Mission Summary:

Motional’s onboard autonomous driving system team works at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional’s next-generation on-board autonomous driving system. As part of this team, the High Performance Compute Platform team is responsible for Motional’s current and next-generation onboard autonomous driving system. If you are a software engineer and love the idea of working on embedded AI hardware and software compute systems to create the next generation of autonomous vehicles, we would love to talk with you. We are hiring in a range of levels, from graduate engineers to Staff engineers.

What You'll Be Doing:

• Design and develop infrastructure software on various hardware platforms for applications such as vision processing, radar systems, safety monitoring, etc., to be run on self-driving vehicles.
• Design test harnesses for embedded software components and full systems
• Provide technical mentorship to engineers.
• Proactively work with cross-functional engineering teams to solve complex and interesting problems.
  
  

What We're Looking For:

• Experience with creating detailed requirements from use cases.
• Ability to lead a technical initiative, including breaking down work and guiding other engineers through execution.
• Experience writing software for embedded platforms in C and C++.
• Experience with Test-Driven Development (TDD).
• Experience working on embedded Linux / RTOSs.
• Experience working with networks (Ethernet, CAN etc.) and the common networking protocols.
• Experience with debugging on embedded platforms.
• Experience writing software in Python and experience doing automation with shell scripting.
• Experience working with ARM Cortex MCUs or Microprocessors.
  
  

Bonus Points (not required):

• Experience working with large data pipelines, and platforms that require deterministic execution.
• Experience using inter-system communication protocols such as I2C and SPI
• Experience deploying Machine Learning models.
• Experience working with GPUs.
• Experience working directly with the Linux kernel or Device Drivers.
• Experience with Simulation and Code Generation, and knowing when their use is appropriate.
• Experience working with Bazel.
• Experience integrating various sensors, including Cameras, IMUs, Radars, LIDARs.
• Experience with PyTorch, TensorFlow, ONNX, and/or other ML 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.

Shopmonkey's vision is to help every shop thrive by equipping them with the tools they need to run and grow their business. Our cloud based all-in-one shop management software takes owners and technicians from quote to cashing out a satisfied customer. Our software has a modern and intuitive UI and our backend is powered by the latest technologies so our clients can focus on the things they do best. As a Senior ML Engineer at Shopmonkey, you will be a part of a globally distributed engineering team working closely with your product and design counterparts. You will have the chance to work on the frontier of ML modeling, data pipelines, MLOps, and agentic AI systems to meet real-world auto shop needs. Shopmonkey has the structured data, workflows, and operational maturity to deliver ML powered systems that are not only intelligent but trusted and useful. You’ll move fast to bring ML systems from discovery all the way through production, helping to shape the future of the automotive care experience. For any Bay area based candidates, this would be hybrid with 2-3 days/week office at our Morgan Hill, CA office for collaboration. 

What you will do:

• Design, build, and ship production-ready ML models across a range of problem spaces: regression, classification, clustering, ranking, and recommendation systems.
• Conduct end-to-end development of ML systems: data gathering, experimentation, feature engineering, model training, evaluation, deployment, and monitoring.
• Define and track model performance metrics, run A/B tests, and iterate based on real-world feedback.
• Help design and implement shared feature stores so that reusable features can serve multiple models consistently in both batch and real-time contexts.
• Work within a modern MLOps environment to ensure scalable and reliable deployment of models.
• Contribute to training infrastructure, model versioning, and CI/CD pipelines for ML workflows.
• Work closely with data scientists and data engineers to develop data driven solutions that are high impact for businesses.
• Translate complex ML workflows into digestible updates for cross-functional stakeholders.
• Contribute to backlog velocity by owning appropriate tickets and delivering high-impact work in a collaborative, fast-paced environment.
• Implement NLP and LLM-powered components for sentiment analysis, real-time conversation evaluation, and behavior optimization.
• Contribute to analytics and predictive features such as no-show prediction and sentiment dashboards.
• Help build and ship AI agents that help automate key auto-shop business processes.

We are looking for people who have:

• Minimum of 5+ years of industry experience in applied machine learning; advanced degrees (Master’s or PhD) may offset years of experience.
• Proven experience shipping models into production (not just proof-of-concepts or notebooks).
• Proficiency in Python; experience with ML frameworks like PyTorch or Tensorflow.
• Strong foundations in classical ML/DL. Including some of the following: regression, classification, clustering, ranking, feature engineering, model evaluation, and experimentation.
• Bachelor’s degree in a STEM field, or equivalent practical experience.
• Strong collaboration and communication skills—comfortable working with PMs, designers, engineers and other cross functional team members.

Nice to have:

Candidates should have some experience in one or more of the following areas.

• Understanding of MLOps principles: model versioning, orchestration, evaluation, monitoring, model serving, and CI/CD for ML. 
• Understanding of MLOps, and experience with modern tooling like MLFlow, DVC, Airflow, etc.
• Experience with LLMs and NLP frameworks (e.g., TensorFlow, Hugging Face, LangChain).
• Experience with/interest in LLM workflows and agentic workflows
• Cloud infrastructure experience, (e.g. GCP, AWS).
• Familiarity with vector databases (e.g. Pinecone, pgvector) and embedding-based retrieval or similarity search.
• Strong SQL skills for working with large-scale data.
• Experience designing or contributing to feature stores (e.g. Feast, VertexAI Feature Store, Tecton) for shared, reusable feature pipelines.

Bonus Points:

• Prior experience working at a high growth startup.
• Experience building consumer-facing agents in vertical SaaS, in the automotive industry (business or consumers).
• Background in data processing or real-time analytics.
• Experience with Snowflake or other large-scale data warehouse solutions.

