortable writing docs/runbooks and collaborating across teams.
• Functional knowledge of Linux system administration and automation
  
  
  

⭐  Exceptional candidates will have...

• A Bachelor’s degree in Computer Science, related technical field, or equivalent practical experience
• Experience managing a production infrastructure on AWS, Google Cloud, Alibaba Cloud, or Azure.
• Exposure to multi-cluster or multi-region architectures and the operational patterns that make them reliable.
• Experience with service mesh / zero-trust networking patterns (e.g., Istio mTLS, traffic management, authorization policies).

Our mission at Duolingo is to develop the best education in the world and make it universally available. It’s a big mission, and that’s where you come in!

At Duolingo, you’ll join a team that cares about finding innovative solutions to complex technical problems, running countless experiments (300+ at a time!) with our massive user base to make data-driven decisions, and educating our users and employees alike. You’ll have limitless learning opportunities, mentorship and collaboration with world-class minds, and a variety of projects with large scopes — while doing work that’s both fun and meaningful. 

Join our life-changing mission to develop education for our half a billion (and growing!) learners around the world.

About the role...

As a Platform Engineer on the Compute team, you’ll improve how engineers build and run services, helping ensure our compute platform is reliable at scale for our learners and fellow Duo engineers.

The Compute team builds and operates the core compute platform that powers our applications. We build paved roads (self-service platform primitives, templates, and tooling) and we operate them so product engineers can focus on shipping features with confidence.

This role is based in our Pittsburgh office and requires in-office presence Monday through Thursday.

🧠  You will...

• Build and operate core compute primitives that power production workloads based on Kubernetes, across different regions and cloud providers. 
• Improve the delivery pipeline from commit to production by evolving our GitOps and deployment patterns, making rollouts safer and faster.
• Raise the operational bar: define SLOs, build dashboards/alerts, write runbooks, and participate in on-call/incident response to keep the platform dependable.
• Enable effective self-service by turning platform capabilities into reusable abstractions.

✅  You have...

• Strong problem-solving skills and experience delivering and shipping pragmatic solutions in production environments.
• Experience with distributed systems fundamentals (networking, service-to-service communication, failure modes, caching/storage concepts).
• Experience working on production systems (through industry roles, internships, or substantial open-source work).
• Experience with one or more CI/CD tools (Jenkins, Argo CD, GitHub Actions) and infrastructure management tools (Terraform, CloudFormation)
• Clear written and verbal communication skills—comfortable writing docs/runbooks and collaborating across teams.
• Functional knowledge of Linux system administration and automation
  
  
  

⭐  Exceptional candidates will have...

• A Bachelor’s degree in Computer Science, related technical field, or equivalent practical experience
• Experience managing a production infrastructure on AWS, Google Cloud, Alibaba Cloud, or Azure.
• Exposure to multi-cluster or multi-region architectures and the operational patterns that make them reliable.
• Experience with service mesh / zero-trust networking patterns (e.g., Istio mTLS, traffic management, authorization policies).

Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. We’re searching for a Staff Software Engineer to join Aurora’s Vehicle Connectivity team. The Vehicle Connectivity team is responsible for Aurora’s self-driving vehicle connectivity stack for reliable, observable, and secure vehicle-to-cloud communications.

In this role, you will

• Define vehicle connectivity and communication architecture
• Address connectivity-specific concerns for vehicle runtime, e.g. networking, performance, and observability
• Perform connectivity bring up of new vehicle platforms
• Work with autonomy engineers to meet performance and efficiency requirements for data collection and offload
• Provide engineering support for field testing and fleet operations

Required Qualifications

• BS/MS/PhD in Computer Science or related field, or equivalent industry experience
• Strong programming skills in C++
• Ability to work across multiple programming languages and paradigms
• Experience working with networking protocols, such as TCP, UDP, gRPC, HTTP
• A passion for writing robust, intuitive, high-performance code, and being pragmatic about it
• Experience with Linux network configuration and troubleshooting
• Ability to navigate and work effectively in large codebases
• Strong verbal and written communication skills 
• Work autonomously, but still be a great team player with colleagues across time-zones

Desirable Qualifications 

• Experience with performance evaluation and optimization. 
• Network health monitoring frameworks
• Strong programming skills in GoLang
• Experience with safety critical systems
• Good understanding of ethernet switching and interaction between OSI layers 1, 2, 3 and 4

The base range for this position is $171,000 - $273,000.  Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits.

 #LI-td-1

 #Mid-Senior 

About us

TeleTracking began with a simple but powerful belief that no one should wait for the care they need. More than a slogan, it’s a promise to continually improve healthcare.

TeleTracking builds groundbreaking technology incorporating deep clinical expertise. Our solutions are used in the nation’s largest healthcare systems and around the world to positively impact patients, families and communities.

What’s your contribution to the TeleTracking story?

