About the team 

Roku continues a long-term trajectory of supporting summer internship roles within the organization, to provide applicants with experience of our enthusiasm, expertise and company culture.  

The Advanced Development, AI @ Roku (Roku’s internal AI productivity suite), Content, and Edge AI team within the Engineering organization has been granted 4 summer internships in 2026. These internships are only available to postgraduate students who are currently studying for a PhD or have recently successfully completed a PhD. 

About the role 

The purpose of the internship is to involve each student in the work of the AI @ Roku, Content, and Edge AI team. Each student will be assigned a supervisor among the senior members of the team. This supervision aims to ensure that the intern finishes their contract with a clear view of what permanent employment within the AI @ Roku, Content, and Edge AI team would be like.  

All the intern projects will be clearly defined at the outset, easy to get started and challenging to complete, and relevant to the AI @ Roku, Content, and Edge AI team’s Q3 and Q4 2026 goals. 

Project outlines: 

• Multi-modal AI: Building new experiences for TVs with Vision-Language Models, focusing on multi-modal understanding and natural language interaction. 
• Developing multimedia content generation techniques 
• Content understanding and analysis for episodic and cinematic videos 
• Deploying and optimizing AI algorithms to the edge 
• Using AI to improve image and video quality 

What you'll be doing 

• Define, agree, and track the meaningful measures (KPIs) of success in the project.  
• Collaborate effectively with other members of the project team to achieve goals.  
• Contribute meaningfully to the implementation and delivery of the project.  
• Identify and communicate key learnings within and across project teams.  
• Actively partner with experts in the project field in Cambridge, and internationally.  
• Conduct feasibility studies to determine if 'new-to-the-world' ideas are viable for long-term integration into the Roku OS. 
• Present findings and prototypes to the broader engineering and product leadership to influence Roku’s long-term innovation roadmap.

We’re excited if you have 

• PhD in STEM discipline (current or recent)  
• Strong problem-solving and analytical skills 
• Excellent communication and presentation skills 
• Proficient in data visualization and interpretation 
• High-level proficiency in Python 
• Background in mathematics, statistics, and CS 
• Ability to design and implement machine learning models 
• Familiarity with machine learning frameworks (PyTorch, Tensorflow) 
• Understanding of machine learning and computer vision algorithms and techniques 
• Experience with image processing pipelines, embedding spaces or modern LLM orchestration frameworks (e.g., LangChain, LlamaIndex, LangFuse) is a plus. 

About the Team

The Advanced Development team at Roku pushes beyond today’s product lines to invent the next generation of intelligent and generative media systems.
We explore ideas that sit years ahead of production, developing foundational technologies that will redefine how content is understood, created, and personalised across millions of Roku devices.

This is a rare environment — a PhD-level, cross-disciplinary group combining machine learning research, software engineering, and DevOps.
Everyone here is an expert, but not narrowly focused. The team blends deep technical mastery with broad creative vision — people who challenge convention, embrace ambiguity, and build what’s never been built before. It’s a collaborative, low-ego, ownership-driven culture built on trust and curiosity.

We’re seeking an Applied Scientist with a strong foundation in mathematics, machine learning, and computer science, combined with experience in cloud engineering, DevOps, and computer vision — someone who thrives where research meets production.

About the Role

As a Senior Applied Machine Learning Engineer, you’ll help design, build, and deploy the systems that make media smarter.
You’ll work across the full model and software lifecycle, from prototype to production, developing scalable ML pipelines and cloud architectures that power generative AI, intelligent media understanding, content analysis, and advertising intelligence.

You’ll operate at the intersection of machine learning, infrastructure, and software engineering, taking ownership from data collection through deployment — and seeing your work directly influence how audiences experience Roku’s content and advertising ecosystem.

What You’ll Be Doing

• Deploying scalable, fault-tolerant computer vision, media understanding, and generative AI systems to production

• Overseeing the full model development cycle: ideation, prototyping, implementation, deployment, testing, and operations

• Designing uncertainty metrics and communicating results to both technical and non-technical stakeholders

• Gathering and compiling datasets, defining annotation ontologies, auditing annotation operations, and ensuring data quality

• Staying up to date with industry and academic trends in computer vision, machine learning, and generative models for media and advertising

• Working closely with product and other engineering teams to implement new content and advertising experiences through cloud services

• Integrating services from other teams around the company, while also providing reusable ML services to others

• Evaluating and providing feedback on new platform technologies provided by internal teams

• Working with QA teams to address bugs and contribute to automation and quality assurance

We’re Excited If You Have

• A Master’s degree (PhD preferred) in Computer Science, Applied Mathematics, or a related field

• Strong background developing applied machine learning systems using PyTorch or TensorFlow

• Expertise in image processing, computer vision, or natural language processing

• Experience using AWS, GCP, or Azure for storing data, training, and serving models

• Proven ability to evaluate models and communicate insights effectively

• Experience building APIs with frameworks such as GraphQL or REST

• Experience with workflow orchestration tools such as Airflow, Argo, AWS Step Functions, or Metaflow

• Hands-on experience with Docker, Kubernetes, Terraform, CloudFormation, CI/CD automation, and Python build or packaging tools

#LI-PA1

Cambridge Mobile Telematics (CMT) is the world’s largest telematics service provider. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell® Fusion, gathers sensor data from millions of IoT devices — including smartphones, proprietary Tags, connected vehicles, dashcams, and third-party devices — and fuses them with contextual data to create a unified view of vehicle and driver behavior. Auto insurers, automakers, commercial mobility companies, and the public sector use insights from CMT’s platform to power risk assessment, safety, claims, and driver improvement programs. Headquartered in Cambridge, MA, with offices in Budapest, Chennai, Seattle, Tokyo, and Zagreb, CMT measures and protects tens of millions of drivers across the world every day.

CMT is looking for a creative, collaborative, and highly motivated Machine Learning Intern to help develop applied machine learning capabilities using real-world mobility and behavioral data. This internship is ideal for someone who enjoys solving ambiguous problems, building practical ML workflows, working with messy real-world datasets, and translating technical findings into clear recommendations.

The intern will work on a 12-week applied ML project involving data exploration, labeling strategy, feature engineering, model development, evaluation, and stakeholder-facing analysis. The ideal candidate has strong Python skills, a solid foundation in machine learning, strong initiative, and the ability to make progress in open-ended problem spaces.

Responsibilities:

• Explore real-world mobility datasets and identify useful behavioral, temporal, contextual, and aggregate patterns
• Help define practical data labeling guidelines, quality criteria, and edge-case handling rules
• Build and validate datasets for supervised machine learning experiments
• Develop baseline and improved ML models using Python and standard data science workflows
• Perform feature engineering, model evaluation, error analysis, and iteration
• Assess model performance across relevant data segments and edge cases
• Summarize findings, limitations, risks, and opportunities for technical and product stakeholders
• Produce clear documentation, analysis reports, and final recommendations

Qualifications:

• Currently pursuing a Bachelor’s, Master’s, or PhD degree in Computer Science, Data Science, Machine Learning, Statistics, Engineering, Applied Math, or a related quantitative field
• Strong Python programming skills and comfort working with real-world datasets
• Solid understanding of supervised machine learning, feature engineering, classification metrics, train/test splits, and error analysis
• Experience with common Python data science libraries such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or similar tools
• Strong analytical and problem-solving skills, with a bias toward initiative and ownership
• Clear written and verbal communication skills, including the ability to explain technical findings to non-technical stakeholders

Compensation and Benefits:

• Fair and competitive hourly rate based on education level
• Flexible scheduling options depending on role and responsibilities

Additional Perks:

• Feel great working to improve road safety around the world!
• Join one of our many employee resource groups including Black, AAPI, LGBTQIA+, Women, Book Club and Health & Wellness
• CMT will do all that is possible to support our employees and create a positive and inclusive work environment for all!

Commitment to Diversity and Inclusion:

At CMT, we believe the best ideas come from a mix of backgrounds and perspectives. 

We are an equal-opportunity employer committed to creating a workplace and culture where everyone feels valued, respected, and empowered to bring their unique talents and perspectives. Diversity is essential to our success, and we actively seek candidates from all backgrounds to join our growing team. 

We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability state. CMT is headquartered in Cambridge, MA. To learn more, visit www.cmtelematics.com and follow us on X @cmtelematics.

Your Impact at LILA

As a Machine Learning Scientist focused on Scientific Reasoning, you will help pioneer the next generation of AI systems capable of reasoning like a scientist. You’ll design novel frameworks that push the boundaries of LLM-based reasoning methods — while also implementing scalable frameworks that integrate with Lila’s platforms. This role bridges deep theoretical thinking with practical ML engineering, enabling breakthroughs in how scientific hypotheses are generated, tested, deployed and optimized.

What You'll Be Building

• Design and formalize frameworks for scientific reasoning with LLMs, including structured prompting, reasoning chains, and test-time compute.
• Explore and implement methods for in-context learning, self-reflection, and adaptive reasoning in scientific discovery workflows.
• Build scalable model prototypes that can be deployed to solve frontier scientific problems.
• Collaborate with scientists and engineers to encode domain knowledge into reasoning systems that integrate symbolic and statistical approaches.

What You’ll Need to Succeed

• PhD (preferred) or equivalent research/industry experience in Computer Science, Machine Learning, AI, Engineering, Materials Science or related fields.
• Strong programming skills in Python with deep expertise in LLM frameworks (PyTorch, HuggingFace Transformers, LangChain, LlamaIndex, and related toolkits).
• Expertise in LLM reasoning methods: in-context learning, test-time compute, chain-of-thought, or tool-augmented reasoning.
• Ability to balance theoretical research with practical ML engineering to deliver scalable solutions.

Bonus Points For

• Research experience in causal reasoning, symbolic AI, or probabilistic programming.
• Contributions to open-source LLM reasoning frameworks.
• Familiarity with scientific discovery pipelines in chemistry, biology, or materials science.
• Experience with multimodal reasoning (e.g., combining text, image, and experimental data).
• Publications in top ML/AI conferences (NeurIPS, ICML, ICLR, ACL).

COMPANY DESCRIPTION

Flagship Pioneering is a bioplatform innovation company that invents and builds companies that change the world. We bring together the greatest scientific minds with entrepreneurial company builders and assemble the capital to allow them to take courageous leaps. Those big leaps in human health, sustainability and beyond exponentially accelerate scientific progress in areas ranging from disease detection and treatment and nature-positive agriculture to novel applications of AI that are driving the creation of new technologies.   

What sets Flagship apart is our ability to advance science and technology by uniting innovation, company creation, and capital investment under one roof in a way that is largely without precedent. Our scientific founders, entrepreneurial leaders, and professional capital managers are each aligned around an institutionalized process that enables us to innovate and transform for the benefit of people and planet.   

Many of the companies Flagship has founded have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  

Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. 