In the United States, the range is typically a salary of $165,000 to $200,000 + bonus + equity + benefits. The range provided is Shopmonkey’s reasonable estimate of the base compensation for this role. The actual amount will be based on job-related and non-discriminatory factors such as location, experience, training, skills, and abilities. Consult with your Recruiter during the initial call to determine a more targeted range based on these job-related factors. In addition to this base compensation company stock options and benefits as outlined below are included.

Location: 439th CES/CER, 250 Patriot Avenue, Westover ARB, MA 01022 (Onsite)

Citizenship: US Citizen (MUST)

Security Clearance: Requires successful completion of NACI/SF85 (Public Trust) background investigation.

Position Overview:

The Support Specialist provides on-site, junior-level technical support to the 439th Civil Engineer Squadron (CES) at Westover ARB to help operate and sustain AI-enabled solutions in a secure DoD environment. Working under guidance from senior engineers and government stakeholders, the role supports day-to-day platform operations, basic data/model updates using established procedures, user support, and documentation for AI/ML-enabled capabilities (e.g., contract analysis, energy management, and snowfall recovery).

This position contributes to mission readiness by helping operationalize and improve critical AI-driven platforms for military engineering, utilities, contracting, and emergency response.

Key Responsibilities:

• AI/ML support: Run established training/evaluation scripts and assist with basic model validation, tuning experiments, and periodic updates under supervision.
• Deployment & integration support: Assist with deployments in secure, on-site/air-gapped environments by following checklists and runbooks; support integration testing and coordinate tickets with DoD IT/cyber teams.
• Documentation: Update and maintain technical notes, user guides, and training materials using provided templates and standards.
• Training support: Assist with recurring user training and help translate technical workflows for non-technical users; capture feedback and improvement requests.
• Security: Follow DoD cybersecurity requirements; protect CUI/FCI; comply with privacy and confidentiality rules.
• Sustainment: Perform basic monitoring and health checks; execute approved patches/updates using runbooks; troubleshoot common issues and escalate complex problems.
• Reporting: Support labor-hour reporting and contract deliverables on Government timelines.
• Coordination: Log, track, and triage support tickets/incidents; communicate status updates; coordinate with the Resources Flight Chief and technical leads to resolve issues.

Required Qualifications:

• Education: Bachelor’s degree in computer science, engineering, data science, or related field.
• Experience: Minimum of 2 years’ direct hands-on experience or commensurate education in artificial intelligence, machine learning, data science, or a related field within secure DoD or comparable environments.
• Technical Skills: Working knowledge of Python; basic Linux command line; Git/version control; ability to run and troubleshoot scripts; familiarity with at least one ML library (e.g., scikit-learn or PyTorch/TensorFlow) and basic data handling (CSV/SQL fundamentals).
• Security & compliance: Ability to follow secure handling procedures for CUI/FCI and comply with DoD/site security requirements; willingness to complete required security and privacy training.
• Communication: Clear verbal and written communication for technical and non-technical audiences; able to draft documentation and provide status updates with guidance.
• Professionalism: Present a professional appearance; display contractor identification badges at all times; adhere to all base regulations and government conduct policies.

Additional Requirements:

• U.S. citizenship; eligible for base access.
• Occasional travel to other AFRC locations, as authorized.
• Compliance with privacy, safety, and physical/environmental security rules.
• Maintain service continuity and follow contract staffing constraints.
• Maintain personally issued computing equipment per contract terms.

Preferred Qualifications:

• Prior support for .mil/.gov AI, data, or software solutions (internships/projects count).
• Familiarity with DoD acquisition/contracting and compliance processes.
• Familiarity with containers (Docker) and/or basic CI/CD concepts; interest in automation/scripting to improve repeatable support tasks.

About the Role:

Stylus Medicine is seeking an intellectually curious and creative Director, who will serve as a key scientific leader on our Platform team, owning the computational strategy that drives large serine recombinase (LSR) engineering and clinical translation. This role sits at the intersection of protein engineering, multi-omics analysis, and AI/ML-driven design and will lead a high-impact team spanning NGS and computational biology while driving the analytical infrastructure and predictive capabilities needed to advance our proprietary LSR platform from discovery through translation.

Key Responsibilities & Accountabilities:

• Lead and develop a team of NGS and computational biology scientists, setting technical direction, mentoring team members, and fostering a collaborative, high-performance culture
• Lead computational efforts to design and optimize LSRs and related genome engineering proteins for genetic medicine applications, integrating sequence-function, structure-function, and protein-DNA interaction datasets to guide engineering campaigns
• Lead analysis of NGS data from LSR experiments — including amplicon sequencing, WGS, hybrid capture, and long-read sequencing — to quantify on-target efficiency and genome-wide specificity
• Build, apply, and critically evaluate predictive and generative models for protein variant and library design — including rigorous benchmarking of AI/ML approaches such as protein language models, structure prediction tools, and generative design frameworks
• Integrate proprietary screening data with public evolutionary and structural datasets to prioritize variants and identify engineering levers for activity, specificity, and developability
• Oversee the design, implementation, and deployment of robust, scalable computational pipelines for multi-omics data analysis, ensuring accuracy, reproducibility, and maintainability
• Provide scientific input into platform strategy, translating computational insights into actionable directions for the broader team
• Partner closely with the Screening, Genome Engineering, and NGS teams to design experiments, interpret complex datasets, and refine engineering hypotheses
• Contribute to patent filings, regulatory submissions, and publications

Qualifications & Skills:

• PhD in Computational Biology, Bioinformatics, Protein Engineering, Machine Learning or a related quantitative discipline, with 10+ years or Master’s with 14+ years of industry experience
• Demonstrated research in protein modeling, protein design, or computational biology through publications, preprints, or equivalent industry contributions
• Hands-on experience applying AI/ML methods to protein variant and library design, including protein language models, structure prediction, and generative design approaches (e.g., AlphaFold2/3, RFdiffusion, ProteinMPNN, ESM)
• Expert-level proficiency in Python and/or R, with deep familiarity with data science libraries (e.g., Pandas, NumPy, Bioconductor, SciPy, PyTorch, or JAX)
• Experience with cloud computing platforms and workflow management systems
• Excellent communication and documentation skills, with a track record of effective collaboration in fast-paced, interdisciplinary environments

Pay Range; $245,000 - $265,000

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 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 seasoned expert in Perception Capabilities, you'll field cutting-edge software products and solutions using state-of-the-art computer vision and deep learning. Your breadth of experience spans critical problem areas like detection, classification, segmentation, and tracking, along with essential supporting functions such as data engines, labeling policies, training pipelines, model optimization, simulation, and testing. You're driven by seeing your work significantly impact the product, taking initiative and owning outcomes.

You're eager to apply these skills to the perception stack of self-driving vehicles. You understand the unique challenges of safety-critical, open-world applications and are committed to ensuring engineering excellence and rigorous evaluation.

You'll devise creative solutions to complex problems, collaborating with interdisciplinary, capability-focused teams across the organization to design, implement, and validate end-to-end solutions.

What you’ll be doing:

• Lead the development and implementation of solutions that span across perception components and directly interface with downstream modules in the autonomy system.
• Analyze the performance of the perception subsystem to proactively identify potential limitations with respect to a set of system-level capabilities. Document potential hazards and lead the cross-team efforts to eliminate or mitigate them.
• Collaborate on innovative solutions to address “long tail” challenges. For example, developing techniques for synthetic data generation, advanced data mining, or leveraging language embeddings in our onboard models.
• Depending on your background and interests, you may also help optimize and deploy models to the vehicle, ensuring they operate within the constraints of our onboard hardware and meet our functional requirements and interface specifications.
• Interface with systems and safety engineers, test engineers, and data analysts to ensure appropriate requirements, test coverage, and performance metrics

What We’re Looking for:

• Master's or Ph.D. in Machine Learning, Computer Science, Robotics, or a related field; or equivalent industry experience
• Strong leadership skills in executing large, complex technical initiatives
• Track record of successful interdisciplinary work and product-focused engineering
• Broad understanding of Deep Learning algorithms, architectures, and applications
• Experience designing, training, and analyzing neural networks for applications relevant to perception
• Fluency in Python, including common libraries for deep learning (PyTorch), scientific computing, and data analysis and visualization
• Advanced knowledge of software engineering principles, including software design, source control management, build processes, code reviews, and  testing methods 
• Excellent communication and interpersonal skills
• Experience mentoring and leading others

Bonus Points:

• Experience shipping a “real” product and balancing ongoing support with next-generation innovation
• Experience developing safety-critical products or components
• Publications in relevant conferences (CVPR, ICML, NeurIPS, etc.)
• Strong programming skills in C++ and/or CUDA programming
• Experience with TensorRT and model quantization

You’re an engineer who is excited to work on cutting-edge software products and solutions using state-of-the-art computer vision and deep learning. You have a solid foundation in areas like detection, classification, segmentation, or tracking—whether through previous roles, internships, or research—and you’re eager to apply those skills in an impactful, production-focused environment.

You’re curious, motivated, and ready to learn from experienced teammates. You enjoy solving challenging technical problems, asking thoughtful questions, and collaborating with others to build high-quality software.

You’re excited about contributing to the perception stack for self-driving vehicles and learning what it means to build technology for safety-critical, open-world applications.

What you’ll be doing:

• Contribute to the design, development, and improvement of perception components that support downstream modules within the autonomy system.
• Help analyze perception system performance to identify areas for improvement and participate in efforts to address them.
• Work with teammates to explore creative approaches to “long-tail” challenges—for example, experimenting with synthetic data, data mining techniques, or new model architectures.
• Depending on your background and interests, you may also help optimize and deploy models to the vehicle, ensuring they operate within the constraints of our onboard hardware and meet our functional requirements and interface specifications.
• Interface with systems and safety engineers, test engineers, and data analysts to ensure appropriate requirements, test coverage, and performance metrics

What We’re Looking for:

• Master's or Ph.D. in Machine Learning, Computer Science, Robotics, or a related field; or equivalent industry experience
• Solid understanding of fundamental deep learning concepts and computer vision techniques
• Experience building, training, or evaluating neural networks
• Proficiency in Python and familiarity with libraries like PyTorch or similar
• Exposure to software engineering fundamentals—version control, code reviews, testing, and collaborative development
• Clear communication skills and willingness to ask questions, share ideas, and work with others

Bonus Points:

• Previous experience with sensor fusion and/or previous exposure to the AV industry
• Experience shipping a “real” product and balancing ongoing support with next-generation innovation
• Exposure to safety-critical products or components
• Publications in relevant conferences (CVPR, ICML, NeurIPS, etc.)
• Strong programming skills in C++ and/or CUDA programming
• Experience with TensorRT and model quantization

On our Behaviors team (Prediction and ML Planner), you will have the opportunity to work with world-class ML engineers, whose mission is to make self-driving vehicles a reality and to create a positive social impact. The Behaviors team develops ML models that learn how different agents navigate complex real-world traffic scenarios. We leverage these models to set the behavior of our self-driving vehicle, and predict the behavior of other agents such as vehicles and vulnerable road users. 