When you chose to bring your passion and skills to helping achieve our purpose, you’ll be part of a team that understands that there’s a human life behind every data point.   Your skills, curiosity, and compassion—will help fuel our innovation and achieve the TeleTracking promise of revolutionizing modern healthcare. 

About the role

We are seeking a highly skilled and experienced Senior Machine Learning Engineer to join our innovative team at TeleTracking Technologies, focusing on improving patient care. The ideal candidate will have a strong background in developing, deploying, and optimizing machine learning models, as well as expertise in data ingestion and modeling techniques. This role requires a deep understanding of hospital operations, healthcare data, clinical workflows, and regulatory requirements (such as HIPAA or GDPR). The successful candidate will be passionate about leveraging advanced technologies to enhance patient care and operational efficiency. 

What you will do

• Design, develop, and implement machine learning and deep learning models to address hospital-specific challenges such as patient flow optimization, resource allocation, bed management, and predictive analytics for patient outcomes.
• Build and optimize data ingestion and modeling pipelines as needed
• Utilize domain driven techniques and design patterns to build and contribute to technical
• design.
• Collaborate with cross-functional teams including data scientists, software engineers, clinicians, hospital administrators, and experts in TeleTracking Technologies to identify and develop high-impact machine learning solutions.
• Work with large-scale healthcare and hospital datasets including structured data (EHRs, hospital operational data), unstructured data (clinical notes, imaging).
• Ensure data privacy and security, adhering to healthcare regulations such as HIPAA and GDPR, especially when working with sensitive hospital data.
• Mentor junior engineers and data scientists, providing guidance on machine learning techniques, particularly those relevant to hospitals and healthcare systems.
• Monitor, troubleshoot, and enhance the performance of deployed models using MLOps best practices, ensuring they operate effectively in hospital environments.
• Write technical architectural and design documents.

What we look for

• Proven experience in end-to-end design and deployment of machine learning models from ideation to production in healthcare or similar settings.
• Strong programming skills and experience with object or component-oriented development software, one or more of: Python or R, with proficiency in ML frameworks, one or more of: TensorFlow, PyTorch, or Scikit-learn.
• Expertise in NLP, computer vision, or other specialized machine learning techniques applicable to healthcare and hospital environments.
• Deep knowledge of a scripting or statistical programming language (Python preferred). Ability to efficiently work with very large datasets and deal with non-standard machine learning datasets (class-imbalances, sparse matrices, etc.)
• Assess model performance; train multiple models; carry out tuning.  Run A/B tests on models.
• Comfortable writing complex SQL queries and developing python packages.
• Experience with cloud-based management and hosting, one or more of: AWS, Azure, GCS, CloudFormation, Terraform, or Ansible. Interest in developing services as well as the underlying infrastructure
• Experience with database management system software, one or more of: Oracle, MSSQL, MongoDB, MySQL, DynamoDB, or PostgreSQL. 
• Experience with Version Control Software, one or more of: git, Mercurial, CVS, TFS, or Subversion. 
• Strong understanding and experience executing several software development methodologies and life cycles. Ability to understand and translate business requirements into technical specifications.
• Experience with agile development practices.
• Excellent written and oral communication skills. Adept and presenting complex topics, influencing, and executing with timely / actionable follow-through.
• Strong analytical and problem-solving skills with the ability to convert information into practical training deliverables. Uses rigorous logic and methods to solve difficult problems.
• Knowledge of clinical workflows and hospital operations, and how technology can enhance efficiency and patient care.
• Familiarity with healthcare-specific machine learning challenges, such as data imbalance, longitudinal data, and real-time processing in hospital environments.
• Be an active listener, probe requirements for all projects from relevant stakeholders, stay nimble and willing to produce rapid iterations.

Education

• Bachelor's degree in computer science, Data Science, Machine Learning, Artificial Intelligence, or a related field; 7 or more years of experience.
• Master's or PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field; 5 or more years of experience. (preferred)

Applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future.

Mission

Speechify is the easiest way to listen to the world’s information. Articles on the web, documents in the cloud, books on your phone. We absorb it all and let you listen to it at your desk, on the go, at your own speed, and with tools that make learning easier, deeper, and faster.

What streaming services have done for audio entertainment, we’re doing for audio information. And whatever we’re doing seems to be working. We’re #1 in our category, and experiencing exponential growth.

Overview

We're looking for a Senior Software Engineer to join our Core Experiences Team. This team builds and maintains the foundational services and SDKs that power Speechify’s product experience across platforms. It's a critical role for someone who enjoys working at the intersection of product and infrastructure, thinks strategically, and is passionate about designing clear, reliable APIs and simple systems that directly enhance the user experience.