THE ROLE

We are seeking a Senior Machine Learning Scientist, Climate and Hydrology to lead the scientific direction and machine learning development for hydrology-aware climate modeling within our broader environmental modeling platform.

This role sits at the intersection of hydrology, weather and climate science, and large-scale machine learning. The Sr. Scientist will help shape how hydrologic process understanding, climate data, and modern ML methods are brought together in next-generation prediction systems, with emphasis on scientifically grounded model development and evaluation.

The individual in this role will work across science, ML, engineering, and data teams to define research priorities, guide model training and benchmarking, build reproducible workflows, and translate scientific insight into scalable model development. This is a cross-functional role requiring strong technical depth, structured scientific thinking, and the ability to move between foundational research and applied execution.

KEY RESPONSIBILITIES

• Lead scientific and technical efforts at the intersection of hydrology, climate science, and machine learning.
• Help define research priorities, modeling directions, and evaluation strategies for next-generation climate and environmental prediction systems.
• Contribute to the development and improvement of ML-based modeling approaches informed by physical and Earth system science.
• Work with large-scale climate, weather, hydrology, and remote sensing datasets to support model development and scientific analysis.
• Build and oversee reproducible workflows for data processing, model training, benchmarking, and validation.
• Collaborate closely with research, engineering, and data teams to translate scientific goals into scalable technical execution.
• Guide assessment of model performance, uncertainty, and scientific robustness across a range of environmental conditions and applications.
• Communicate findings through internal reviews, external collaborations, publications, and technical presentations.
• Help shape the broader scientific roadmap and contribute to team growth and cross-functional leadership.

PROFESSIONAL EXPERIENCE & QUALIFICATIONS 

• PhD in machine learning, computational science, Earth science, atmospheric science, hydrology, AI, computer science, or a related quantitative discipline.
• 5+ years of postdoctoral, industry, or applied research experience in climate ML, weather ML, hydrologic modeling, Earth system modeling, or a closely related field.
• Demonstrated experience with ML-accelerated weather, climate, or hydrology models, with a strong publication track record in the area.
• Experience working with large climate datasets, including reanalysis products, remote sensing datasets, observational datasets, and model output.
• Experience with the computational infrastructure required to manage, preprocess, and train on large-scale climate datasets, preferably in the AWS ecosystem.
• Strong programming skills in Python and experience with modern ML frameworks such as PyTorch
• Background in scientific ML, spatiotemporal modeling, data assimilation, hybrid physics-ML methods, or related approaches is strongly preferred.
• Ability to design rigorous evaluation frameworks, performance metrics, and benchmarking approaches for environmental prediction systems.
• Strong technical writing and communication skills, including reports, presentations, and peer-reviewed publications.
• Demonstrated ability to work independently in fast-paced, ambiguous environments while collaborating effectively across disciplines.
• Experience leading cross-functional scientific efforts, mentoring researchers, or helping define research roadmaps is preferred.

LOCATION: Cambridge, MA or Boulder, CO (some travel to Cambridge, MA based headquarters if working from Colorado)

ABOUT FLAGSHIP PIONEERING:

Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  

Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.

We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

#LI-TL1

The salary ranges for this role are $127,000 - $205,900 (Colorado) and $168,000 - $231,000 (Massachusetts). Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Protocos currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Protoco's good faith estimate as of the date of publication and may be modified in the future.

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a researcher at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. As such, we’re looking for candidates who are keen scientists and engineers, with the theoretical and practical skills needed for impactful AI research.

We are looking for researchers with experience of AI in low-power, edge and embodied scenarios such as robotics, autonomous driving and augmented/virtual reality. We are interested in the training and deployment of multimodal AI models in these contexts, covering areas such as world models, real-time computer vision, generating and reasoning over audio/video streams.

The Team

Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world.

We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations.

Perhaps the best way to get an idea of what we’re all about is to read one of our papers or an article on our blog. If you’re excited to work at the cutting edge of AI supported by new hardware and want to develop your skills in this area, we’d love to hear from you!

Responsibilities and Duties

• Generate AI/ML ideas, design experiments, implement them & evaluate results.
• Prepare, submit & present your work to AI conferences and workshops.
• Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware.

About you:

Essential:

• Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry, Biomedical Engineering).
• Experience in embodied AI, world models or robotics.
• Python programming in a modern deep learning framework, e.g. PyTorch or JAX.
• Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling.
• Capable of designing, executing and reporting from ML experiments.
• Mathematics skills to support the above: calculus, probability theory and linear algebra.
• Experience submitting papers to international scientific conferences or workshops.

Desirable:

• Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton.
• Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling.
• Practical familiarity with software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution.
• Conference or workshop submissions in the fields of embodied AI, world models, or robotics.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a research engineer at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. We are therefore looking for individuals who combine strong machine learning experience with practical engineering skills to deliver impactful AI research. We are seeking AI researchers with strong software engineering experience, particularly in lower-level programming and performance optimisation for hardware efficiency. Our research spans a broad range of topics, including efficient training and inference, world models, life sciences, reinforcement learning, and beyond. You will work closely with researchers to generate ideas and translate them into scalable implementations, contributing to publications and projects that help to steer the future of AI hardware.

The Team

Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world.

We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations.

Perhaps the best way to get an idea of what we’re all about is to read one of our papers or an article on our blog. If you’re excited to work at the cutting edge of AI supported by new hardware and want to develop your skills in this area, we’d love to hear from you!

Responsibilities and Duties

• Generate AI/ML ideas, design experiments, implement them & evaluate results.
• Prepare, submit & present your work to AI conferences and workshops.
• Provide technical insight to internal teams by designing experiments and delivering clear, actionable reports.
• Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware.

About you:

Essential:

•  Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry).
• Python programming in a modern deep learning framework, e.g. PyTorch or JAX.
• Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling.
• Capable of designing, executing and reporting from ML experiments.
• Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton.
• Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling.
• Practical familiarity with software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution

Desirable:

• Mathematics skills to support the above: calculus, probability theory and linear algebra.
• Experience submitting papers to international scientific conferences or workshops.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About Graphcore 

Graphcore is one of the world’s leading innovators in Artificial Intelligence compute.  

It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.  

As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.   

Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation.  

Job Summary  

Reporting to a Team Lead in the frameworks team you will play a pivotal role in designing, implementing, optimising, maintaining and supporting the software required to ensure the machine learning accelerators that Graphcore develop enjoy first-class support in state-of-the-art machine learning frameworks such as PyTorch and Triton (note: “Triton” here refers to the Triton language/compiler and not the inference framework).  

This role sees you joining our Triton team, where you will be part of a SCRUM team working on delivering new features, optimise performance, reviewing code changes, writing technical documentation, working with upstream communities, maintaining the code base and supporting users.  

In this role you will closely collaborate with other engineers, both within the Triton team as well as other engineering teams. You help the team coordinate and deliver complex, open-ended technical tasks. You are pro-active and an excellent communicator. You will develop deep expertise in the Triton project and will (in time) contribute to the team’s technical direction and processes. You understand the importance of managing code quality and code complexity and balancing this against the need to deliver business outcomes. 

Note that the job title and associated benefits will be tailored to the successful candidate’s level of experience at the job offer stage to one of Software Engineer, Staff Engineer, Senior Staff Engineer, Principal Software Engineer or Senior Principal Software Engineer. Note that as the machine learning software landscape is quite fast moving and as such flexibility is essential – over time you may be asked to work on teams other than Triton. 

The Team 

The Frameworks team ensures Graphcore hardware works seamlessly with the tools that ML engineers and researchers love – Pytorch, Triton, Jax and TensorFlow. We’re a talented and diverse team of engineers and we foster a culture of collaboration, openness and learning. All our software teams follow agile working practices, and we care deeply about both ease-of-use as well as performance. We work closely work with other Graphcore teams as well as leading open-source communities.  

By joining us, you’ll join our exciting journey on the cutting edge of the machine learning industry. Your contributions will make a real difference – enabling machine learning engineers and researchers to unlock the full potential of Graphcore’s hardware.  

Responsibilities and Duties 

• Designing and implementing new features & maintaining and supporting existing features.
• Developing and maintaining unit tests, component tests and integration tests.
• Optimising the software stack to make it more performant for our users.
• Managing complex technical tasks with cross-team dependencies.
• Managing code quality, code complexity and technical debt.
• Provide technical leadership, contributing the team’s technical direction and processes.
• Contributing to documentation, including user manuals and tutorials.
• Conducting code reviews.
• Resolving regressions, performance issues and software defects.
• Coaching and mentoring other team members.
• Contributing to a collaborative team culture.
• Contribute to continuous improvements to improve ways of working.
• Working with upstream open-source development teams. 

Candidate Profile 

Essential: 

• Demonstrable strong software engineering skills.
• Demonstrable experience of Python/C++ development.
• Demonstrable experience of compiler development. 

Desirable: 

• Experience with MLIR (or more generally: SSA-based IRs) is highly desirable. 
• Demonstrable knowledge of AI/ML.
• Understanding of computing architectures.
• Experience of maintaining and supporting a complex code base.
• Experience in profiling / optimising high performance code.
• Experience of writing ML kernels.
• Experience of using and/or development of ML Frameworks.
• Experience in a computationally intensive engineering field. 

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.

The Team

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!

Responsibilities and Duties

• Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
• Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
• Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
• Design and conduct experiments on novel AI methods, implement them and evaluate results.
• Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
• Engage with AI community and keep in touch with the latest developments in AI.

Candidate Profile

Essential:

• Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
• Proficiency in deep learning frameworks like PyTorch/JAX.
• Strong Python or C++ software development skills
• Expertise in deep learning from model training to optimisation and evaluation.
• Experience in distributed training or inference of ML models across 64+ accelerators.
• Capable of designing, executing and reporting from ML experiments.
• Developed deep understanding of performance bottlenecks and how to overcome them.
• Ability to move quickly in a dynamic environment
• Enjoy cross-functional work collaborating with other teams.
• Strong communicator - able to explain complex technical concepts to different audiences.

Desirable:

• Experience in one or more of:
  • MLOps for Kubernetes-based clusters
  • Building production systems with large language models
  • Efficient computing based on low-precision arithmetic.
• Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
• Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
• Have contributed to open-source projects or published research papers in relevant fields.
• Knowledge of cloud computing platforms.
• Keen to present, publish and deliver talks in the AI community.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About us 

At Graphcore, we’re building the future of AI compute.

We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.

As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a Graduate Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. This role is ideal for someone who loves working hands-on with models, has a strong foundation in ML fundamentals, and wants to push the boundaries of AI performance in real-world systems. 

The Team

The Applied AI team’s role is to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for different modalities and applications. 

Responsibilities and Duties

• Implement state-of-the-art machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators. 
• Evaluate new software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews. 
• Benchmark models and key model components to identify performance bottlenecks and improve model efficiency. 
• Design and conduct experiments on novel AI methods, analyse and report results clearly. 
• Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware. 
• Stay current with AI research and actively engage with the broader AI and open-source community. 