We are looking for proven leaders and technical experts who are passionate about Level 4 autonomous driving technology, excited by intellectual challenges, and interested in pursuing career growth with a fast-growing company.

What You’ll Be Doing:

• Define and execute motion planning and prediction projects that improve our self-driving vehicles’ capability to safely, comfortably and legally navigate complex traffic scenarios
• Lead, manage and grow a team of engineers
• Design and lead the implementation of behavior models that leverage the latest advancements in machine learning, generative AI and reinforcement learning
• Productionize and deploy solutions onto autonomous vehicle fleets
• Collaborate with perception, simulation, data platform and integration teams to validate and enhance your products’ on-road performance
• Communicate strategies, progress and challenges to executive leadership

What We’re Looking for:

• Masters or Ph.D. in Computer Science or a related technical field; or equivalent industry experience
• Proven leadership skills at executing large, complex technical initiatives
• Extensive experience managing and leading engineers
• Experience with deep learning frameworks such as TensorFlow or PyTorch
• Fluency in Python, including standard scientific computing libraries
• Proven track record of designing, developing and deploying ML solutions for autonomous vehicles or robotics
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, testing methods 
• Excellent communication and interpersonal skills

Bonus Points:

• Experience with embedded systems and real-time optimization, especially in the autonomous driving industry
• Experience with state of the art generative AI, and/or reinforcement learning, paradigms
• Proven track record of publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• Strong programming skills in C++ and/or CUDA programming

On Behaviors, you'll have the opportunity to work with world-class ML engineers and research scientists to make self-driving vehicles a reality and create positive social impact. Our team works on the tech stack responsible for understanding highly complex driving scenes and generating safe, comfortable driving trajectories for our robotaxi using the latest deep neural networks. We are looking for engineers who are passionate about Level 5 autonomous driving, excited by intellectual challenges, and interested in pursuing career growth with a fast growing company.

What You’ll Be Doing:

You will be directly responsible for training, evaluation, and deployment of our ML-based scene understanding, behavior prediction, and planning models. Day to day responsibilities include:

• Designing and executing high value experiments based on collaborative input from other machine learning engineers
• Prototyping and implementing continuously improving metrics to evaluate the performance of our behavioral models across many desired scenarios and robotaxi capabilities
• Timely release of major model updates, including analysis of both off-line and on-road evaluation data
• Staying up to date with the latest trends in our fast moving industry, and proposing new architectures and network designs based on published literature
• Maintaining a high quality training and evaluation code-base, including portions of our training stack dedicated to dataset generation and evaluation

What We’re Looking For:

• In-depth understanding of common Machine Learning and Deep Learning algorithms
• Experience designing, training, and analyzing neural networks for at least one of the following applications: motion planning, object detection, sensor fusion, motion prediction, and/or multi-object tracking
• Strong track record of tackling less well-defined problems, and executing efficiently to deliver the successful outcome
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, and  testing methods
• Experience with PyTorch or other python-based deep learning frameworks
• Experience working with large data sets and deriving insights from data
• Experience mentoring and collaborating with others

Education
Masters or PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics or a related field; or equivalent industry experience

Bonus Points

• Publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• PhD in machine learning, computer vision, or robotics vision
• Experience working on autonomous vehicles or related fields
• Experience in deploying models into real-world environments
• Strong programming skills in C++ and/or CUDA programming
• This opportunity can support remote work within the United States, with occasional travel.

Working Conditions

While performing the duties of this job, the employee is frequently required to convey and detect communication

The employee occasionally required to remain stationary at times for up to 50% of working time, move in designated working areas; & operate and use office equipment

The employee may occasionally move up to 50 pounds

On Behaviors, you'll have the opportunity to work with world-class ML engineers and research scientists to make self-driving vehicles a reality and create positive social impact. Our team works on the tech stack responsible for understanding highly complex driving scenes and generating safe, comfortable driving trajectories for our robotaxi using the latest deep neural networks. We are looking for engineers who are passionate about Level 5 autonomous driving, excited by intellectual challenges, and interested in pursuing career growth with a fast growing company.

What You’ll Be Doing:

You will be directly responsible for training, evaluation, and deployment of our ML-based scene understanding, behavior prediction, and planning models. Day to day responsibilities include:

• Designing and executing high value experiments based on collaborative input from other machine learning engineers
• Prototyping and implementing continuously improving metrics to evaluate the performance of our behavioral models across many desired scenarios and robotaxi capabilities
• Timely release of major model updates, including analysis of both off-line and on-road evaluation data
• Staying up to date with the latest trends in our fast moving industry, and proposing new architectures and network designs based on published literature
• Maintaining a high quality training and evaluation code-base, including portions of our training stack dedicated to dataset generation and evaluation

What We’re Looking For:

• In-depth understanding of common Machine Learning and Deep Learning algorithms
• Experience designing, training, and analyzing neural networks for at least one of the following applications: motion planning, object detection, sensor fusion, motion prediction, and/or multi-object tracking
• Strong track record of tackling less well-defined problems, and executing efficiently to deliver the successful outcome
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, and  testing methods
• Experience with PyTorch or other python-based deep learning frameworks
• Experience working with large data sets and deriving insights from data
• Experience mentoring and collaborating with others

Education
Masters or PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics or a related field; or equivalent industry experience

Bonus Points

• Publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• PhD in machine learning, computer vision, or robotics vision
• Experience working on autonomous vehicles or related fields
• Experience in deploying models into real-world environments
• Strong programming skills in C++ and/or CUDA programming
• This opportunity can support remote work within the United States, with occasional travel.