What You’ll Do

• Collaborate with Speechify’s Product Teams to scope projects, find ways to reduce scope to get faster feedback from customers, navigate the integration/UX tradeoffs relevant to different product teams, and deliver solutions via our cross-platform SDK
• Optimize existing functionality to ship things like faster loading and more intelligent parsing by designing more efficient abstractions and improving our algorithms
• Ship cloud functions, lightweight backend services, and jobs using Kotlin

An Ideal Candidate Should Have

• Systems thinking. You know that optimizing a system requires end-to-end design, and you believe simplicity is valuable.
• Speed. You work quickly to generate ideas and options and know how to decide which things can ship now and what things need time
• Focus. We’re a high-growth startup with a busy, remote team. You know how and when to engage or be heads down
• Collaboration. You know how to inspire, play, and negotiate with opinionated designers, marketers, and PMs
• Preferred Tech Stack:
  • Kotlin Multiplatform (Java experience is OK but role is Kotlin Multiplatform) 
  • Prior experience with GCP is a plus

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.

The United States Based Salary range for this role is: 140,000-200,000 USD/Year + Bonus + Stock depending on experience

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.

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

The responsibilities of our Platform team include building and maintaining all backend services, including, but not limited to, payments, analytics, subscriptions, new products, text to speech, and external APIs.

This is a key role and ideal for someone who thinks strategically, enjoys fast-paced environments, is passionate about making product decisions, and has experience building great user experiences that delight users.

We are a flat organization that allows anyone to become a leader by showing excellent technical skills and delivering results consistently and fast. Work ethic, solid communication skills, and obsession with winning are paramount. 

Our interview process involves several technical interviews and we aim to complete them within 1 week. 

What You’ll Do

• Design, develop, and maintain robust APIs including public TTS API, internal APIs like Payment, Subscription, Auth and Consumption Tracking, ensuring they meet business and scalability requirements
• Oversee the full backend API landscape, enhancing and optimizing for performance and maintainability
• Collaborate on B2B solutions, focusing on customization and integration needs for enterprise clients
• Work closely with cross-functional teams to align backend architecture with overall product strategy and user experience

An Ideal Candidate Should Have

• Proven experience in backend development: TS/Node (required)
• Direct experience with GCP and knowledge of AWS, Azure, or other cloud providers
• Efficiency in ideation and implementation, prioritizing tasks based on urgency and impact
• Preferred: Experience with Docker and containerized deployments
• Preferred: Proficiency in deploying high availability applications on Kubernetes

What We Offer

• A dynamic environment where your contributions shape the company and its products
• A team that values innovation, intuition, and drive
• Autonomy, fostering focus and creativity
• The opportunity to have a significant impact in a revolutionary industry
• Competitive compensation, a welcoming atmosphere, and a commitment to an exceptional asynchronous work culture
• The privilege of working on a product that changes lives, particularly for those with learning differences like dyslexia, ADD, and more
• An active role at the intersection of artificial intelligence and audio – a rapidly evolving tech domain

The United States Based Salary range for this role is: 140,000-200,000 USD/Year + Bonus + Stock depending on experience

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.

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:

We are seeking a capable and highly motivated Engineer for our Developer Platform team to enable rapid and safe software delivery by providing a unified, efficient and reliable platform for all developers at Motional. You will join a talented team of engineers that focuses on improving the engineering experience and developer productivity of teams in Motional by providing them with the necessary tools and processes. You will collaborate closely with teams across the organization to identify pain points and drive initiatives that empower teams to deliver on Motional’s mission.

Your main focus will be on our real-time data pipeline which enables our AVs to communicate with our cloud services such as Command Center, Over-The-Air update systems, and ride hailing partners integration.

This is a remote-first position based anywhere in the U.S. 

What you'll do:

• Design, build, document, test, and maintain network, compute, and storage infrastructure of our cloud platform for the AV fleet.
• Monitor, support, and resolve issues as they arise in production.
• Assist with high-level analysis, system design, and code reviews.
• Communicate with stakeholders and team leads to identify system requirements.

What you'll be working on:

• Observability systems, CI/CD pipelines for integrations with ride hailing partners like Uber and Lyft.
• Cost Optimizations, Performance Optimizations, Security Improvements of Cloud Systems.
• Automation, services and LLM-integrated bots to reduce developer toil.

What you bring:

• 2+ years of professional work experience as a Software Engineer/DevOps in designing and building distributed systems.
• Experience with Golang / Python.
• Experience with AWS (EC2, EKS, ECS, Lambda, VPC, S3, IAM, IOT).
• Knowledge of CI/CD tools (e.g. GitLab CI, Jenkins).
• Experience with Infrastructure as Code (Terraform).
• Comfortable taking projects from ideation through to completion, with strong communication and collaboration across multiple international teams.
• Comfortable working in a fast-paced, continuous delivery environment.

Bonus points for experience with:

• Working with a globally distributed team.
• Experience with serverless architectures, building systems using SQL and NoSQL databases.
• Experience with time-series databases and monitoring tools (Prometheus, VictoriaMetrics, Grafana, Redash).
• Experience with GenAI solutions (Q Developer/Business, Gemini).
• Experience in Site Reliability Engineering.
• Experience with error monitoring software (Sentry, Incident.io).