Candidate Profile 

Essential:

• Bachelor’s/Master's degree in Machine Learning, Computer Science, Maths, Data Science, or related field. 
• Proficiency in deep learning frameworks such as PyTorch/JAX and strong software development skills. 
• Solid understanding of deep learning fundamentals — architectures, optimisation, evaluation, and scaling. 
• Capable of designing, executing and reporting from ML experiments. 
• Comfortable working in a fast-moving, occasionally ambiguous environment. 
• Enjoy cross-functional work collaborating with other teams. 

Desirable:

Experience in one or more of: 

• Development of deep learning models including large generative models for language, vision and other modalities; 
• Low-precision/efficient compute; 
• Distributed training of large-scale ML models. 
• Experience writing high performance C++/Triton/CUDA kernels. 
• Contributions to open-source projects or published research. 
• Familiarity with cloud platforms and ML infrastructure. 
• Enthusiasm for presenting, publishing, or engaging in the AI community. 

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments. 

About us

At Graphcore, we’re building the future of AI compute.

We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.

As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.

We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a research intern at Graphcore, you will advance AI research and investigate new ideas that push the limits on important AI/ML problems. Specialised hardware has driven progress in AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to this exciting field. We’re looking for candidates who are keen scientists and engineers, with the theoretical and practical skills needed for impactful AI research.

During your internship, you’ll investigate a research question that relates to machine learning or accelerated computation. Practically, this means designing experiments, implementing models and algorithms, exploring data and presenting results, with the aim to present work publicly to the wider research community. You will be largely self-directed in your research activity, with freedom to pursue the most promising approach, and with the benefit of appropriate mentoring and guidance.

Our team is interested in numerous topics across the field, including (but not limited to) efficient training and inference, improved model capabilities in the areas of factuality and reasoning, image and video generation, world models and molecule generation for life science applications.

The Team

Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world.

Candidate profile

We’re looking for technically-minded, highly-motivated and adaptable problem-solvers who can thrive in our collaborative team.

Essential

• A PhD student working in the field of Machine Learning and Artificial Intelligence, or be pursuing a Masters degree in a relevant field with demonstrable research experience (for example, interesting ML publications);
• Able to demonstrate your coding skills (Python, C++), for example through projects or previous work experience, and have experience of algorithm implementation in machine learning frameworks such as PyTorch;
• Passionate about the field of AI, both theory and practice, with a keen interest to continue learning.

We are excited to hear about your research! Please provide a short summary of a recent project with your application (and we’d love to hear about any other relevant work).

Benefits

In addition to a competitive salary, our centrally located offices provide a well-stocked kitchen with healthy food, drinks and snacks and our very own barista (Bristol only for now, but we have great coffee everywhere). We have an active social scene too: cycling, yoga, running, board games, table tennis, football, to name a few.

We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

About Graphcore 

Graphcore is one of the world’s leading innovators in Artificial Intelligence compute.  

It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.  

As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.   

Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation.

Job Summary 

We are looking for a Principal Architect to define and drive the architectural vision of the software stack for the Graphcore ML accelerator.

In this role, you will shape the architecture of our software ecosystem and maintain a deep understanding of the product’s hardware and software components, their interfaces, and how they interact.

You are an excellent communicator, and you proactively convey the software architecture.

You bring a pragmatic, trade-off-aware approach to decision-making, fully recognising the impact of architectural choices on product direction and engineering outcomes. 

The Team 

The software architecture team is responsible for defining, maintaining and communicating the overarching architecture of our software stack, from firmware to ML frameworks.

The team works within the wider software organisation, partnering closely with engineering teams who deliver against this architectural vision. 

Responsibilities and Duties

• Define & document the software architecture of the software stack.  
• Work across different software domains to ensure a coherent end too end architecture.
• Clearly communicate the software architecture to engineering teams.
• Ensure teams understand key software architectural components, interfaces, and guiding principles. 
• Continuously maintain the software architecture as the product evolves. Communicate architectural changes proactively to all relevant stakeholders. 
• Stay informed about trends and developments in the ML/software ecosystem.
• Assess the architectural impact of new technologies.
• Be aware of the usability, performance, reliability, maintainability, testability, security and observability constraints on the software architecture. 
• Prototyping and validating architectural concepts through proof-of-concept implementations. 
• Contribute to future and/or related product definitions with a forward-looking mindset, understanding and communicating the impact of future direction on software architecture. 
• Work with the HW and Silicon architects to ensure product the needs of software stack. 
• Ensure the software architecture meets the product requirements.  
• Work closely with engineering, product, leadership, and partners to ensure alignment between business goals and architectural direction. 

Candidate Profile

Essential:

• Expertise in one or more of these technologies; Firmware, Drivers, ML frameworks (PyTorch, Triton, etc), Collectives, HPC, Tooling, C & C++, CPU architectures.
• Technical expertise in the ML accelerator software space.
• Excellent communication skill, both written and verbal. 
• Collaborative mindset and ability to influence. 
• Pragmatic approach to decision making, being very aware of trade-offs and implications. 

Desirable

• Demonstrable experience in a previous architecture role. 

Benefits

• In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

  Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.  

Job Summary

As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.

The Team

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!

Responsibilities and Duties

• Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
• Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
• Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
• Design and conduct experiments on novel AI methods, implement them and evaluate results.
• Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
• Engage with AI community and keep in touch with the latest developments in AI.

Candidate Profile

Essential:

• Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
• Proficiency in deep learning frameworks like PyTorch/JAX.
• Strong Python or C++ software development skills
• Expertise in deep learning from model training to optimisation and evaluation.
• Experience in distributed training or inference of ML models across 64+ accelerators.
• Capable of designing, executing and reporting from ML experiments.
• Developed deep understanding of performance bottlenecks and how to overcome them.
• Ability to move quickly in a dynamic environment
• Enjoy cross-functional work collaborating with other teams.
• Strong communicator - able to explain complex technical concepts to different audiences.

Desirable:

• Experience in one or more of:
  • MLOps for Kubernetes-based clusters
  • Building production systems with large language models
  • Efficient computing based on low-precision arithmetic.
• Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
• Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
• Have contributed to open-source projects or published research papers in relevant fields.
• Knowledge of cloud computing platforms.
• Keen to present, publish and deliver talks in the AI community.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.

The Team

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!

Responsibilities and Duties

• Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
• Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
• Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
• Design and conduct experiments on novel AI methods, implement them and evaluate results.
• Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
• Engage with AI community and keep in touch with the latest developments in AI.

Candidate Profile

Essential:

• Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
• Proficiency in deep learning frameworks like PyTorch/JAX.
• Strong Python or C++ software development skills
• Expertise in deep learning from model training to optimisation and evaluation.
• Experience in distributed training or inference of ML models across 64+ accelerators.
• Capable of designing, executing and reporting from ML experiments.
• Developed deep understanding of performance bottlenecks and how to overcome them.
• Ability to move quickly in a dynamic environment
• Enjoy cross-functional work collaborating with other teams.
• Strong communicator - able to explain complex technical concepts to different audiences.

Desirable:

• Experience in one or more of:
  • MLOps for Kubernetes-based clusters
  • Building production systems with large language models
  • Efficient computing based on low-precision arithmetic.
• Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
• Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
• Have contributed to open-source projects or published research papers in relevant fields.
• Knowledge of cloud computing platforms.
• Keen to present, publish and deliver talks in the AI community.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

Senior: PLN 260,400 - 352,200
Staff: PLN 350,700 - 474,400
Subject to alignment to the responsibilities and duties of the role - we currently have multiple positions available at both Senior and Staff level

About Graphcore 

At Graphcore, we’re building the future of AI compute.

We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.

As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.

To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.

We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence

Job Summary

As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.

The Team

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!

Responsibilities and Duties

• Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
• Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
• Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
• Design and conduct experiments on novel AI methods, implement them and evaluate results.
• Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
• Engage with AI community and keep in touch with the latest developments in AI.

Candidate Profile

Essential:

• Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
• Proficiency in deep learning frameworks like PyTorch/JAX.
• Strong Python or C++ software development skills
• Expertise in deep learning from model training to optimisation and evaluation.
• Experience in distributed training or inference of ML models across 64+ accelerators.
• Capable of designing, executing and reporting from ML experiments.
• Developed deep understanding of performance bottlenecks and how to overcome them.
• Ability to move quickly in a dynamic environment
• Enjoy cross-functional work collaborating with other teams.
• Strong communicator - able to explain complex technical concepts to different audiences.

Desirable:

• Experience in one or more of:
  • MLOps for Kubernetes-based clusters
  • Building production systems with large language models
  • Efficient computing based on low-precision arithmetic.
• Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
• Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
• Have contributed to open-source projects or published research papers in relevant fields.
• Knowledge of cloud computing platforms.
• Keen to present, publish and deliver talks in the AI community.

Benefits

In addition to a competitive salary, Graphcore offers annual leave policy, medical and dental health plans, a gym card, and employee pension (matched up to 4%). We review our benefits on a yearly basis to ensure we offer a valuable and rewarding benefits programme to our employees. We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

About Graphcore 

At Graphcore, we’re building the future of AI compute.

We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.

As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.

To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.

We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence

Job Summary

Applicants for this role should have strong experience working with machine learning systems and frameworks, along with a solid understanding of core AI concepts and model behaviour. The role centres on testing, validating, and benchmarking a complex ML software stack, with a particular focus on performance, reliability, and correctness across modern AI workloads.

The ideal candidate is an experienced ML engineer who understands how contemporary models are trained and executed, and who has hands-on experience debugging functional and performance issues in ML systems. This person will be comfortable working with industry-standard frameworks and state-of-the-art models, bringing them up on internal infrastructure, and collaborating closely with software and hardware teams in a technically demanding environment spanning ML frameworks, infrastructure, and AI accelerator hardware.

The Team

The ML QA team is composed of highly skilled software engineers with a strong focus on automation, software quality, and data-driven validation. The team works closely with industry-standard machine learning frameworks and models, contributing to upstream open-source projects and collaborating across the wider software organization. 

Operating in a fast-paced environment, the team plays a critical role in ensuring reliability, performance, and maintainability across the ML software stack, helping to deliver robust and high-quality products to customers. 

Responsibilities and Duties

• Benchmark ML models and frameworks, analysing results to identify regressions, performance bottlenecks, and correctness issues. 
• Work hands-on with industry-standard ML frameworks to validate functionality and performance across different execution environments. 
• Build and maintain automated testing and benchmarking pipelines targeting simulators, emulators, and physical hardware. 
• Collaborate closely with software teams to ensure adequate test coverage for new and existing features. 
• Develop tooling and scripts (primarily in Python) to support testing, benchmarking, and functional reporting. 
• Take ownership over aspects of our testing and infrastructure, owning the roadmap and driving innovation independently.