Working Conditions

While performing the duties of this job, the employee is frequently required to convey and detect communication

The employee occasionally required to remain stationary at times for up to 50% of working time, move in designated working areas; & operate and use office equipment

The employee may occasionally move up to 50 pounds

Motional’s global headquarters are located at 100 Northern Avenue in Boston, MA. Nestled in the bustling Seaport district with sweeping views of Boston Harbor and downtown Boston, the office is located close to major transit lines and a quick walk to various restaurants and popular attractions.

Mission Summary:

Motional’s CORE team is responsible for our vehicle’s Compute and Onboard Runtime Environment. We are creating a world leading AI compute platform for autonomous vehicles. This is the system that executes the software and neural networks that make our vehicles autonomous. 

The Next-Gen Technologies team is part of CORE. We work at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional’s on-board vehicle architecture. If you are a software engineer and love the idea of working on embedded AI hardware and software systems to create the next generation of autonomous vehicles, we would love to talk with you. 

What You’ll Be Doing:

As a senior engineer in the Next-Gen Technologies team, you will help us improve the compute performance of our current generation and next-generation autonomous driving systems through participating in full lifecycle development from idea to proofs-of-concept to production. 

Specifically, you will:

• Focus deeply on ML model deployment, integration of multiple ML models, and ML model optimization on embedded compute platforms.
• Dive deep into the full ML software stack. Analyze ML workload performance on a variety of hardware processors, optimize ML models, improve ML software, and help us continually improve our stack through the application of efficient and effective ML approaches. 
• Design, develop, test, integrate, and optimize software and tools on a variety of ML compute architectures.
• Collaborate with deep learning experts in perception, prediction, and other autonomous driving application areas to enable algorithms on GPU, NPU, and other ML accelerator architectures.
• Optimize the utilization of GPU/NPU resources and sharing of GPU/NPU access across multiple programs running on the same system.
• Lead designs to determine the needs of the system and how to best meet those needs through continually improving our ML software stack.
• Advise peers and management on technical matters.

What We’re Looking For:

• Experience with machine learning accelerators, including GPUs, NPUs, TPUs, and their programming environments, including CUDA, TensorRT, or similar technologies.
• Strong experience with modern C++ development in a Linux environment.
• Experience with parallel and high-performance computing.
• Comfortable with experimentation and evaluating different options as we work towards finding solutions that work. 
• A degree in Software Engineering, Computer Science, Electrical or Electronic Engineering, or similar technical field of study, or you have equivalent knowledge gained through your practical experience.

Preferred, but not required:

• Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks. 
• Experience with embedded systems development for ARM-based system-on-chip architectures.
• Experience working in a MLOps or DevOps environment.
• Passion for self-driving technology and its potential for positive impact on the world.
  
  

This role is hybrid from our Boston office. It requires two in-office days each week, ideally Tuesday and Thursday.

On our Behaviors team (Prediction and ML Planner), you will have the opportunity to work with world-class ML engineers, whose mission is to make self-driving vehicles a reality and to create a positive social impact. The Behaviors team develops ML models that learn how different agents navigate complex real-world traffic scenarios. We leverage these models to set the behavior of our self-driving vehicle, and predict the behavior of other agents such as vehicles and vulnerable road users. 

We are looking for proven leaders and technical experts who are passionate about Level 4 autonomous driving technology, excited by intellectual challenges, and interested in pursuing career growth with a fast-growing company.

What You’ll Be Doing:

• Define and execute motion planning and prediction projects that improve our self-driving vehicles’ capability to safely, comfortably and legally navigate complex traffic scenarios
• Lead, manage and grow a team of engineers
• Design and lead the implementation of behavior models that leverage the latest advancements in machine learning, generative AI and reinforcement learning
• Productionize and deploy solutions onto autonomous vehicle fleets
• Collaborate with perception, simulation, data platform and integration teams to validate and enhance your products’ on-road performance
• Communicate strategies, progress and challenges to executive leadership

What We’re Looking for:

• Masters or Ph.D. in Computer Science or a related technical field; or equivalent industry experience
• Proven leadership skills at executing large, complex technical initiatives
• Extensive experience managing and leading engineers
• Experience with deep learning frameworks such as TensorFlow or PyTorch
• Fluency in Python, including standard scientific computing libraries
• Proven track record of designing, developing and deploying ML solutions for autonomous vehicles or robotics
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, testing methods 
• Excellent communication and interpersonal skills

Bonus Points:

• Experience with embedded systems and real-time optimization, especially in the autonomous driving industry
• Experience with state of the art generative AI, and/or reinforcement learning, paradigms
• Proven track record of publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• Strong programming skills in C++ and/or CUDA programming

Role overview

The Manager, Data Science will lead an Inventory & Dealer Data Science team focused on developing, deploying, and optimizing machine learning models that power CarGurus’ products and business insights. Sitting within the broader Data Science organization, this team is responsible for modeling automotive marketplace dynamics and dealer behavior. The team owns Machine Learning solutions end-to-end across the ML lifecycle, from R&D to production. This leader is accountable for the team’s strategic direction and impactful delivery, while building a culture of innovation, technical excellence, and collaboration. The Manager partners with Product, Engineering, Analytics, and cross-functional stakeholders to ensure team efforts advance CarGurus’ goals in intelligence-powered products.