Mission Summary:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

Mission Summary:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems, and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

Mission Summary:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

Mission Summary:

As a Principal Engineer within the Platform Engineering group, you will serve as the primary architect for Motional’s global cloud and hybrid infrastructure. Your mission is to evolve our current R&D environment into a hardened, cost-efficient, and scalable commercial backbone. You will lead the technical strategy for our hybrid cloud migration and ensure our internal frameworks provide  the velocity required for rapid AV deployment.

What you’ll be doing:

• Architect and drive the execution of our hybrid cloud strategy. You will design the networking, security, and compute abstraction layers that allow seamless workload mobility between on-premises high-performance computing (HPC) and AWS.
• Serve as the primary architect for our Kubernetes-based developer framework. You will identify and implement core improvements to the framework to enhance scalability, cost tracking and access control, reduce configuration complexity, and increase developer velocity.
• Lead organization-wide initiatives to harden service reliability. You will define the standards for observability, failover strategies, and production-grade stability across all platform-layer services.
• Prototype and deploy GenAI-focused tooling and agents to automate Platform Engineering tasks. This includes leveraging LLMs for automated incident response, predictive infrastructure scaling, and intelligent log analysis.
• Act as the final technical authority for infrastructure decisions, ensuring all new platform services align with long-term scalability and security goals.

What we’re looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 12+ years in Software or Infrastructure Engineering, with significant experience in a Principal or Architect capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (EKS, Operators (KubeRay, ArgoWorkflow, Airflow), Service Mesh, GitOps)
• Proven track record of designing and migrating complex workloads between on-prem data centers and AWS
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff.

Bonus Points (not required):

• Expert-level experience managing and architecting solutions using Equinix datacenter products and services (e.g., Equinix Fabric, Network Edge, Metal) to enable high-speed hybrid cloud connectivity (AWS Direct Connect)
• Prior experience in the Autonomous Vehicle, Robotics, or large-scale IoT/Fleet Management industries.
• Experience leading technical projects in a globally distributed team
• Proven expertise in CI/CD orchestration, build systems (Bazel), frameworks

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:

The Autonomous Vehicle (AV) Test Engineering team at Motional is responsible for validating the safety, functionality, and performance of our self-driving technology through rigorous, system-level testing. As a Senior AV Test Engineer, you will take a lead role in the strategy, planning, and execution of comprehensive test campaigns that span perception, planning, and control systems across virtual, closed-course, and public road environments.

In this role, you will guide test development efforts across multiple AV subsystems, mentor junior engineers, and drive improvements to our tooling, test infrastructure, and methodologies. You will lead cross-functional initiatives that directly impact release readiness and safety assurance, translating high-level engineering requirements into scalable test plans and measurable quality outcomes. Your work will contribute to iterative development, fault injection analysis, and validation of safety-critical systems that meet the highest standards for reliability and compliance.

Essential Duties:

Technical Leadership & Validation:

• Lead the planning, design, and execution of validation campaigns across simulation, SIL, HIL, and on-vehicle environments, with a focus on perception, planning, and control systems.
• Serve as the technical lead for scenario-based testing, utilizing platforms like Applied Intuition or equivalents for synthetic and re-simulation use cases.
• Lead AV capability campaigns, employing AB Dynamics equipment for system-level validation and performance benchmarking.
• Own the creation and execution of test plans for complex mechatronic and software systems, including drive-by-wire (DBW), braking, steering, and system integrations.

Test Infrastructure & Analysis:

• Develop and maintain robust test infrastructure using Python, Shell, and automation frameworks, ensuring modular, reusable, and scalable test architecture.
• Conduct detailed analysis of logs and system behavior, supporting root-cause investigations across AV software and hardware components.
  
  

Quality Assurance & Compliance:

• Participate in and lead QA review cycles, helping to define entry/exit criteria, regression coverage, and release readiness gates.
• Ensure traceability and compliance by documenting procedures, results, and outcomes using Jira, GitLab, and Confluence.
• Contribute to compliance with safety-critical standards such as ISO26262, ISO21448 (SOTIF), and ISO9001.
  
  

Collaboration & Mentorship:

• Represent the Test Engineering function in cross-functional planning meetings, collaborating closely with perception, planning, controls, and safety teams.
• Mentor and support junior Test Engineers, providing technical guidance and fostering a culture of continuous learning and improvement.
  
  

Strategic Adaptation & Support:

• Continuously adapt and optimize test strategies in response to evolving product requirements, architectural shifts, and real-world findings.
• Support after-hours testing or incident triage as needed by fleet or field operations.
• Travel domestically on occasion to support on-site testing or operational deployments.
• Perform additional duties as assigned.