Candidate Profile

Essential:

• Experience working in Machine Learning or ML-adjacent engineering roles. 
• Strong foundation in core AI and ML concepts (e.g. neural networks, training vs inference, numerical precision, performance trade-offs). 
• Hands-on experience with one or more major ML frameworks such as PyTorch, TensorFlow, JAX, or similar. 
• Strong proficiency in Python for ML workflows, experimentation, and automation. 
• Experience designing, running, and analysing ML benchmarks or experiments. 
• Experience working in Linux environments. 
• Strong analytical and debugging skills, with the ability to reason about model behaviour and system performance. 
• Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field. 

Desirable

• Experience with MLOps pipelines, model deployment, or production ML systems. 
• Familiarity with performance analysis, profiling tools, or numerical accuracy validation. 
• Exposure to distributed training or inference systems. 
• Experience with hardware-accelerated ML, compilers, or system-level performance considerations. 
• Familiarity with CI/CD systems used for ML workflows. 
• Experience contributing to open-source ML frameworks or tooling. 

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About Graphcore 

At Graphcore, we’re building the future of AI compute.

We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.

As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.

To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.

We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence

Job Summary

Applicants for this role should have strong experience designing, developing, and maintaining high-quality software systems. The role focuses on testing and validating a complex machine learning software stack, with particular emphasis on software architecture, automation, and engineering best practices.

The ideal candidate is an experienced software engineer who values code quality, testability, and long-term maintainability, and enjoys building systems that other engineers rely on. This person will be comfortable working across large codebases, contributing to CI/CD infrastructure, and shaping technical direction through thoughtful design and mentoring in a technically demanding environment spanning ML frameworks, infrastructure, and AI accelerator hardware

The Team

The ML QA team is composed of highly skilled software engineers with a strong focus on automation, software quality, and data-driven validation. The team works closely with industry-standard machine learning frameworks and models, contributing to upstream open-source projects and collaborating across the wider software organization. 

Operating in a fast-paced environment, the team plays a critical role in ensuring reliability, performance, and maintainability across the ML software stack, helping to deliver robust and high-quality products to customers. 

Responsibilities and Duties

• Design, implement, and maintain robust test infrastructure and automation for a complex ML software stack.
• Architect and evolve test frameworks and tooling with a focus on scalability, maintainability, and developer experience.
• Build and maintain CI/CD pipelines targeting simulators, emulators (e.g. QEMU), and physical hardware.
• Create representative ML workloads and gain insights from their execution. (Numerical accuracy, performance analysis and benchmarking).
• Work closely with all Software development teams, supporting a culture of quality, security and maintainability.
• Review code and designs, setting a high bar for software engineering best practices.
• Mentor and support junior engineers, helping raise the overall technical capability of the team.
• Evaluate existing test strategies and infrastructure, identifying gaps and driving improvements aligned with team and organizational goals. 

Candidate Profile

Essential:

• Experience in production-quality software engineering roles.
• Strong software design and architecture skills, with experience working on large or complex systems.
• Strong proficiency in Python, including experience building and maintaining production codebases.
• Solid experience with CI/CD systems and automated testing (preferably GitHub-based workflows).
• Experience working in Linux environments.
• Familiarity with C or C++, with the ability to read, debug, and reason about low-level code when needed.
• Proven ability to mentor junior engineers and influence engineering practices within a team.
• Strong problem-solving skills and a proactive, self-directed approach to work.
• Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field. 

Desirable

• Exposure to machine learning frameworks such as PyTorch, JAX, Triton, TensorFlow
• Experience with distributed workload management systems such as Kubernetes, VLLM, Keras or MLOps pipelines
• Experience working with hardware simulators or emulators (e.g. QEMU).
• Experience developing for or working with FPGA-based systems.
• Experience with people management or mentoring 

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

MORSE is looking for an Autonomy Software Engineer to research, develop and prototype novel autonomy software to enable pre-mission planning and operation of novel aircraft and other autonomous systems. MORSE is seeking an aeronautical, aerospace, mechanical, or electrical engineer in optimization, estimation theory, and/or guidance, navigation and control (GNC) algorithms. Responsibilities will include autonomy development from conceptualization, including the selection and evaluation of sensors, implementation, including software development, and evaluation, including data analysis. The role will involve developing and testing code in a simulation and HWIL environment as well as field tests of the implemented autonomy software. The ideal candidate will have a strong foundation in physics, math, statistics, and computer science, as well as experience handling electronic hardware and machine learning. The candidate will work with a multidisciplinary team in an agile environment to rapidly design, prototype and test autonomous systems.

Responsibilities:

• Research, develop, and prototype novel autonomy software and algorithms for autonomous systems

• Develop and evaluate autonomy software from conceptualization to implementation, including selection and evaluation of sensors, software development, and data analysis

• Design, develop, and test algorithms for autonomous systems, including sensor fusion, guidance, navigation, and control

• Develop and test code in simulation and Hardware-In-the-Loop (HWIL) environments, as well as participate in field tests of implemented autonomy software on vehicles and systems

• Collaborate with a multidisciplinary team in an agile environment to rapidly design, prototype, and test autonomous systems

Requirements:

• US CITIZENSHIP REQUIRED or the ability to obtain a U.S. Security Clearance.

• The candidate must have experience developing real-world solutions with computational constraints, measurement errors, and modeling uncertainty

• 5+ years of Autonomy, Guidance, Navigation and or Controls experience 

• Experience with developing both algorithms and the simulations for test and validation

• Expertise in positioning, navigation, and timing, particularly for aircraft

• The ideal candidate will have experience working on complex, multi-disciplinary R&D programs

• BS. or M.S. (preferred) in Aeronautical/Aerospace, or Electrical Engineering (Control or Signal Processing focus)  or equivalent degree/major

Desired Qualifications:

• Expertise in sensor fusion such as Kalman filtering and experience with multiple sensing modalities

• Experience with designing and integration of autonomy hardware (sensors, flight controllers)

• Experience with C++ and Python software languages and software design best practices.

• Experience with sensor modeling, simulation, and performance analysis

• Experience in ML model development and deployment using open-source tooling such as PyTorch and ONNX

• Ability to lead a small multi-disciplinary team is a plus

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

Your Impact at LILA

As a Robotics Scientist at Lila, you will lead the research and development of autonomous robotic systems that serve as the intelligent physical infrastructure of our scientific superintelligence platform. You’ll develop novel algorithms and deploy intelligent robotic solutions that interact seamlessly with human scientists and complex lab environments. Your work will accelerate our mission by enabling fully autonomous workflows for scientific discovery, combining cutting-edge robotics, machine learning, and systems engineering.

What You'll Be Building

• Pioneering approaches for precise and dexterous robotic manipulation that leverage foundation models, reinforcement learning, diffusion-based methods, and human guidance to enable adaptive and intelligent robotic systems capable of complex tasks across diverse scientific environments
• Developing novel human-robot interaction frameworks that incorporate imitation learning, and learning from human guidance, feedback, demonstrations and corrections, creating intelligent robotic agents that can seamlessly integrate with human scientific workflows and rapidly adapt to new experimental contexts
• Advancing dexterous manipulation research through cutting-edge machine learning approaches, including diffusion models and adaptive learning algorithms, that synthesize multi-modal sensing (tactile, visual, and language) to develop generative skill representation sand sophisticated motor learning policies for intelligent robotic systems
• Designing autonomous robotic systems with trust calibration mechanisms, enabling intelligent agents that can dynamically adjust their behaviors based on contextual information in complex scientific tasks

What You’ll Need to Succeed

• Ph.D. in Robotics, Machine Learning, Computer Science, or a related field with demonstrated expertise in foundation models for robotic learning
• Advanced proficiency in reinforcement learning, diffusion-based methods, imitation learning, and adaptive learning algorithms for robotic manipulation
• Expert-level experience with machine learning frameworks (PyTorch, TensorFlow) and deep learning architectures for developing foundation models, with specific expertise in diffusion-based generative models for robotics
• Proven track record of developing multi-modal perception systems integrating tactile, visual, language and other contextual sensing for intelligent robotic agents
• Strong publication record in robot learning, demonstrating innovative approaches to trust calibration, contextual learning, and generative robotic skill learning

Bonus Points For

• Research contributions to foundation models and diffusion methods in robotics
• Experience with large-scale machine learning model development, particularly generative and diffusion-based approaches
• Expertise in human-in-the-loop learning, correction-based training paradigms, and diffusion-guided skill transfer
• Demonstrated ability to translate theoretical machine learning research, especially diffusion and generative models, into practical robotic implementations

Your Impact at LILA

Join us in shaping the future of science! We are seeking Sr Principal, and Principal Software Engineers with full stack experience to join our software org, where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. Our teams span across App, Data, SSR, and LaS with a strong emphasis on Python development for scientific applications. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you!

Our Software Teams

The Application Team (App)- They build the operating system for AI-driven science. Their platform unifies Machine Learning, Life Sciences, Physical Sciences, and Software into one AI-native experience — where a scientist can go from hypothesis to experiment to result in a single conversation. They ship the agents, the integrations, and the interfaces that make that possible.

The Scientific System of Record Team (SSR) - They build the memory layer for Lila's operations. It answers two questions:what did we plan to build? and what actually happened? These systems connect scientific intent to physical reality. Together with the data and automation teams, their systems ensure reproducibility and close the Design-Build-Test-Learn (DBTL) loop.

The Lab as Software Team (LaS)- They act as the physical and virtual execution layer for Lila's AI-driven science. This connects the Lila App to AI Science Factories (AISFs), the mechanism through which experiments are dispatched to real instruments. This team owns the full stack of lab integration: orchestration of labflows, instrument integrations, bi-directional data transfer, and the UI/UX that AI scientists and operators use to interact with the lab.

The Data Platform Team (Data)- They build and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.

What You'll Be Building

• Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.
• Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
• Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.
• Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
• Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.
• Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.

What You'll Need To Succeed  

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 8-15 years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend.
• Full Stack Development: Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)
• Hands on experience using AI coding assistants to drive productivity is required.
• Communication & Collaboration: Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
• Problem Solving: Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability.

Bonus Points For

• Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
• Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django).
• Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).
• Familiarity with Python for Science: Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).
• Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
• Experience with laboratory devices, robotics, or hardware drivers.

Your Impact at LILA

Join us in shaping the future of science! We are seeking Staff, Senior II, and Senior I Software Engineers with either frontend or backend experience to join our software org, where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. Our teams span across App, Data, SSR, and LaS with a strong emphasis on Python development for scientific applications. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you!

Our Software Teams

The Application Team (App)- They build the operating system for AI-driven science. Their platform unifies Machine Learning, Life Sciences, Physical Sciences, and Software into one AI-native experience — where a scientist can go from hypothesis to experiment to result in a single conversation. They ship the agents, the integrations, and the interfaces that make that possible.

The Scientific System of Record Team (SSR) - They build the memory layer for Lila's operations. It answers two questions:what did we plan to build? and what actually happened? These systems connect scientific intent to physical reality. Together with the data and automation teams, their systems ensure reproducibility and close the Design-Build-Test-Learn (DBTL) loop.

