The Developer Productivity team supports the software development ecosystem used by thousands of engineers to develop and maintain MongoDB Server and developer tools in a complex matrix of multi-platform self-hosted products and multi-cloud hosted environments. Our tools and services, as well as our domain expertise, ensure that MongoDB developers can ship high quality products quickly, confidently, and securely. 

As a senior staff engineer you’ll be responsible for helping shape the future of our developer ecosystem and ensure we take advantage of the rapidly-evolving engineering landscape. You will partner with engineering leadership across the company to identify system-wide opportunities to accelerate developer velocity and efficiency, enhancing smart data collection and integrating analytics assistants that produce actionable insights every step of the way. 

Our team champions a strong culture of inclusivity, diversity, and collaboration. If you like working on a strongly collaborative team that loves to experiment, have a passion for open source, and generally thrive on being a champion for software development best practices  and data-informed decision making, this is the role for you.

Candidate Profile

• Minimum 10 years of experience in the developer productivity domain in large engineering environments, including building, and operationalizing complex, reliable systems
• Strong experience supporting one or more of C++, Java, Go, Python, Rust, Node, Typescript
• Strong understanding of DevOps culture and tooling
• Extensive experience with CI/CD pipelines, common deployment models and infrastructure, and various techniques used to safely roll changes into production environments
• Hands-on experience implementing metrics and observability tools and best practices, as well as data processing pipeline technologies such as Iceberg, Trino, Kafka
• Proven track record of driving technical execution and vision for organizations with 50+ team members
• Skilled in advocating for and representing the needs of a 1000+ customer base to internal platform teams

Responsibilities

• Drive strong operational excellence standards for internal software development tools and services, helping define golden paths that optimize the efficiency and velocity of our engineering organization
• Design software development systems to ensure best practices and champion industry standards
• Advise Engineering partners on technical implications of systems design with regards to complexity, testability, and maintainability
• Lead development and project management of some of the largest projects within MongoDB to improve developer productivity and software quality, particularly around data-driven insights and optimizations
• Provide technical mentorship and leadership to the Developer Productivity organization, promoting best practices and fostering team growth

Bonus points if the candidate has experience with:

• Strong systems fundamentals, including testing and monitoring multi-threaded systems
• Performance profiling of complex systems
• Leveraging best of breed AI tools & technologies in the developer productivity domain along with the necessary telemetry and analytics to measure effectiveness in a rapidly changing environment
• Experience supporting the development of database internals or core components for data processing systems

Success Measures

• In the first month, you will have understood the high level architecture of at least one of the systems within the MongoDB developer ecosystem and fixed a few bugs
• In three months, you will have a broad understanding of the overall developer ecosystem and be taking on code review responsibilities
• In six months, you will have taken on code and design review responsibilities for key systems within the software development ecosystem
• In twelve months, you will be leading the development of MongoDB’s next generation developer ecosystem, helping to mentor engineers on the team, and using your experience to drive the long-term DevProd-wide technical roadmap

About MongoDB

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

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

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

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

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

MongoDB is an equal opportunities employer.

Req ID: 1273376291

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 Toronto or Montreal office or remotely in the Canada 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

About the Company 

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.

A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight. 

Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. 

Meet the Team: 

As a Staff Machine Learning Engineer specializing in BEV (Bird’s-Eye View) and Multi-Modal Perception, you will lead the development of next-generation models that unify information across cameras, LiDAR and radar to deliver a rich spatial understanding of the driving environment. 

You will drive architectural innovation, large-scale model training, and data-driven improvements that directly advance the perception capabilities at the heart of Torc’s autonomous driving stack. 

This is a technical leadership role focused on model innovation and maturity, not downstream feature integration. 

What You’ll Do 

• Lead BEV model development: Define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding). 
• Design advanced multi-modal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations.
• Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations.
• Own large-scale training workflows — from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization.
• Advance model robustness and generalization, addressing long-tail conditions such as low visibility, occlusions, and rare scene configurations.
• Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance.
• Collaborate cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces.
• Mentor and guide ML engineers, cultivating best practices in experimentation, code quality, and model validation.
• Stay at the forefront of ML research, exploring self-supervised learning, large-scale pretraining, or foundation models for 3D perception. 

What You’ll Need to Succeed 

• 10+ years of experience in deep learning for perception, 3D vision, and/or autonomous systems
• M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or related field (or equivalent practical experience).
• Proven expertise in BEV modeling, 3D scene understanding, and multi-view fusion.
• Strong background in multi-modal sensor fusion, particularly integrating camera and LiDAR data.
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with large-scale data pipelines, distributed training, and experiment management systems.
• Demonstrated leadership in driving ML model innovation and mentoring technical teams. 

Bonus Points! 

• Experience with autonomous driving or robotics perception in production environments.
• Experience with MLOps and infrastructure tools (Ray)
• Hands-on expertise in BEV-based ML architectures, LiDAR-vision fusion, or spatial-temporal modeling.
• Familiarity with 3D labeling, calibration, and sensor simulation pipelines.
• Track record of publications or open-source contributions in top-tier venues (CVPR, ICCV, NeurIPS, ICRA, CoRL).
• Understanding of performance tradeoffs and deployment constraints (latency, memory, accuracy). 

Knowledge of English is required since the selected candidate will need to collaborate daily with English-speaking colleagues in the United States and work with technical documentation written exclusively in English. 