What you'll do

• Oversee the development, training, and evaluation of machine learning models covering Inventory and Dealer intelligence. Leverage modeling techniques including recommendations, demand forecasting, churn risk prediction, and valuation algorithms.
• Facilitate experimentation, A/B testing, and measurement of production models, ensuring robust evaluation of business impact
• Build and develop a high-performing team - providing regular coaching, feedback, and performance assessments; ensure equitable access to growth opportunities.
• Drive innovation, continuous improvement, and adoption of best-in-class ML and AI practices. Champion best practices across the organization.
• Participate in roadmap planning, aligning the team’s work with broader business priorities and long-term strategy. Flag resource constraints and escalate tradeoff decisions when needed.
• Act as a visible, accessible leader - communicating objectives, updates, risks, and wins to all stakeholders.
• Partner with peers in the Machine Learning Platform, Product and Data teams, and other stakeholders to facilitate effective cross-team working relationships and represent data science in cross-functional forums.

What you'll bring

• 5+ years of experience in a data science or machine learning engineering role
• 2+ years of people leadership experience
• Deep expertise in machine learning models and their application to classification, regression, ranking, and recommendation problems. Familiarity with standard evaluation metrics and statistical best practices.
• Experience deploying and owning models in production and a working understanding of MLOps practices.
• Strong working knowledge of Python development best practices and the Python ML ecosystem (e.g., scikit-learn, PyTorch, XGBoost, numpy, pandas).
• Experience with cloud platforms, particularly AWS, including deploying, monitoring, and troubleshooting ML models.
• Effective communication and facilitation abilities with both technical and non-technical stakeholders (including data scientists, product management, senior leadership, and engineers).
• Experience contributing to the technical direction for a team and a strategic mindset for prioritizing projects and aligning them with business goals.
• Knowledge of standard software development methodologies and a history of driving continuous improvement.
• Experience in a collaborative, cross-functional environment, with the ability to build effective working relationships and foster an inclusive team culture.

Position Overview:

We are seeking a Principal Quant Engineer to work in close collaboration with Research, Portfolio Management, and Data teams to design and implement scalable, production-grade platforms that support Acadian’s quantitative research and investment processes.

This role focuses on building quant tooling that enables researchers to efficiently develop, evaluate, and deploy quantitative models across investment processes including alpha, risk, transaction cost analysis, attribution, etc. The emphasis is on creating infrastructure that makes quantitative research repeatable, observable, and production-ready at scale.

Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week.

What You’ll Do:

• Design and implement quant tooling that supports signal construction and evaluation workflows, powering production portfolios across global asset universes.
• Work closely with quantitative researchers to ensure research infrastructure continues to evolve in step with their needs, enabling efficient experimentation and reliable alpha generation.
• Lead the AI centric development and evolution of core platform capabilities that support signal computation, data access, workflow orchestration, and distributed execution for both quantitative signals and machine learning-based techniques, including deep learning workflows built with PyTorch.
• Ensure platform flexibility while maintaining integration with downstream processes for portfolio construction, portfolio management, marketing, client reporting, etc
• Incorporate machine learning and AI techniques into signal construction, parameter estimation, and efficacy evaluation workflows where they improve forecast quality and research productivity, including hyper‑parameter optimization using frameworks such as Optuna or similar.
• Provide architectural leadership to improve robustness, transparency, scalability, and performance of data- and compute-intensive research and production workflows.
• Collaborate with data and platform teams to streamline access to market data, alternative datasets, and compute resources, reducing friction in the research “inner loop” of signal development, backtesting, and validation.
• Contribute to the end‑to‑end machine learning lifecycle capabilities, including experiment tracking, model management, embedding and graph visualization, and deployment, drawing on tools and concepts such as MLflow.

We’re Looking for Teammates With:

• Bachelor’s degree or higher in Computer Science, Engineering, Applied Mathematics, or a related quantitative field.
• 7+ years of experience in software engineering, with significant hands-on work supporting quantitative research, signal development, or systematic investing in production environments.
• Demonstrated experience building and operating quantitative platforms that support signal modeling, evaluation, and deployment at scale.
• Strong proficiency in Python and modern data processing and numerical computing tools (e.g., Polars).
• Familiarity with machine learning and AI-assisted development techniques, including the use of AI or agentic coding tools (e.g. Claude Code) to improve research and engineering productivity.
• Solid understanding of system design concepts.
• Demonstrated ability to deliver robust, transparent, and performant systems.
• Strong attention to detail and a commitment to producing accurate, reliable, and well-engineered solutions.
• Creativity and enthusiasm for solving complex problems.
• Strong work ethic and a roll-up-your-sleeves mindset, with the ability to operate effectively in a hands-on, collaborative environment.

The base salary range for this role is $185,000 – $225,000 per year. Actual compensation will be determined based on a candidate’s skills, qualifications, and relevant experience. In addition to base pay, this position may be eligible for discretionary incentive compensation and includes participation in Acadian’s comprehensive benefits program, which includes health, retirement, and wellness offerings.

Staff Data Scientist, Graph ML

About Us Valo Health is a technology company applying human and machine intelligence to accelerate the creation of life-changing medical treatments. At the core of this vision is Valo’s computational platform: an end-to-end, integrated drug discovery and development engine built from the ground up. Valo hires the best and gives them first-class training and support. We approach our work fearlessly, learn quickly, improve constantly, and celebrate our wins. A centerpiece of our culture is our commitment to inclusion across race, gender, age, religion, identity, and experience. Diversity fuels the Valo experience and drives us every day. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.