The Lab as Software Team (LaS)- They act as the physical and virtual execution layer for Lila's AI-driven science. This connects the Lila App to AI Science Factories (AISFs), the mechanism through which experiments are dispatched to real instruments. This team owns the full stack of lab integration: orchestration of labflows, instrument integrations, bi-directional data transfer, and the UI/UX that AI scientists and operators use to interact with the lab.

The Data Platform Team (Data)- They build and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.

What You'll Be Building

• Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.
• Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
• Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.
• Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
• Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.
• Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.

What You’ll Need to Succeed

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 5-10 years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend.
• Full Stack Development: Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)
• Hands on experience using AI coding assistants to drive productivity is required.
• Communication & Collaboration: Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
• Problem Solving: Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability.

Bonus Points For

• Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
• Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django).
• Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).
• Familiarity with Python for Science: Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).
• Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
• Experience with laboratory devices, robotics, or hardware drivers.

AI Resident – 2026 Cohort

The AI Residency Program is a full-time research opportunity designed to bridge the gap between academic research and industry applications in AI for materials science. Residents will work closely with Lila scientists and engineers on high-impact, open-science projects, with the option to focus on either fundamental or applied research.

• Duration: 6–12 months (extension possible)
• Start Dates: First hires beginning January 2026, with rolling applications and additional intakes in Summer and Fall 2026
• Cohort Size: Small group of selected residents
• Mentorship: Pairing with technical mentors, feedback from cross-functional teams
• Resources: Access to proprietary datasets, high-performance compute, and Lila’s research infrastructure

Research areas include ML-accelerated simulations, Bayesian methods, representation learning, generative models, agentic science, and ML-driven automation.

Your Impact at Lila

The Lila Sciences AI Residency is a full-time research program at the intersection of artificial intelligence and materials science. As a resident, you'll join a cohort of researchers tackling open-ended scientific challenges alongside Lila’s world-class team of scientists and engineers. With access to proprietary datasets, high-performance compute infrastructure, and experienced mentors, you'll pursue ambitious research projects with both academic and real-world impact. Publishing is encouraged but not required — what matters most is pushing the frontier of scientific discovery.

What You'll Be Building

• Design and execute independent research projects in AI for materials science
• Collaborate with Lila scientists and engineers on cutting-edge, open-science initiatives
• Explore domains such as ML-accelerated simulations, Bayesian methods, representation learning, generative AI, agentic science, and ML-driven automation
• Contribute to collaborative team research and co-develop novel approaches to scientific discovery
• Share findings internally and externally; publications are welcome but not mandatory

What You’ll Need to Succeed

• Degree in Materials Science, Chemistry, Computer Science, AI/ML, Physics, Mathematics, or related field (Bachelor’s, Master’s, or PhD)
• Proficiency in Python and deep learning frameworks (e.g., PyTorch)
• Experience working with large-scale datasets or simulations
• Familiarity with modern AI/ML architectures and training techniques
• Strong research background, demonstrated through publications, thesis work, or open-source projects

Bonus Points For

• Prior work on ML applications in scientific domains (e.g., materials discovery, chemistry, simulations)
• Familiarity with Bayesian optimization, active learning, or generative models
• Experience in reinforcement learning or agent-based approaches to scientific reasoning
• Open-source contributions or collaborative research experience
• Strong communication and writing skills, especially for conveying complex scientific ideas

Your Impact at LILA

As a ML Research Scientist - Multimodal Data Extraction, you will advance Lila’s vision of scientific superintelligence by developing foundation models that autonomously read, interpret, and structure scientific knowledge across text, images, and experimental data in the physical sciences. Your research will help unify the world’s scientific information into machine-understandable form, powering reasoning, prediction, and autonomous discovery across materials science and chemistry.

What You'll Be Building

• Research and develop AI systems that extract and structure knowledge from diverse scientific sources.
• Design and fine-tune large language, multi-modal and specialized models for factual, interpretable data extraction.
• Build scalable pipelines for unstructured and heterogeneous scientific data, integrating text, tables, and visuals.
• Collaborate with domain experts to align extracted data with real-world discovery workflows.
• Publish research that advances the state of the art in multimodal understanding and AI-driven knowledge extraction.

What You’ll Need to Succeed

• PhD (or equivalent research experience) in Computer Science, Chemistry, Materials Science, or related field.
• Expertise in machine learning, NLP, and vision–language modeling using PyTorch and Hugging Face Transformers.
• Proven ability to train, fine-tune, and evaluate LLMs and multimodal models for scientific data extraction.
• Strong understanding of data structures and representations used in the physical sciences.
• Demonstrated research impact through publications, preprints, or open-source work (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP, Scientific Journals).

Bonus Points For

• Experience with multimodal fusion architectures and document-level understanding.
• Knowledge of scientific document parsing (OCR, table extraction, figure-caption linking).
• Familiarity with knowledge graph construction or reasoning systems for science.
• Experience with noisy or heterogeneous real-world scientific data.
• Collaborative mindset and passion for advancing AI in the physical sciences.

Your Impact at LILA

As a Sr./Principal Research Engineer focused on Synthetic Data, you’ll contribute to the vision, roadmap, and delivery of our synthetic data program—from asset generation and simulation to ML training integration and measurable model gains. You will be working with the Research Engineering team in designing, generating, and implementing artificial datasets to train, test, and improve Lila’s platform and help us reach our goals.

What You'll Be Building

• Help to define the synthetic data strategy and multi-quarter roadmap
• Develop evaluation frameworks that tie synthetic interventions to real model performance
• Establish standards for asset quality, diversity, documentation, and reproducibility; contribute to a healthy review culture.

What You’ll Need to Succeed

• 6+ years in applied ML/ML systems with 3+ years leading industry initiatives; track record of advanced algorithms and frameworks designed specifically for generating large-scale synthetic data.
• 8+ years working with modern ML workflows: Python, PyTorch, dataset tooling, training loops, and evaluation frameworks; comfortable profiling and optimizing GPU-heavy pipelines.

Bonus Points For

• Track record of building synthetic data sets from source data to measurably augment model performance in targeted domains.
• Experience instruction fine tuning and hillclimbing therein.
• Building product requirements and feedback into sythentic data generation pipeline at scale
• Knowledge of quantization/distillation, routing/mixture-of-experts, and cost optimization at scale
• Experience working in compliance-heavy environments (HIPAA, PCI, FedRAMP) and on-prem/VPC deployments

Your Impact at LILA

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai

At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, please apply.

What You'll Be Building

• Agentic AI for science: Systems that perform sequential decision‑making and multi‑step reasoning to solve domain‑specific problems.
• Workflow/code generation: From natural language intent to typed, executable steps for lab instruments.
• Evaluation & reliability: Benchmarks, test suites, and telemetry to measure capability and quantify progress toward scientific goals.

What You’ll Need to Succeed

• PhD or Masters in a quantitative discipline (e.g., Computer Science, Physics, Mathematics, Engineering) with a strong background in machine learning and one domain of science (e.g. biology or materials science).
• Strong grasp of LLMs and agent architectures (planning, tool use, structured function calling, code generation) and how to adapt them to domains.
• Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow, JAX) and experience implementing scalable solutions for complex tasks.
• Comfort collaborating across disciplines and interfacing with simulations and real lab systems.

Bonus Points For 

• Building long‑horizon agents or RL for control/decision‑making; experience with model‑based or offline RL.
• Designing domain‑specific benchmarks and evaluation harnesses for complex scientific tasks.
• Digital‑twin development, calibration, and sim‑to‑real transfer.
• Publications or open‑source contributions in AI for science (especially publications in top-tier conferences like NeurIPS, ICML, AAAI, ICLR).

Location

San Francisco, CA or Cambridge, MA (Hybrid and On-Site available depending on team needs).

Your Impact at LILA

Your role in our Physical Sciences division will focus on designing and implementing state-of-the-art simulation approaches to model transport, kinetics, rare events, and reaction networks, and integrating them with AI-driven platforms for materials discovery. Your work will be integral to our efforts in predicting, designing, and controlling the behavior of complex materials and molecular systems, and their acceleration via agentic AI. You will partner with diverse teams at Lila, including machine learning experts working on scientific superintelligence and materials science experts performing real-world experiments.

What You'll Be Building

• Develop and extend molecular dynamics and Monte Carlo algorithms to capture rare events, non-equilibrium processes, transport phenomena, and mapping complex reaction networks.
• Build scalable simulation workflows that integrate statistical mechanics methods with machine learned interatomic potentials and agentic AI frameworks.
• Design methods for coupling dynamics simulations with experimental observables to enable closed-loop verification and discovery with automated labs.
• Collaborate with computational scientists, machine learning experts, and platform engineers to advance the fidelity and scalability of simulation-driven materials discovery.
• Establish reproducible, modular software pipelines for statistical mechanics and dynamics simulations that can be deployed on HPC and cloud-based infrastructure.

What You’ll Need to Succeed

• PhD or equivalent research/industry experience in Physics, Chemistry, Chemical Engineering, Mechanical Engineering, Applied Mathematics, or related fields.
• Strong background in statistical mechanics, free energy calculations, reaction mapping, non-equilibrium dynamics, and rare-event sampling.
• Demonstrated expertise with molecular dynamics, Monte Carlo, and/or kinetic simulation software and frameworks (LAMMPS, GROMACS, OpenMM, HOOMD, etc.).
• Solid programming skills and experience with scientific computing (Python, C/C++, MPI, CUDA, etc.).
• Experience running and automating simulations on HPC and/or cloud environments at scale.

Bonus Points For

• Strong publication record applying advanced statistical mechanics or dynamics simulations to molecular and materials systems, including but not limited to molecular/biomolecular systems and solid-state materials and interfaces.
• Prior work in coupling dynamics simulations with data-driven, AI-based, and/or agentic frameworks.
• Good familiarity with machine learning frameworks (PyTorch, JAX, TensorFlow, etc.)
• Prior experience working with machine learned interatomic potentials, including model training, fine-tuning, and data generation
• Worked closely with experimental teams to extract and corroborate experimental observables from dynamics simulations

We are embarking on a transformative journey with DriveWell Atlas, a groundbreaking initiative to build a family of novel AIs on telematics data. As a Principal Machine Learning Engineer on the DriveWell Atlas team, you will be at the forefront of developing these next-generation AIs. You will lead innovative projects focused on designing, pre-training, fine-tuning, and deploying these AIs. Your work will directly contribute to enhancing our capabilities in risk assessment, driver engagement, and crash and claims processing. This role requires a deep understanding of modern AI methods, machine learning, physics-informed modeling, and experience with sensor data. We are not tweaking existing models for marginal gain. You will have an opportunity to build a first-of-its-kind LLM from petabyte-scale data. 