Work Location: For this position, we are open to hiring in either the Torc Montreal, Quebec (Canada) or Ann Arbor, MI (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States or Canada.

Perks of Being a Full-time Torc’r (Canada) 

Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers: 

• A competitive compensation package that includes a bonus component and stock options
• Medical, dental, and vision for full-time employees
• RRSP plan with a 4% employer match
• Public Transit Subsidy (Montreal area only)
• Flexibility in schedule and generous paid vacation
• Company-wide holiday office closures
• Life Insurance 

 At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities. Even if you don’t meet 100% of the qualifications listed for this opportunity, we encourage you to apply. 

Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. 

CAD Compensation Range: $209,300-313,800 CAD

Job ID: 102405

About the Company 

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.

A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight. 

Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. 

Meet the Team: 

As a Staff Machine Learning Engineer focused on Road & Lane Detection, you will lead the model development efforts that enable Torc’s autonomous vehicles to perceive and interpret road geometry, lane structures, and drivable surfaces with precision and robustness. 

You’ll define the next generation of deep learning architectures and data-driven approaches that extract high-fidelity road and lane semantics from multi-modal sensor data — driving critical improvements in perception accuracy, stability, and scalability. 

This is a technical leadership role focused on model innovation and maturity, not downstream feature integration. 

What You’ll Do 

• Own the model roadmap for Road & Lane Detection within the Model Dev ML org — from concept through production-grade model maturity. 

• Research, design, and train advanced neural architectures (e.g., multi-camera BEV transformers, LiDAR-vision fusion models, topological lane graph networks) to detect, segment, and model road structures and lane connectivity. 

• Lead data strategy for this domain — defining data curation, labeling policies, and active learning pipelines to capture long-tail scenarios (e.g., occlusions, complex merges, construction zones). 

• Develop robust metrics and evaluation frameworks for lane and road geometry accuracy, temporal consistency, and cross-domain generalization. 

• Advance foundational capabilities such as self-supervised pretraining, synthetic-to-real adaptation, and temporal modeling for road and lane understanding. 

• Drive large-scale experiments — designing, running, and analyzing results from distributed training workflows and ablations to identify scalable improvements. 

• Collaborate with other model dev/perception teams to ensure model coherence and interface consistency. 

• Mentor engineers and scientists, setting best practices for model training, evaluation, and code quality. 

• Stay ahead of the research frontier by evaluating and adapting emerging techniques (e.g., BEV-based large models, vectorized map prediction, lane graph transformers) to production-grade perception. 

What You’ll Need to Succeed 

• 10+ years of experience developing deep learning models for perception or computer vision at scale. 

• M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related field (or equivalent experience). 

• Deep expertise in semantic and instance segmentation, BEV modeling, or scene topology estimation. 

• Strong understanding of lane and road geometry modeling, camera calibration, and sensor projection. 

• Proficiency with Python and modern ML frameworks (e.g., PyTorch, Lightning). 

• Experience with distributed training pipelines, experiment management, and large-scale dataset handling. 

• Proven leadership in guiding technical roadmaps, mentoring engineers, and driving measurable model improvements. 

Bonus Points! 

• Experience developing ML models for autonomous driving, mapping, or ADAS systems. 

• Familiarity with multi-modal fusion (camera, LiDAR, radar, HD maps). 

• Hands-on experience with BEV-based and topological prediction models. 

• Contributions to perception-related ML research (CVPR, NeurIPS, ICCV, ICLR, ICRA). 

• Strong intuition for data quality, bias mitigation, and uncertainty modeling. 

Work Location: For this position, we are open to hiring in either the Torc Montreal, Quebec (Canada) or Ann Arbor, MI (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States or Canada.

Perks of Being a Full-time Torc’r (Canada) 

Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers: 

• A competitive compensation package that includes a bonus component and stock options
• Medical, dental, and vision for full-time employees
• RRSP plan with a 4% employer match
• Public Transit Subsidy (Montreal area only)
• Flexibility in schedule and generous paid vacation
• Company-wide holiday office closures
• Life Insurance 

 At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities. Even if you don’t meet 100% of the qualifications listed for this opportunity, we encourage you to apply. 

Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. 

Hiring Range for Job Opening  

CAD Compensation Range:

$209,300-313,800 CAD

Job ID:  R-102402

Machine Learning Engineer – Biometrics (Computer Vision)

We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process.

What You’ll Do

• Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition)
• Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions.
• Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX
• Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps.
• Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS.
• Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.

What We’re Looking For

• Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis.
• Deep expertise in computer vision and biometrics, especially face recognition.
• Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact.
• Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code.
• Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow.
• Cloud Native: Hands-on experience scaling training jobs on multi-GPU clusters and deploying services on AWS (SageMaker, EC2, EKS).

Nice to Have

• Research Publications: Papers in CVPR, ICCV, ECCV, or FG related to face recognition, image quality assessment, or fairness.
• Large Scale Search: Experience with vector databases (e.g., Milvus, Faiss) and approximate nearest neighbor (ANN) search algorithms.
• Familiarity with privacy, security, and compliance in biometric systems.
• Mobile/Edge Experience: Experience porting models to edge or mobile devices utilizing frameworks such as CoreML, LiteRT, and/or TFLite.
• Synthetic Data: Experience using GANs or diffusion models to generate synthetic faces for training.
• Strong communication skills.