Required Qualifications:

• Bachelor's degree or higher in Engineering, Robotics, ECE, Computer Science, or a related technical field.
• 5+ years of experience in software QA or system testing, including AV, ADAS, robotics, or safety-critical systems.
• Demonstrated expertise in test strategy, test case development, test execution, and automation for complex mechatronic or autonomous systems.
• Experience leading end-to-end testing across software stacks and physical systems, including SIL, HIL, and real-world deployments.
• Strong analytical skills with proficiency in debugging, log parsing, and root cause analysis.
• Excellent verbal and written communication skills, with the ability to drive cross-functional alignment and effectively report technical findings.
• Proven ability to lead technical efforts, mentor junior engineers, and operate with minimal oversight.
• High level of initiative, ownership, and accountability in a fast-paced, startup-style engineering environment.
• Flexible schedule and willingness to support late meetings, night shifts, or urgent test activities as required.

Preferred:

• Master’s degree in a relevant field.
• Deep familiarity with CAN-based tools (CANalyzer, CANoe) for fault injection, bus analysis, and subsystem verification.
• Working knowledge of industry standards for safety-critical systems such as ISO26262, ISO21448 (SOTIF), and ISO9001.
• Familiarity with AV safety case development or safety validation workflows.
• Experience with robotic middleware (e.g., ROS), cloud telemetry systems, or AV data pipelines.
• Experience in a cross-site or global engineering organization.
  
  

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.

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.

While performing the duties of this job, the team member is regularly exposed to outside weather conditions, road conditions, and pedestrian traffic.  The team member is regularly exposed to mechanical and computer parts. The team member is occasionally exposed to fumes and airborne particles.  The 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 regarding race, sex, color, national origin, age, disability, veteran status, genetic data, religion, gender identity, sexual orientation, or another 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

The AI Data Foundry is the vital link between our company's expansive data and the engineering teams that rely on it. We architect and operate the core services and infrastructure necessary to transform petabytes of raw data each month into actionable insights for our Machine Learning and Autonomy engineers. This role is a key contributor across our core initiatives:

• Data Processing: We are building state-of-the-art infrastructure to meet the demands of our rapidly scaling fleet and sophisticated ML engineering requirements. You will be responsible for ensuring that our core data pipelines are robustly engineered—meaning they are performant, user-friendly, and highly fault-tolerant.
• Data Discovery: we are building a platform that combines cataloging data for search needs across our metadata stores, vector datastores, and various data warehouses to super charge our ML pipelines. You would be coming in and working with ML and autonomy stakeholders to build out this new discovery platform. 
• Data Lineage: we are getting started with building our data lineage system that will track the usage of all data at Motional. You would work with our stakeholders to ensure our system is widely adopted and address customer pain points.

What You'll Be Doing

• Play a key role in designing and building the next generation of data infrastructure.
• Build scalable backend services for data discovery and data lineage systems.
• Write high quality, maintainable code to process petabytes of data 
• Lead and champion high-level system design and quality code reviews/technical contributions.
• Design, build, and maintain scalable data processing and access with cloud ETL technology.

What We're Looking For

• 3+ years of professional experience as a data engineer or data pipeline engineer
• BS or MS in Computer Science or related field
• Strong development experience with Python and SQL.
• Expertise with AWS services and solutions (EKS, Lambda, ETL) 
• Experience with distributed system design and development
• Comfortable working in a fast-paced, continuous delivery environment

Bonus Points (not required): 

• Experience with Infrastructure Monitoring
• Experience working in cross-functional development teams 

Mission:

We are seeking a highly skilled and motivated Principal Engineer, Technical Lead to lead our large-scale AI model and software evaluation framework – Ground Truth Regression. The ideal candidate will have a strong background in data engineering, machine learning principals and leadership, with experience in developing and deploying advanced data analysis and large scale orchestration frameworks.

At Motional, large scale orchestration for data analysis plays a critical role in delivering our ML-centered autonomous driving vehicle. Our robo-taxi ML Models and software are developed by hundreds of developers and deployed multiple times a day – The Ground Truth Regression team provides the large scale perception analysis framework for validating all changes to perception which can impact the autonomous vehicle behavior. 

The GTRegression team validates the end impact of ML and software changes to the autonomous vehicle while those changes are in development, providing the tools for Root Cause Analysis, and safety consideration. We monitor for model errors, anomalies, rare objects & long-tail driving scenarios across thousands of driving hours. The team develops the full stack – AWS Kubernetes orchestration framework, ReSimulation, model metrics, regression reports, and deep dive analysis UI’s. The team works together with all other perception and ML teams to develop the metrics and analyze results.

What You'll Do: 

• Lead a team of engineers with a diverse skill set to deliver critical software supporting the development of the autonomous vehicle ML models and software.
• Drive innovation by researching and developing new large scale data warehousing and analysis.
• Own large-scale data analysis workflows that surface unexpected impacts of planned and unplanned changes to the ML models and software.
• Build high-quality datasets to improve ML products through training & edge case validation.
• Provide statistical depth on model performance & generalization through rigorous error and change analysis.
• Provide technical leadership and mentorship to the team, fostering a culture of collaboration and continuous improvement.
• Manage project timelines, resources, and deliverables to ensure successful project execution.