CMT is looking for a Principal Machine Learning Engineer, Foundation Models to help us change the world. CMT has helped protect over 65 million drivers and prevent over 126,000 crashes worldwide. We build AI to solve some of the most difficult challenges in mobility — understanding and reducing risk, detecting crashes, and getting people life-saving help. The problems are hard. The impact is real. No matter your role, your work will matter at CMT.

Responsibilities: 

• Use independent judgment and discretion to lead the design, pre-training, fine-tuning, and deployment of novel foundation models for vehicle telematics 
• Develop and implement novel algorithms for modeling both automotive physics and human driving behavior 
• Pioneer advanced self-supervised learning techniques, including the design and implementation of innovative tasks tailored to multi-modal telematics sensor data to learn rich representations of movement and driver behavior 
• Develop models robust to noise, missing data, and diverse operating conditions typical of real-world mobile sensor and IoT datasets 
• Build and manage scalable training and inference pipelines using tools like Ray, PyTorch DDP, Horovod, or similar frameworks 
• Integrate these AIs into production systems while ensuring high performance and reliability
• Optimize these AIs for efficient deployment on various platforms, including cloud and edge/mobile devices 
• Collaborate closely with engineering, product, and research teams to translate cutting-edge research into impactful products and features for the DriveWell Atlas platform 
• Mentor junior scientists and contribute to the broader AI/ML strategy at CMT 
• Stay abreast of the latest AI advancements, evaluating and adopting emerging technologies and methodologies relevant to telematics 
• Contribute to efforts in AI explainability and interpretability 
• Complete any tasks as they arise

Qualifications: 

• Bachelor’s degree or equivalent years of experience and/or certification in Artificial Intelligence, Computer Science, Electrical Engineering, Physics, Mathematics, Statistics, or a related field 
• 7+ years of professional experience in AI/ML 
• 3+ years of hands-on experience developing and deploying foundation models, with a strong portfolio in generative AI for sequential or spatio-temporal data 
• Strong, hands-on experience in building and training time-series transformer architectures for complex sensor fusion and behavioral modeling tasks is required 
• Deep expertise in designing pretraining tasks for self-supervised learning on noisy, real-world sensor data 
• Proficiency in Python and common data science libraries (e.g., Pandas, NumPy, scikit-learn)
• Extensive experience with deep learning frameworks such as PyTorch (preferred) or TensorFlow for large-scale model training and deployment 
• Solid understanding and practical experience with distributed training techniques and efficient training methodologies for large models 
• Experience building and maintaining large-scale data processing pipelines and machine learning infrastructure using tools like Spark, Airflow, Docker, and cloud platforms (e.g., AWS, GCP, Azure)
• Excellent problem-solving skills and the ability to translate complex business problems into tractable AI-based solutions 
• Strong verbal/written communication and collaboration skills, with the ability to effectively convey complex technical concepts to diverse audiences 
• Product-focused thinking with a proven ability to deliver impactful AI solutions

Nice to Haves: 

• PhD or Master's degree preferred 
• Experience with MLOps practices and tools for managing the lifecycle of machine learning models
• Publications in top-tier AI/ML conferences or journals 
• Familiarity with techniques for model interpretability and explainability (XAI) 
• Awareness of ethical AI principles, bias detection, and mitigation strategies in machine learning models, including experience with or understanding of model guardrails

Compensation and Benefits:

• Fair and competitive salary based on skills and experience, and annual performance bonus
• Equity may be awarded in the form of Restricted Stock Units (RSUs)
• Medical, Dental, Vision and Life Insurance, matching 401k, short-term & long-term disability and parental leave
• Unlimited Paid Time Off including vacation, sick days & public holidays
• Flexible scheduling and work from home policy depending on role and responsibilities

Base Salary Range

• The base salary range for this position is: $177,000 to $221,300. This range is specifically for Cambridge, MA

Additional Perks:

• Work on a mission with real impact: crashes prevented, injuries avoided, lives protected around the world
• Join an industry leader — 65 million drivers protected, powering 140+ programs across 25 countries
• Be part of the team inventing the future of mobility and road safety
• Move fast, own outcomes, do work that matters
• High ownership, small teams, and direct access to leadership — no layers between your work and its impact
• Unlimited PTO, flexible scheduling, competitive salary, annual performance bonus, RSUs, and full benefits including medical, dental, vision, and 401k match
• Summer Fridays
• Join one of our employee resource groups: Black, AAPI, LGBTQIA+, Women, Book Club, and Health & Wellness
• Comprehensive wellness, education, and employee assistance programs

Commitment to Diversity and Inclusion:

At CMT, we believe the best ideas come from a mix of backgrounds and perspectives. 

We are an equal-opportunity employer committed to creating a workplace and culture where everyone feels valued, respected, and empowered to bring their unique talents and perspectives. Diversity is essential to our success, and we actively seek candidates from all backgrounds to join our growing team. 

We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability state. CMT is headquartered in Cambridge, MA. To learn more, visit www.cmtelematics.com and follow us on X @cmtelematics.

About Cambridge Mobile Telematics:

Cambridge Mobile Telematics (CMT) is the world’s largest telematics and AI company for safer mobility. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell Fusion®, proactively identifies and reduces driving risk, leading to fewer crashes and injuries. To date, CMT’s technology has helped prevent over 126,000 crashes worldwide. CMT enables partners to measure risk, detect crashes, provide life-saving assistance, and streamline claims. Headquartered in Cambridge, MA, CMT operates globally with offices in Budapest, Hungary; Chennai, India; Seattle, Washington; Tokyo, Japan; and Zagreb, Croatia. Learn more at www.cmt.ai.

CMT is looking for a Director of AI, Foundation Models to lead development of our Telematics Foundation Model and related model variants. This leader will oversee sub-teams spanning AI Modeling and AI Ops, balancing near-term customer delivery with longer-term R&D, and turning advances in machine learning into practical capabilities across our platform, products, and customer solutions. CMT has helped protect over 65 million drivers and prevent over 126,000 crashes worldwide. We build AI to solve some of the most difficult challenges in mobility — understanding and reducing risk, detecting crashes, and getting people life-saving help. The problems are hard. The impact is real. No matter your role, your work will matter at CMT.

Responsibilities: 

• Use independent judgment and discretion to lead the Foundation Models function, including Modeling and AI Ops, setting direction, supporting execution, and developing team members
• Own the strategy, roadmap, and execution for CMT’s Telematics Foundation Model and related variants, delivering across customer work, product initiatives, and internal R&D 
• Lead end-to-end model development, including data strategy, pretraining, fine-tuning, evaluation, deployment, monitoring, and continuous improvement 
• Build, grow, and retain a high-performing team, including mentoring senior contributors and technical leaders across applied machine learning, model development, evaluation, infrastructure, and AI Ops
• Develop and scale machine learning systems for telematics, translating research and new techniques into production-ready solutions with clear business impact 
• Balance current customer commitments with forward-looking R&D, setting realistic goals and managing scope, pace, and quality across multiple initiatives 
• Stay current on relevant research, tools, and techniques, applying them where they materially improve model performance, efficiency, robustness, or cost 
• Partner cross-functionally to identify and prioritize new use cases, and expand the impact of the foundation model across the business and external opportunities 
• Establish and maintain evaluation frameworks for model performance, quality, reliability, and business value 
• Own large-scale compute strategy and spend, including planning, efficiency, infrastructure tradeoffs, and budget stewardship 
• Lead cross-functional initiatives from early prototyping through production rollout, and serve as a senior technical advisor to stakeholders on tradeoffs, timelines, and risks 
• Complete any additional tasks as they arise 

Qualifications: 

• Bachelor’s degree or equivalent years of experience and/or certification in Computer Science, Machine Learning, Statistics, Mathematics, Engineering, or a related technical field required; equivalent advanced industry experience may be considered for exceptional candidates 
• 9+ years of relevant experience in machine learning, data science, applied research, software engineering, or a related technical field
• 5+ years of experience in a management role leading high-performing technical teams
• Strong foundation in machine learning principles, algorithms, and practices, including deep learning, representation learning, supervised and self-supervised learning, and model evaluation 
• Experience leading teams that build and operate production-grade ML systems at scale
• Strong understanding of modern deep learning frameworks and tooling, such as PyTorch, TensorFlow, JAX, and related ecosystems 
• Proven coding skills using Python and other relevant tools for machine learning and data-intensive development 
• Experience with AI Ops / MLOps practices, including experiment tracking, deployment pipelines, monitoring, reproducibility, incident response, and production support 
• Experience with large-scale compute and data infrastructure, including distributed training, GPU-based workloads, cloud or cluster environments, and cost/performance optimization 
• Demonstrated ability to balance research innovation with pragmatic delivery for product and customer needs 
• Strong customer focus and presentation skills, with the ability to obtain requirements, influence outcomes, and communicate technical strategy clearly to technical and non-technical audiences
• Proven ability to lead cross-functional teams and collaborative problem-solving, incorporating feedback from partners, customers, and internal stakeholders into effective solutions 
• Experience recruiting, mentoring, and retaining strong technical talent

Nice to Haves: 

• Masters or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Engineering, or a related technical field 
• Experience with telematics, mobility, automotive, IoT, geospatial, multimodal, or other high-volume time-series data 
• Experience building reusable model platforms or foundation models that support multiple downstream use cases 
• Experience managing substantial compute budgets and infrastructure decisions at scale
• Experience supporting enterprise or customer-specific SOWs while also advancing a longer-term R&D roadmap 
• Familiarity with responsible AI, privacy, security, and model governance considerations in production environments

Compensation and Benefits:

• Fair and competitive salary based on skills and experience, and annual performance bonus
• Equity may be awarded in the form of Restricted Stock Units (RSUs)
• Medical, Dental, Vision and Life Insurance, matching 401k, short-term & long-term disability and parental leave
• Unlimited Paid Time Off including vacation, sick days & public holidays
• Flexible scheduling and work from home policy depending on role and responsibilities

Base Salary Range

• The base salary range for this position is: $229,500 to $286,900. This range is specifically for Cambridge, MA

Additional Perks:

• Work on a mission with real impact: crashes prevented, injuries avoided, lives protected around the world
• Join an industry leader — 65 million drivers protected, powering 140+ programs across 25 countries
• Be part of the team inventing the future of mobility and road safety
• Move fast, own outcomes, do work that matters
• High ownership, small teams, and direct access to leadership — no layers between your work and its impact
• Unlimited PTO, flexible scheduling, competitive salary, annual performance bonus, RSUs, and full benefits including medical, dental, vision, and 401k match
• Summer Fridays
• Join one of our employee resource groups: Black, AAPI, LGBTQIA+, Women, Book Club, and Health & Wellness
• Comprehensive wellness, education, and employee assistance programs

Commitment to Diversity and Inclusion:

At CMT, we believe the best ideas come from a mix of backgrounds and perspectives. 

We are an equal-opportunity employer committed to creating a workplace and culture where everyone feels valued, respected, and empowered to bring their unique talents and perspectives. Diversity is essential to our success, and we actively seek candidates from all backgrounds to join our growing team. 