As Staff Data Scientist, Graph ML, you will develop and deploy graph ML solutions to synthesize and extract novel insights from Valo data in the context of a vast corpus of knowledge in medicine, molecular biology, human genetics, and drugs, to drive compute-enabled biological hypothesis generation. Our innovative platform leverages advanced computational techniques, starting with patient data, to bring better medicines to patients, faster. You will be responsible for developing and delivering graph ML and network biology-based analyses that generate and support drug target hypotheses on the path to discovery of new medicines. You will collaborate closely with a diverse group of data scientists and biologists to enable the contextualization of predicted drug targets within relevant patient subpopulations. You will also be accountable for communicating methodological approaches and key results to internal and external cross-functional stakeholders. Additionally, you will work with other data scientists, software engineers, and data engineers to continue building and improving Valo’s integrated graph platform to accelerate insights across multiple projects and applications.

A successful candidate brings deep technical expertise in graph machine learning, network analytics, and modern data science best practices, along with experience in biology research in the context of drug discovery, and curiosity and excitement to learn.

What You’ll Do…

•  Lead the design, implementation and application of graph ML and network biology approaches to target discovery from RWD and multi-omic datasets.
• Prototype new approaches aimed at enhancing and improving Valo’s graph platform, seeking out new scientific opportunities to increase the team’s impact.
• Work with world-class engineers to ensure that graph methodologies, graph construction, and underlying data are robustly integrated, to develop generalizable solutions to core scientific problems.
• Apply your technical knowledge and intuition to break down large problems into solvable pieces. Time is limited; you’ll need to prioritize which problems are critical-path today from those that can wait.
•  Be an agile and pro-active Data Science team member, providing regular updates on your work, and input into the work of your colleagues; championing data science best practices; participating in code, design, and analysis review.

What You Bring…

•  MS or PhD in a quantitative field with extensive experience at the intersection of machine learning and graph analytics
•  Experience in healthcare, medicine, molecular biology, computational biology, or life sciences.
•  Advanced knowledge of and experience with graph ML techniques such as Graph Neural Network (GNN) models applied to link prediction, node classification, and other biomedical-relevant computational tasks, as well as related explainability methods
• Experience or general knowledge of knowledge-graph building and graph databases
•  Familiarity with general graph algorithms and relevant Python libraries
• Strong experience in Python and machine learning and/or deep-learning frameworks (e.g., pytorch)
• Experience with data science best practices (data provenance, code versioning, reproducibility, git, etc), large-scale data analytics engines (e.g., Spark or Dask), and working in cloud environments (e.g., AWS)
• Strong data visualization, analytical, problem-solving, and communication skills, with demonstrated ability to condense, summarize, and synthesize results into informative and actionable presentations to experts from different fields.

Nice to have…

•  Experience with traditional drug discovery and development processes and approaches
• Domain expertise in neuroscience, immunology, and/or cardio-metabolic biology
•  Knowledge of related data science domains, including statistical genetics, multi-omics & real-world evidence 

ROLE SUMMARY 

As an Engineer on the Generative & Agentic AI team, you will play a hands-on role in building, deploying, and operating AI capabilities that power Babel Street’s intelligence applications. You will work closely with senior AI leaders, product teams, and engineers to implement generative and agentic AI solutions that support investigative, analytical, and operational workflows across the platform. 

This is an execution-focused role for an engineer with strong foundations in machine learning and generative AI who is excited to work on real-world, mission-driven applications. You will contribute directly to LLM and SLM pipelines, retrieval and grounding systems, agent workflows, and AI-enabled features—while learning how to deliver AI that is safe, reliable, cost-efficient, and production-ready. 

This is a hybrid role to be based out of either our Reston, VA/Washington DC office or our Somerville MA office.

ROLE FOCUS 

This role spans three practical execution areas: 

Generative AI & Model Development 

You will help implement and operate LLM- and SLM-based systems, contributing to prompt development, fine-tuning, evaluation, and inference optimization. You will support retrieval-augmented generation (RAG) pipelines, embeddings, and grounding techniques to ensure AI outputs are accurate, explainable, and aligned with intelligence use cases. 

Agentic AI & Workflow Automation 

You will assist in building and integrating agent-based workflows that automate analytical tasks, connect platform services, and support intelligence applications. This includes implementing agent logic, tool-use patterns, and basic orchestration under the guidance of senior engineers. 

AI Engineering & Production Delivery 

You will help productionize AI capabilities using modern AI SDLC tools and practices, contributing to evaluation, testing, telemetry, and guardrails that reduce hallucinations and ensure safe behavior. You will work within established governance frameworks to ensure AI features are measurable, reliable, and cost-aware. 

KEY RESPONSIBILITIES 

• Implement and maintain LLM and SLM pipelines, including prompt engineering, inference, and evaluation. 

• Support RAG pipelines, embeddings, and retrieval systems used in intelligence applications. 

• Assist in building agent workflows that automate analytical or operational tasks. 

• Write clean, maintainable code (Python or others) to support AI services and integrations. 

• Contribute to AI evaluation, testing, and hallucination-mitigation techniques. 

• Use AI-assisted development tools (e.g., Copilot, Cursor) to improve development velocity and quality. 

• Collaborate with Product and Engineering teams to integrate AI capabilities into user-facing workflows. 

• Follow established AI governance, safety, and cost-optimization practices. 

QUALIFICATIONS 

Required 

• 5+ years of experience in software engineering, machine learning, or applied AI roles. 

• Hands-on experience working with LLMs and/or SLMs, including prompting, inference, or fine-tuning. 

• Experience building or contributing to RAG pipelines, embeddings, or retrieval systems. 

• Familiarity with agent-based systems or workflow automation (academic, professional, or open-source). 

• Strong programming skills in Python; experience with common libraries (PyTorch, TensorFlow, etc.). 

• Solid foundation in machine learning concepts (training, evaluation, overfitting, metrics). 