What We're Looking For:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Excellent leadership, communication, and problem-solving skills.
• Proven record leading a small diverse dedicated team supporting a large user base.
• Experience in machine learning, data mining, and statistical analysis.
• Proven experience in developing and deploying large scale cloud frameworks.
• Strong programming skills in Python.
• Experience with large-scale data processing and distributed computing technologies.
  
  

Bonus Points:

• Ph.D. in Computer Science or a related field.
• Experience with cloud platforms such as AWS, Google Cloud, or Azure.
• Experience with machine learning in the autonomous driving domain.
• Publications or contributions to the AI/ML community.

Mission:

We are seeking a highly skilled and motivated Senior Data Analysis Engineer for our large-scale AI model and software evaluation framework – Ground Truth Regression. The ideal candidate will have a strong background in data engineering, machine learning principals and statistical analysis. 

At Motional, large scale orchestration for data analysis plays a critical role in delivering our ML-centered autonomous driving vehicle. Our robo-taxi ML Models and software are developed by hundreds of developers and deployed multiple times a day – The Ground Truth Regression team provides the large scale perception analysis framework for validating all changes to perception which can impact the autonomous vehicle behavior. 

The GTRegression team validates the end impact of ML and software changes to the autonomous vehicle while those changes are in development, providing the tools for Root Cause Analysis, and safety consideration. We monitor for model errors, anomalies, rare objects & long-tail driving scenarios across thousands of driving hours. The team develops the full stack – AWS Kubernetes orchestration framework, ReSimulation, model metrics, regression reports, and deep dive analysis UI’s. The team works together with all other perception and ML teams to develop the metrics and analyze results.

What You'll Do:

• Work with ML Engineers and Autonomy Software Developers to develop new data analysis metrics and KPI’s for the validation of the autonomous vehicle performance.
• Drive innovation by researching and developing new large scale data analysis systems.
• Own large-scale data analysis workflows that surface unexpected impacts of planned and unplanned changes to the ML models and software.
• Build high-quality datasets to improve ML products through training & edge case validation.
• Provide statistical depth on model performance & generalization through rigorous error and change analysis.

What We're Looking For:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Strong programming skills in Python, SciPy, and related data analysis frameworks.
• Experience in machine learning, data mining, and statistical analysis.
• Experience with large-scale data processing and distributed computing technologies.
• Strong communication and software development skills: goal setting, design documentation, test frameworks, collaborative cross team development.
  
  

Bonus Points:

• Ph.D. in Computer Science or a related field.
• Experience with cloud platforms such as AWS, Google Cloud, or Azure.
• Experience with machine learning in the autonomous driving domain.
• Publications or contributions to the AI/ML community.

About the Role:

In the ML Training, our mission is to provide a reliable, scalable, and easy to use training framework for modeling needs of Stack AV. In addition, this team is responsible for the overall developer experience of ML engineers including building tools for testing, validation, and understanding models and the data used to train them. Finally, we are responsible for model optimization and deployment.

Responsibilities: 

• Experience with both ML Platforms and building ML-based applications.
• Experience building scalable, reliable infra at a fast-paced environment working with MLEs on several different modeling teams.
• A deep understanding of design tradeoffs and ability to articulate those tradeoffs and work with others on getting alignment.
• Experience with building ML models or ML infra in the domains of autonomous vehicles, perception, and decision making (desirable but not required).
• Experience with model training, model optimization, or large data processing pipelines.

Qualifications (Preferred): 

• Built an end to end ML model pipeline including components such as logs processing, feature extraction, dataset storage, model configuration management, model training, experiment frameworks, and serving deployment.
• Shipped ML products (NLP, computer vision, recommender systems, etc.) at scale to make business impact.
• Knows how to build appropriate abstractions and tooling to ensure MLEs are able to rapidly iterate on models.
• Prior AV experience

Our mission at Duolingo is to develop the best education in the world and make it universally available. It’s a big mission, and that’s where you come in!

At Duolingo, you’ll join a team that cares about finding innovative solutions to complex technical problems, running countless experiments (300+ at a time!) with our massive user base to make data-driven decisions, and educating our users and employees alike. You’ll have limitless learning opportunities, mentorship and collaboration with world-class minds, and a variety of projects with large scopes — while doing work that’s both fun and meaningful. 

Join our life-changing mission to develop education for our half a billion (and growing!) learners around the world.

Below you can find more information about the teams we're hiring for:

DevX AI Team

The DevX AI team empowers engineers at Duolingo to be more effective by introducing AI-infused tools that simplify workflows and reduce friction and toil. As a Software Engineer II on the team, you’ll contribute to building and improving AI-driven developer tools used across Duolingo’s engineering organization. You’ll work on projects that improve day-to-day developer workflows. You will help build and maintain systems that integrate AI into the software lifecycle, including contributing to production MCP infrastructure that connects to AI coding agents, with a focus on reliability, usability, and measurable impact.

🧠  You will...