We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability state. CMT is headquartered in Cambridge, MA. To learn more, visit www.cmtelematics.com and follow us on X @cmtelematics.

About Cambridge Mobile Telematics:

Cambridge Mobile Telematics (CMT) is the world’s largest telematics and AI company for safer mobility. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell Fusion®, proactively identifies and reduces driving risk, leading to fewer crashes and injuries. To date, CMT’s technology has helped prevent over 126,000 crashes worldwide. CMT enables partners to measure risk, detect crashes, provide life-saving assistance, and streamline claims. Headquartered in Cambridge, MA, CMT operates globally with offices in Budapest, Hungary; Chennai, India; Seattle, Washington; Tokyo, Japan; and Zagreb, Croatia. Learn more at www.cmt.ai.

CMT is looking for a Senior Software Engineer to help build and scale our on-device ML systems within the Mobile SDK. This team owns the core C/C++ runtime that powers real-time sensor processing and machine learning across millions of mobile devices. The platform enables rapid deployment of new or updated driving behavior algorithms while maintaining strict guarantees around performance, battery usage, and correctness. 

CMT is looking for a Senior Software Engineer, Machine Learning Systems to help us change the world. CMT has helped protect over 65 million drivers and prevent over 126,000 crashes worldwide. We build AI to solve some of the most difficult challenges in mobility — understanding and reducing risk, detecting crashes, and getting people life-saving help. The problems are hard. The impact is real. No matter your role, your work will matter at CMT.

Responsibilities: 

• Use independent judgment and discretion to design and implement high-performance C/C++ components for real-time sensor data processing and on-device ML execution optimized for latency, memory footprint, and battery efficiency in resource-constrained mobile environments
• Drive architectural evolution of the on-device ML runtime to support rapid algorithm iteration for over-the-air delivery 
• Evolve and optimize core runtime modules responsible for filtering, resampling, and preprocessing sensor streams 
• Translate product and algorithmic requirements into scalable, maintainable system designs
• Collaborate with Data Science and Mobile teams to integrate and validate on-device models
• Lead design and code reviews and drive high standards for performance, reliability, and maintainability
• Debug and resolve production issues related to on-device algorithm behavior
• Raise the technical bar of the team through architectural rigor and performance discipline
• Complete any additional tasks as they arise 

Qualifications: 

• Bachelor’s degree or equivalent years of experience and/or certification in Computer Science, Electrical Engineering, or related field 
• 4+ years of software engineering experience 
• Strong proficiency in modern C++ (C++11 or later) 
• Experience building high-performance, cross-platform C++ libraries deployed in production mobile environments 
• Demonstrated experience optimizing systems for CPU efficiency and memory footprint
• Experience designing or maintaining real-time or streaming data systems with strict latency constraints
• Strong debugging and analytical skills 
• Experience with automated testing and performance validation

Nice to Haves: 

• Experience with signal processing or time-series data 
• Familiarity with on-device ML frameworks (e.g., PyTorch, TensorFlow Lite, CoreML, ExecuTorch)
• Experience integrating C++ with Android or iOS 
• Experience building SDK components embedded in third-party mobile apps

Compensation and Benefits: 

• Fair and competitive salary based on skills and experience, and annual performance bonus • Equity may be awarded in the form of Restricted Stock Units (RSUs) 
• Medical, Dental, Vision and Life Insurance, matching 401k, short-term & long-term disability and parental leave 
• Unlimited Paid Time Off including vacation, sick days & public holidays 
• Flexible scheduling and work from home policy depending on role and responsibilities

Base Salary Range

• The base salary range for this position is: $139,300 to $174,100. This range is specifically for Cambridge, MA

Additional Perks:

• Work on a mission with real impact: crashes prevented, injuries avoided, lives protected around the world
• Join an industry leader — 65 million drivers protected, powering 140+ programs across 25 countries
• Be part of the team inventing the future of mobility and road safety
• Move fast, own outcomes, do work that matters
• High ownership, small teams, and direct access to leadership — no layers between your work and its impact
• Unlimited PTO, flexible scheduling, competitive salary, annual performance bonus, RSUs, and full benefits including medical, dental, vision, and 401k match
• Summer Fridays
• Join one of our employee resource groups: Black, AAPI, LGBTQIA+, Women, Book Club, and Health & Wellness
• Comprehensive wellness, education, and employee assistance programs

Commitment to Diversity and Inclusion:

At CMT, we believe the best ideas come from a mix of backgrounds and perspectives. 

We are an equal-opportunity employer committed to creating a workplace and culture where everyone feels valued, respected, and empowered to bring their unique talents and perspectives. Diversity is essential to our success, and we actively seek candidates from all backgrounds to join our growing team. 

We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability state. CMT is headquartered in Cambridge, MA. To learn more, visit www.cmtelematics.com and follow us on X @cmtelematics.

About Cambridge Mobile Telematics:

Cambridge Mobile Telematics (CMT) is the world’s largest telematics and AI company for safer mobility. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell Fusion®, proactively identifies and reduces driving risk, leading to fewer crashes and injuries. To date, CMT’s technology has helped prevent over 126,000 crashes worldwide. CMT enables partners to measure risk, detect crashes, provide life-saving assistance, and streamline claims. Headquartered in Cambridge, MA, CMT operates globally with offices in Budapest, Hungary; Chennai, India; Seattle, Washington; Tokyo, Japan; and Zagreb, Croatia. Learn more at www.cmt.ai.

ABOUT PIONEERING INTELLIGENCE 

Pioneering Intelligence builds on Flagship Pioneering’s legacy of founding cutting-edge science and computational ventures, harnessing recent advances in AI, machine learning, and data to accelerate fundamental research and create a portfolio of AI-first companies. As part of Flagship’s integrated model of science, entrepreneurship, and capital, it transforms breakthrough ideas into world-changing companies, elevating the AI advances happening across the ecosystem in human health, sustainability, and beyond.

THE ROLE

We are seeking a Principal Scientist (Embedded ML/Computational) to lead multiple AI/ML or computational projects across early stage ventures, as a part of Flagship’s company origination process. You will define and deliver pragmatic AI strategies, oversee method and platform development (e.g., systems design, drug design, molecular modeling, systems biology, protein design, LLM/agentic workflows), and ensure rigor in model development, benchmarking, scaling, and reporting. You will manage cross functional contributors as applicable, influence company direction, and represent PI to venture teams and external partners. The ideal candidate is a self-directed serial deep diver - someone who can move from protein design one week to mass spec or docking pipelines the next and then spin up LLM based agents that automate scientific workflows. 

KEY RESPONSIBILITIES

• Program Leadership: Lead development, implementation, control, and reporting of several AI/ML or computational projects within assigned ventures in line with broader strategic plans of PI and Flagship, budgets, and timelines. 
• Technical Ownership: Take a specialized technical role on project teams to oversee method development, pipeline development, and LLM based agent/workflow design; drive benchmarking, scaling, and implementation into production grade systems. 
• Best Practices: promote operational excellence in AI projects by educating cross-functional collaborators. 
• Team Leadership: Manage and/or coordinate internal and external scientists/engineers and crossfunctional project teams as applicable; mentor early hires; support recruiting and interview. 
• Planning & Resourcing: Contribute to project planning, including budgets, resources, and timelines; surface risks and tradeoffs early with clear options. 
• Landscape & Strategy: Independently scout emerging literature and the AI/ML landscape; synthesize concepts to propose new development strategies and identify opportunities for PI and venture portfolios. 
• Representation & Community: Represent PI to portfolio companies and external partners; act as a recognized subject matter expert; actively participate in scientific conferences and meetings. 
• Communication & Influence: Influence the course of projects and technical approaches; adapt and present complex findings to diverse audiences to support meaningful interpretation and action. 

PROFESSIONAL EXPERIENCE & QUALIFICATIONS 

• Master’s, or PhD in a relevant field (e.g., machine learning, mathematics, statistics, computational sciences) with 5+ years' experience scientific/engineering/computational in academic, pharmaceutical, or biotechnology settings; industry AI/ML experience preferred. 
• Experience driving results directly or indirectly through teams of engineers/scientists in dynamic, fastpaced, entrepreneurial, and technical environments. 
• Clear evidence of sustained independent thought and creativity driving high impact, cross disciplinary AI/ML projects. 
• Successful track record of leadership and contribution to decision making on progression of AI/ML models within projects or programs. 
• Depth across multiple core tools and concepts, including Python; modern ML frameworks (PyTorch or JAX/TensorFlow); version control; databases; deep learning architectures; and relevant informatics software. 
• Consistent record of outstanding performance reflected in publications, patents, or high impact internal reports where applicable 

Preferred Qualifications 

• Breadth across domains such as protein modeling/design, proteomics/mass spec, cheminformatics/docking/ADMET, biophysics/MD, and LLM/agentic automation. 
• MLOps expertise: data contracts and lineage (e.g., DVC/LakeFS), experiment tracking (MLflow/W&B), secure AWS infrastructure (S3, Batch/ECS/EKS, SageMaker), Docker, IaC (Terraform/CDK), and CI/CD (GitHub Actions). 
• Generative modeling (diffusion/flow/VAEs) for sequences, graphs, or 3D structures; docking rescoring (e.g., gnina, DiffDock) and pose quality metrics. 
• Workflow orchestration (Airflow/Prefect/Argo), data warehouses (Redshift/Snowflake), vector search (FAISS/pgvector), and lightweight internal tools (FastAPI, Streamlit/Gradio). 
• Experience mentoring early hires, acting as interim Head of ML, and contributing to hiring plans and interview processes at startups. 

WHY PIONEERING INTELLIGENCE

• Operate at the frontier: Build and deploy AI/ML that directly powers discovery and decision making across multiple new ventures. 
• Own the full stack: From scoping and data contracts to models, MLOps, and internal UIs—ship endtoend systems that scientists rely on daily. 
• Compound impact: Convert one off wins into shared libraries and templates adopted across the PI portfolio. 
• Work with founders: Partner closely with venture leadership and PI platform engineers; influence strategy through hands on delivery. 
• Grow fast: Stretch across domains, take on situational leadership, present at conferences, and help shape first ML teams. 

ABOUT FLAGSHIP PIONEERING:

Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  

Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.

We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

#LI-MB1

The salary range for this role is $208,000 - $286,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Pioneering Intelligence currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Pioneering Intelligence’s good faith estimate as of the date of publication and may be modified in the future.

ABOUT PIONEERING INTELLIGENCE 

Pioneering Intelligence builds on Flagship Pioneering’s legacy of founding cutting-edge science and computational ventures, harnessing recent advances in AI, machine learning, and data to accelerate fundamental research and create a portfolio of AI-first companies. As part of Flagship’s integrated model of science, entrepreneurship, and capital, it transforms breakthrough ideas into world-changing companies, elevating the AI advances happening across the ecosystem in human health, sustainability, and beyond.