• Experience using or contributing to modern AI/ML tooling and workflows (ML pipelines, evaluation scripts, model serving). 

• Ability to work in a collaborative, fast-moving, mission-driven environment. 

Preferred 

• Experience applying AI to intelligence, investigative, analytical, or risk-related applications. 

• Familiarity with vector databases, graph databases, or knowledge graphs. 

EDUCATION 

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field required. 
Advanced degree is a plus but not required. 

Benefits at Babel Street (just to name a few...)

• Health Benefits: Babel Street covers 85-100% monthly premium costs for Medical, Dental, Vision, Life & Disability insurances – for you and your family!
• Retirement Plans: Babel Street offers both a Traditional and Roth 401(K) with a very competitive match.
• Unlimited Flexible Leave: We trust our employees to manage their own time and balance their personal and work lives.
• Holidays: Babel Street provides employees with 12 paid Federal Holidays
• Tuition Reimbursement: We are committed to investing in our employees. One way we do that is with our Tuition Reimbursement Program for continuing education.                 

Babel Street is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. Further, Babel Street will not discriminate against applicants for inquiring about, discussing or disclosing their pay or, in certain circumstances, the pay of their co‐worker, Pay Transparency Nondiscrimination. In addition, Babel Street's policy is to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works. Upon request, we will provide you with more information about such accommodations.

ROLE SUMMARY:

As an Engineer on the Image & Computer Vision AI team, you will play a hands-on role in developing and deploying computer vision capabilities that support Babel Street’s intelligence applications. You will build systems that extract, analyze, and reason over visual data—enabling facial matching, object and scene understanding, geolocation and location inference from imagery, and multimodal intelligence workflows. 

This role is execution-focused and suited for engineers with strong foundations in computer vision, image processing, and machine learning who want to apply their skills to real-world, mission-driven problems. You will work closely with AI, Product, and Engineering teams to deliver reliable, scalable, and cost-efficient vision capabilities, including integration with multimodal LLM systems that allow users to search and reason over images using natural language. 

This is a hybrid role to be based out of either our Reston, VA/Washington DC office or our Somerville MA office.

ROLE FOCUS;

This role spans three practical execution areas: 

Computer Vision & Image Analytics 

You will implement and operate image analytics pipelines that support facial matching, object detection, scene understanding, and image similarity. This includes image preprocessing, feature extraction, model inference, evaluation, and performance optimization to meet mission-grade accuracy and latency requirements. 

Geospatial & Location Inference from Imagery 

You will contribute to capabilities that infer location, context, or environmental attributes from imagery—leveraging visual cues, metadata, and learned representations. This includes supporting image-based geolocation, landmark recognition, and contextual scene analysis used in intelligence workflows. 

Multi-Modal AI & Image Search 

You will support multimodal AI systems that combine vision models with LLMs, embeddings, and retrieval pipelines to enable natural-language search and reasoning over images and image collections. You will help integrate visual understanding into broader intelligence applications and workflows. 

KEY RESPONSIBILITIES:

• Build and maintain computer vision pipelines for image ingestion, preprocessing, inference, and evaluation. 

• Implement facial matching, and identity-related vision workflows in accordance with accuracy, safety, and compliance requirements. 

• Develop and support object detection, image similarity, and scene understanding models. 

• Contribute to image-based geolocation and location inference capabilities using visual features and contextual signals. 

• Support multimodal AI workflows that combine image embeddings with LLM-based search and reasoning. 

• Write clean, maintainable Python code and contribute to production services and APIs. 

• Assist with model evaluation, bias testing, and accuracy monitoring for vision systems. 

• Optimize inference pipelines for performance, scalability, and cost efficiency (GPU usage, batching, model selection). 

• Collaborate with Product and Engineering teams to integrate vision capabilities into user-facing intelligence applications. 

QUALIFICATIONS: 

Required 

• 3+ years of experience in computer vision, image processing, or applied machine learning. 

• Hands-on experience with computer vision models and techniques (e.g., CNNs, transformers for vision, feature embeddings). 

• Experience building or integrating image analytics such as facial recognition, object detection, or image similarity. 

• Strong programming skills in Python; experience with common CV/ML libraries (PyTorch, TensorFlow, OpenCV, etc.). 

• Solid understanding of machine learning fundamentals, model evaluation, and performance tradeoffs. 

• Experience working with large image datasets and production ML pipelines. 

• Ability to work collaboratively in a fast-moving, mission-driven engineering environment. 

Preferred 

• Experience with facial matching or biometric systems in regulated or high-stakes environments.
• Experience with image-based geolocation or scene/location inference.
• Familiarity with multimodal AI systems, including combining vision models with LLMs or natural-language search. 

EDUCATION:

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field required. 
Advanced degree is a plus but not required. 

Benefits at Babel Street (just to name a few...)

• Health Benefits: Babel Street covers 85-100% monthly premium costs for Medical, Dental, Vision, Life & Disability insurances – for you and your family!
• Retirement Plans: Babel Street offers both a Traditional and Roth 401(K) with a very competitive match.
• Unlimited Flexible Leave: We trust our employees to manage their own time and balance their personal and work lives.
• Holidays: Babel Street provides employees with 12 paid Federal Holidays
• Tuition Reimbursement: We are committed to investing in our employees. One way we do that is with our Tuition Reimbursement Program for continuing education.                 

Babel Street is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. Further, Babel Street will not discriminate against applicants for inquiring about, discussing or disclosing their pay or, in certain circumstances, the pay of their co‐worker, Pay Transparency Nondiscrimination. In addition, Babel Street's policy is to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works. Upon request, we will provide you with more information about such accommodations.