Source Job

• Develop machine learning models that interpret and predict road and lane structures.
• Work with multi-modal sensor data, including cameras, LiDAR, and radar, to create 3D representations of driving surfaces.
• Optimize algorithms for real-world deployment, influencing vehicle navigation and safety.

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements.

• Train, evaluate, and optimize machine learning models for high performance.
• Contribute to R&D in object detection and multi-object tracking for remote sensing.
• Design and deliver production-grade, maintainable code while managing multi-phase development.

Clarifai is a leading AI platform specializing in computer vision and generative AI. They empower organizations to transform unstructured data into actionable insights. Their globally distributed team operates across the United States, Canada, Estonia, Argentina, and India and is committed to building a diverse and inclusive team.

• Develop and optimize computer vision algorithms for road model detection.
• Design and implement deep learning models for road model inference in BEV frameworks.
• Collaborate with robotics, software, and hardware engineering teams for seamless integration.

Torc has been a leader in autonomous driving since 2007, commercializing solutions with experienced partners and now focused on developing software for automated trucks.

• Convert and Ship CVML R&D ideas to Axon Products.
• Research and develop advanced MLLMs, GenAI, and Computer Vision techniques for cloud, devices and sensors from multimodal data sources.
• Design and implement efficient and scalable MLLM models for inference and analysis of visual data.

Axon is on a mission to Protect Life with their ecosystem of devices and cloud software. Life at Axon is fast-paced, challenging and meaningful with a focus on growth and making a difference.

• Design and implement production-grade auto-labeling pipelines that generate 3D and 4D annotations from multi-modal robot data at scale.
• Develop data-centric learning workflows that connect auto-label outputs, Serve’s dataset infrastructure, and continuous E2E model training and evaluation pipelines.
• Lead initiatives in self-training, weak supervision, and simulation-to-real adaptation to reduce manual labeling dependency and accelerate model iteration cycles.

Serve Robotics is reimagining how things move in cities with their sidewalk robot designed to take deliveries away from congested streets and benefit local businesses.

• Build scalable training pipelines and generating high-fidelity synthetic scenarios.
• Design procedural simulation environments and create diverse long-tail edge cases.
• Optimize RL systems to train robust foundational models.

At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future.

• Design and deliver advanced solutions that generate predictions from a wide range of Computer Vision models.
• Build and evolve key components of the Roboflow Platform to ensure seamless, reliable model deployment at scale.
• Contribute to and maintain Roboflow’s open-source projects, helping grow and support the broader developer community.

Roboflow simplifies building and using computer vision models, and over 1M+ developers, including those from half the Fortune 100, use Roboflow’s tools.

• Design, build, and optimize high-performance systems in C++ supporting AI data pipelines and evaluation workflows.
• Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control.
• Improve reliability, performance, and safety across existing C++ codebases.

Alignerr connects top technical experts with leading AI labs to build, evaluate, and improve next-generation models. They work on real production systems and high-impact research workflows across data, tooling, and infrastructure.

• Apply state-of-the-art AI/ML techniques to develop cybersecurity solutions addressing complex real-world challenges.
• Collaborate with cross-functional teams to translate prototypes into production-ready systems.
• Contribute to research initiatives, keeping the team at the forefront of AI/ML and cybersecurity innovations.

This position is posted by Jobgether on behalf of a partner company.

• Invent and prototype new model architectures that optimize inference speed, including on edge devices.
• Build and maintain evaluation suites for multimodal performance across a range of public and internal tasks.
• Collaborate with the data and infrastructure teams to build scalable pipelines for ingesting and preprocessing large audio datasets.

Liquid AI, spun out of MIT, aims to build efficient AI systems at every scale, creating Liquid Foundation Models that operate on-device and under real-time constraints.

• Build, maintain and improve Torc ML frameworks.
• Use Terraform, AWS Managed Services, EKS, Ray.
• Focus on data ops, ML development pipeline, logging & aggregation.

Torc has been a leader in autonomous driving since 2007. Now a part of the Daimler family, they are focused solely on developing software for automated trucks to transform how the world moves freight. Their culture is collaborative, energetic, and team focused.

• Develop and evaluate AI-based biomarkers using multimodal data.
• Design, implement, and improve machine-learning models to predict patient outcomes and treatment response.
• Conduct research and experimentation to improve model performance, robustness, generalizability, and interpretability.

Artera is an AI startup that develops medical artificial intelligence tests to personalize therapy for cancer patients. They are on a mission to personalize medical decisions for patients and physicians on a global scale and value bringing together individuals from diverse backgrounds.

• Contribute to our core ML infrastructure.
• Prototype new training frameworks and production-ize solutions at scale.
• Design, optimize and test model integration infrastructure.

Clarifai is a leading, full-lifecycle deep learning AI platform for computer vision, natural language processing, LLM's and audio recognition. Clarifai was founded in 2013 and has employees remotely based throughout the United States, Canada, Argentina, India and Estonia.

• Develop and deploy computer vision models for facial recognition, object detection and scene categorization.
• Build scalable pipelines for processing large volumes of image data and generating structured metadata.
• Design and implement recommendation systems leveraging behavioral analytics and transformer-based models.

Jobgether uses an AI-powered matching process to connect candidates with companies. They ensure applications are reviewed quickly and fairly, and their system identifies top-fitting candidates for the hiring company.

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.

At JetBrains, code is their passion and they strive to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in their IDEs.

• Design and build simulation scenarios representing nominal, edge-case, and adversarial conditions.
• Translate autonomy requirements and real-world incidents into reproducible tests with clear, measurable outcomes.
• Script environments, actors, triggers, and behaviors in Isaac Sim or Unreal Engine.

Serve Robotics is actively changing how items are moved via sidewalk robots which provide deliveries without street congestion. They are a team of tech industry veterans in software, hardware, and design are pooling their skills to build the future; their team is agile, diverse, and driven.

• Own the end-to-end lifecycle of ML model deployment—from training artifacts to production inference services.
• Design, build, and maintain scalable inference pipelines using modern orchestration frameworks (e.g., Kubeflow, Airflow, Ray, MLflow).
• Implement and optimize model serving infrastructure for latency, throughput, and cost efficiency across GPU and CPU clusters.

MARA is building a modular platform that unifies IaaS, PaaS, and SaaS which will enable governments, enterprises, and AI innovators to deploy, scale, and govern workloads across data centers, edge environments, and sovereign clouds. They are redefining the future of sovereign, energy-aware AI infrastructure.