• Collaborate on software projects with product design and backend aspects
• Develop, release, and maintain backend services and/or infrastructure
• Lead individual project priorities, milestones, and ensure we're always shipping
• Experience onboarding AI tools and changing engineering processes
• Focus on streamlining engineering processes with AI tools

✅  You have...

• A Bachelor’s degree in Computer Science or related technical field
• Strong competencies in data structures, algorithms, and software design
• Experience programming in Java, Python, or Kotlin
• Solid understanding of one or more database technologies

⭐  Exceptional candidates will have...

• Industry experience building high-traffic APIs using public cloud providers

About the Role:

The Perception Architecture team is responsible for tackling complex, cross-cutting issues in Stack AV’s approach to perception development and accelerating our delivery of perception capabilities. Many problems in the self-driving trucking industry are not well represented in the literature, requiring novel approaches or formulations to tackle challenging perception problems for the product.

As a Staff Software Engineer, you will drive the development of foundational ML architecture and systematic approaches to challenging, cross-cutting perception problems to ensure the self-driving system scales robustly and efficiently. This role will work closely with other teams to identify pain points and forward looking challenges, develop novel experiments to define future direction, and collaborate with other AI teams to convert these discoveries into product.

Responsibilities:

• Contribute to the technical direction of the Perception Architecture team and the broader perception organization.
• Design and develop core machine learning components and architectures that enable the perception system to scale and reliably address complex, long-tail driving scenarios.
• Drive the initial implementation and prototyping of key architectural decisions, such as handling long tail challenges or long range detection.
• Contribute to the improvement of model frameworks, architecture, and data pipelines to eliminate development bottlenecks and accelerate model iteration.
• Lead challenging technical topics and build consensus across sister teams (e.g., dynamic world, static world) and stakeholders to ensure autonomy-wide alignment.

Qualifications:

• BS (with 6+ years of experience), MS (with 5+ years of experience), or PhD (with 3+ years of experience) in a perception-related field, such as Robotics, Computer Vision, or Machine Learning.
• Extensive experience architecting, training, and deploying deep learning models into real-world, safety-critical environments.
• Track record of driving applied research or engineering projects from conception and experimentation to successful productization.
• Strong experience in software engineering, machine learning algorithm design, and building data/metrics pipelines for ML development.
• Fluency in Python and hands-on experience with C++.
• Experience delivering detection, segmentation, tracking, or other perception solutions for real-time robotic applications, with prior experience in sensor fusion being highly desirable.

About the Role:

The Trajectory Generation and Controls team owns the trajectory generation, optimization and controls components that power how autonomous trucks drive. You are a great fit if you have real world experience with Motion Planning, Optimization and/or Controls, as well as a proven record deploying real-time, safety critical software to production.  In this role you will work in all phases of the software development cycle, from solution design to implementation and deployment. You will collaborate with various teams in Autonomy, Systems Engineering and Operations, fostering a culture of safety, principled engineering and a customer oriented mindset.

Responsibilities:

• Own the delivery of TG&C features from design to integration, providing a well controlled, safe and comfortable vehicle experience.
• Provide input to team roadmaps and ensure timely delivery of features. 
• Work closely with systems engineers to ensure a safe, well tested product is delivered.
• Write state of the art software, using modern C++ and Python. Make extensive use of unit testing, simulation, and log simulation.
• Work closely with other Autonomy teams (Constraint Generation, Trajectory Selection, World Fusion, Prediction, ...) to design and deploy robust solutions, encompassing all aspects of autonomous driving. 
• Be involved in design and iteration exercises, and help to align stakeholders by using strong presentation and communication skills.

Qualifications: 

• BS in Computer Science, Electrical Engineering, Mechanical Engineering or closely related field. MS or PhD is a plus.
• 5+ years of software engineering experience. 
• Experience building motion planning and/or control systems for real world applications. Experience with large ground vehicles is a plus.
• Knowledge and experience with vehicle dynamics, system identification and real-time control algorithms. Experience with tooling and metrics is a plus.
• Experience with optimal control or dynamic optimization, especially in the context of model predictive control or real-time trajectory generation.
• Fluent in modern C++.
• Experience with Python.

About the Role:

The AI Data Infrastructure team sits between Stack’s data and its engineers. We provide tooling and infrastructure to process petabytes of data per month into actionable insights for ML and autonomy engineers. In particular, we provide data pipelines to run tasks on new data, services and frontend to search for data, and tooling to stream and transform data in our various data storage mediums.

• Data Processing: we are building state of the art infrastructure to support our scaling fleet and ML engineering needs. For this role, you would be responsible for ensuring our processing pipelines are customer friendly, fault tolerant and performant. 
• Data Search: we provide our end users with a platform that combines searching across our metadata stores, vector database, and parts of our data warehouse and then visualize the data (think YouTube). You would be coming in and working with ML and autonomy stakeholders to improve the reliability of the pipelines that feed into our search platform.  
• Data Lineage: we are getting started with building our data lineage system that will track the usage of all data at Stack. You would work with our stakeholders to ensure our system is widely adopted and address customer pain points.

Responsibilities:

• Play a key role in designing and building the next generation of data infrastructure.
• Build low latency/high throughput, fault tolerant  batch or stream processing systems.
• Build scalable backend services for data search and data curation systems.
• Write high quality Python and SQL.

Qualifications: 

• Proven track record of building scalable, reliable infrastructure in a fast-paced environment.
• Ability to collaborate effectively across teams.
• Strong development experience with Python and SQL.
• Prior experience with Apache Iceberg, Trino, SQLMesh, Flyte / Airflow, and Kubernetes are a plus.
• Prior experience building and managing data platforms for multimodal ML needs is a plus.
• Prior experience with agentic workflows is a plus.
• Prior experience in autonomous vehicles (AV) is a plus.

#LI-AW1

About the Role:

The ML Training Acceleration team is dedicated to increasing Stack AV's product development velocity by accelerating machine learning iterations. Our core mission is to deliver a training system that is reliable, scalable, user-friendly and observable. This involves profiling, optimizing, and fine-tuning our ML models, as well as evangelizing best practices and frameworks among Machine Learning Engineers (MLEs) across the company.

Responsibilities: 

• Analyze ML models to identify and resolve performance bottlenecks.
• Incorporate OSS tools to enable ML engineers self-sufficiently profile and optimize models.
• Deliver solutions to streamline model deployment across various hardware platforms.
• Collaborate with ML researchers to balance model accuracy and speed.
• Implement optimizations using CUDA, Triton, and custom kernels.
• Promote Engineering Excellence: Maintain a high bar for engineering excellence in their own work but also set a culture of engineering excellence within the team.

Qualifications: 

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• Experience: 5+ years of experience (including experience with GPU programming and optimization)
• Technical Skills:
  • Strong programming skills in C++ and Python
  • Proven experience in GPU programming and optimization
  • Familiarity with deep learning frameworks, especially PyTorch
  • CUDA programming
  • Triton language for GPU kernels
  • PyTorch optimization techniques
  • TensorRT implementation
  • ONNX model conversion and deployment
  • Custom GPU kernel development
  • Deep understanding of GPU architectures and performance optimization
• Problem-Solving: Strong analytical and problem-solving skills
• Communication: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders
• Autonomous vehicles (AV) experience is a bonus

About the Role:

In the ML Data Understanding team, our mission is to provide trusted and useful data to efficiently power all of Stack's ML applications end-to-end from mining to training to safety evaluation. We work hand in hand with AV autonomy teams to provide cutting edge solutions to all their data needs, working across data engineering, mining, modeling and infrastructure. In particular, we provide services to find (data mining), curate (datasets), annotate (data labeling), search and serve (high throughput data access) data for all ML needs.

• Data Mining: We are building a framework and infrastructure to find interesting events quickly and flexibly. As part of this mission, you would be setting the direction for and helping us build an inference service using LLMs, open-world models and vector databases.
• Semantic Search for Data Mining: We are building the infrastructure of a highly scalable semantic search service for multimodal data to find interesting events quickly and flexibly. As part of this mission, you would be setting the direction for and helping us build an inference service using the latest AI models & approaches. 
• Dataset management for training: We are building state of the art infrastructure to support machine learning training and inference workloads using OSS components such as Ray, Spark, Lance and Iceberg.

Responsibilities:

• Build state-of-art multimodal data mining and semantic search solutions to power AV product development.
• Develop data understanding platform infrastructure for real-time querying/vector databases and batch/stream processing using technologies like Ray, Spark, Lance, or similar.
• Deliver end-to-end data mining solutions that span onboard (C++) and offboard (ML & Data Infra) infrastructure to accelerate AV product development. 
• Develop e2e solution for real-time semantic search services (text/images/videos) and vector DBs.
• Discover and identify key issues in existing ML infra and proactively improve system performance.
• Build low latency/high throughput batch or stream processing pipelines.
• Drive technical discussions across multiple orgs and deliver solutions on a timely basis.
• Architect and tune ETL pipelines to maximize GPU/CPU/Ram utilization.
• Write readable and high-performance Python/C++ code.

Qualifications: 

• Experience with both ML platforms and building ML-based applications (modeling experience is a bonus).
• Proven track record of building scalable, reliable infrastructure in a fast-paced environment.
• Ability to collaborate effectively across teams.
• Experience building or using ML infrastructure for a large number of customer teams.
• Deep understanding of design trade-offs with the ability to articulate those trade-offs and achieve alignment with others.
• Experience in building ML models or infrastructure in domains such as autonomous vehicles, perception, and decision-making (desirable but not required).
• Experience with model training, model optimization, or large data processing pipelines.
• Prior experience in autonomous vehicles (AV) is a plus.
• 6+ years of experience with:
  • Multimodal data indexing and inference pipelines.
  • Building semantic search service, embedding generation for video/images and vector DB.
  • Large scale ML pipelines (Airflow/Flyte) and model optimization.