THE ROLE

We are seeking a Principal Scientist (Embedded ML/Computational) to lead multiple AI/ML or computational projects across early stage ventures, as a part of Flagship’s company origination process. You will define and deliver pragmatic AI strategies, oversee method and platform development (e.g., systems design, drug design, molecular modeling, systems biology, protein design, LLM/agentic workflows), and ensure rigor in model development, benchmarking, scaling, and reporting. You will manage cross functional contributors as applicable, influence company direction, and represent PI to venture teams and external partners. The ideal candidate is a self-directed serial deep diver - someone who can move from protein design one week to mass spec or docking pipelines the next and then spin up LLM based agents that automate scientific workflows. 

KEY RESPONSIBILITIES

• Program Leadership: Lead development, implementation, control, and reporting of several AI/ML or computational projects within assigned ventures in line with broader strategic plans of PI and Flagship, budgets, and timelines. 
• Technical Ownership: Take a specialized technical role on project teams to oversee method development, pipeline development, and LLM based agent/workflow design; drive benchmarking, scaling, and implementation into production grade systems. 
• Best Practices: promote operational excellence in AI projects by educating cross-functional collaborators. 
• Team Leadership: Manage and/or coordinate internal and external scientists/engineers and crossfunctional project teams as applicable; mentor early hires; support recruiting and interview. 
• Planning & Resourcing: Contribute to project planning, including budgets, resources, and timelines; surface risks and tradeoffs early with clear options. 
• Landscape & Strategy: Independently scout emerging literature and the AI/ML landscape; synthesize concepts to propose new development strategies and identify opportunities for PI and venture portfolios. 
• Representation & Community: Represent PI to portfolio companies and external partners; act as a recognized subject matter expert; actively participate in scientific conferences and meetings. 
• Communication & Influence: Influence the course of projects and technical approaches; adapt and present complex findings to diverse audiences to support meaningful interpretation and action. 

PROFESSIONAL EXPERIENCE & QUALIFICATIONS 

• Master’s, or PhD in a relevant field (e.g., machine learning, mathematics, statistics, computational sciences) with 5+ years' experience scientific/engineering/computational in academic, pharmaceutical, or biotechnology settings; industry AI/ML experience preferred. 
• Experience driving results directly or indirectly through teams of engineers/scientists in dynamic, fastpaced, entrepreneurial, and technical environments. 
• Clear evidence of sustained independent thought and creativity driving high impact, cross disciplinary AI/ML projects. 
• Successful track record of leadership and contribution to decision making on progression of AI/ML models within projects or programs. 
• Depth across multiple core tools and concepts, including Python; modern ML frameworks (PyTorch or JAX/TensorFlow); version control; databases; deep learning architectures; and relevant informatics software. 
• Consistent record of outstanding performance reflected in publications, patents, or high impact internal reports where applicable 

Preferred Qualifications 

• Breadth across domains such as protein modeling/design, proteomics/mass spec, cheminformatics/docking/ADMET, biophysics/MD, and LLM/agentic automation. 
• MLOps expertise: data contracts and lineage (e.g., DVC/LakeFS), experiment tracking (MLflow/W&B), secure AWS infrastructure (S3, Batch/ECS/EKS, SageMaker), Docker, IaC (Terraform/CDK), and CI/CD (GitHub Actions). 
• Generative modeling (diffusion/flow/VAEs) for sequences, graphs, or 3D structures; docking rescoring (e.g., gnina, DiffDock) and pose quality metrics. 
• Workflow orchestration (Airflow/Prefect/Argo), data warehouses (Redshift/Snowflake), vector search (FAISS/pgvector), and lightweight internal tools (FastAPI, Streamlit/Gradio). 
• Experience mentoring early hires, acting as interim Head of ML, and contributing to hiring plans and interview processes at startups. 

WHY PIONEERING INTELLIGENCE

• Operate at the frontier: Build and deploy AI/ML that directly powers discovery and decision making across multiple new ventures. 
• Own the full stack: From scoping and data contracts to models, MLOps, and internal UIs—ship endtoend systems that scientists rely on daily. 
• Compound impact: Convert one off wins into shared libraries and templates adopted across the PI portfolio. 
• Work with founders: Partner closely with venture leadership and PI platform engineers; influence strategy through hands on delivery. 
• Grow fast: Stretch across domains, take on situational leadership, present at conferences, and help shape first ML teams. 

ABOUT FLAGSHIP PIONEERING:

Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  

Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.

We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

#LI-MB1

The salary range for this role is $208,000 - $286,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Pioneering Intelligence currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Pioneering Intelligence’s good faith estimate as of the date of publication and may be modified in the future.

ABOUT PIONEERING INTELLIGENCE 

Pioneering Intelligence builds on Flagship Pioneering’s legacy of founding cutting-edge science and computational ventures, harnessing recent advances in AI, machine learning, and data to accelerate fundamental research and create a portfolio of AI-first companies. As part of Flagship’s integrated model of science, entrepreneurship, and capital, it transforms breakthrough ideas into world-changing companies, elevating the AI advances happening across the ecosystem in human health, sustainability, and beyond.

THE ROLE

We are seeking a Principal Scientist (Embedded ML/Computational) to lead multiple AI/ML or computational projects across early stage ventures, as a part of Flagship’s company origination process. You will define and deliver pragmatic AI strategies, oversee method and platform development (e.g., systems design, drug design, molecular modeling, systems biology, protein design, LLM/agentic workflows), and ensure rigor in model development, benchmarking, scaling, and reporting. You will manage cross functional contributors as applicable, influence company direction, and represent PI to venture teams and external partners. The ideal candidate is a self-directed serial deep diver - someone who can move from protein design one week to mass spec or docking pipelines the next and then spin up LLM based agents that automate scientific workflows. 

KEY RESPONSIBILITIES

• Program Leadership: Lead development, implementation, control, and reporting of several AI/ML or computational projects within assigned ventures in line with broader strategic plans of PI and Flagship, budgets, and timelines. 
• Technical Ownership: Take a specialized technical role on project teams to oversee method development, pipeline development, and LLM based agent/workflow design; drive benchmarking, scaling, and implementation into production grade systems. 
• Best Practices: promote operational excellence in AI projects by educating cross-functional collaborators. 
• Team Leadership: Manage and/or coordinate internal and external scientists/engineers and crossfunctional project teams as applicable; mentor early hires; support recruiting and interview. 
• Planning & Resourcing: Contribute to project planning, including budgets, resources, and timelines; surface risks and tradeoffs early with clear options. 
• Landscape & Strategy: Independently scout emerging literature and the AI/ML landscape; synthesize concepts to propose new development strategies and identify opportunities for PI and venture portfolios. 
• Representation & Community: Represent PI to portfolio companies and external partners; act as a recognized subject matter expert; actively participate in scientific conferences and meetings. 
• Communication & Influence: Influence the course of projects and technical approaches; adapt and present complex findings to diverse audiences to support meaningful interpretation and action. 

PROFESSIONAL EXPERIENCE & QUALIFICATIONS 

• Master’s, or PhD in a relevant field (e.g., machine learning, mathematics, statistics, computational sciences) with 5+ years' experience scientific/engineering/computational in academic, pharmaceutical, or biotechnology settings; industry AI/ML experience preferred. 
• Experience driving results directly or indirectly through teams of engineers/scientists in dynamic, fastpaced, entrepreneurial, and technical environments. 
• Clear evidence of sustained independent thought and creativity driving high impact, cross disciplinary AI/ML projects. 
• Successful track record of leadership and contribution to decision making on progression of AI/ML models within projects or programs. 
• Depth across multiple core tools and concepts, including Python; modern ML frameworks (PyTorch or JAX/TensorFlow); version control; databases; deep learning architectures; and relevant informatics software. 
• Consistent record of outstanding performance reflected in publications, patents, or high impact internal reports where applicable 

Preferred Qualifications 

• Breadth across domains such as protein modeling/design, proteomics/mass spec, cheminformatics/docking/ADMET, biophysics/MD, and LLM/agentic automation. 
• MLOps expertise: data contracts and lineage (e.g., DVC/LakeFS), experiment tracking (MLflow/W&B), secure AWS infrastructure (S3, Batch/ECS/EKS, SageMaker), Docker, IaC (Terraform/CDK), and CI/CD (GitHub Actions). 
• Generative modeling (diffusion/flow/VAEs) for sequences, graphs, or 3D structures; docking rescoring (e.g., gnina, DiffDock) and pose quality metrics. 
• Workflow orchestration (Airflow/Prefect/Argo), data warehouses (Redshift/Snowflake), vector search (FAISS/pgvector), and lightweight internal tools (FastAPI, Streamlit/Gradio). 
• Experience mentoring early hires, acting as interim Head of ML, and contributing to hiring plans and interview processes at startups. 

WHY PIONEERING INTELLIGENCE

• Operate at the frontier: Build and deploy AI/ML that directly powers discovery and decision making across multiple new ventures. 
• Own the full stack: From scoping and data contracts to models, MLOps, and internal UIs—ship endtoend systems that scientists rely on daily. 
• Compound impact: Convert one off wins into shared libraries and templates adopted across the PI portfolio. 
• Work with founders: Partner closely with venture leadership and PI platform engineers; influence strategy through hands on delivery. 
• Grow fast: Stretch across domains, take on situational leadership, present at conferences, and help shape first ML teams. 

ABOUT FLAGSHIP PIONEERING:

Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  

Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.

We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

#LI-MB1

The salary range for this role is $208,000 - $286,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Pioneering Intelligence currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Pioneering Intelligence’s good faith estimate as of the date of publication and may be modified in the future.

Build Models Where None Exist Yet.

At Flagship Pioneering, we create companies from first principles. Within Flagship Labs, small founding teams define new technical theses, test them rapidly, and build ventures around breakthrough ideas.

We are forming a machine learning team inside a newly launched venture, Flagship Labs 120. Our work focuses on extracting latent structure from information-rich measurements of complex physical systems—often requiring mechanism-informed modeling, thoughtful inductive bias design, and principled approaches to inverse problems.

This is a zero-to-one role focused on modeling innovation rather than routine optimization. You’ll design, prototype, test, and refine new approaches that help define the technical foundation of a platform from day one.

What You’ll Do

• Develop and iterate on ML models for complex measurement data, from representation design through validation
• Design objectives and architectures that respect known constraints, symmetries, or latent structure in the data
• Explore and compare modeling strategies, balancing strong baselines with more experimental approaches when appropriate
• Investigate model behavior and failure modes to improve robustness and interpretability
• Collaborate closely with experimental and technical teammates to align modeling with data generation
• Contribute to shaping the long-term ML strategy and technical direction of a new venture

Who You Are

You may come from physics, applied mathematics, engineering, computer science, or another quantitative field.  You have hands-on experience developing machine learning models—ideally in deep learning, representation learning, probabilistic modeling, or related areas. You are comfortable implementing and modifying models, training them end-to-end, and working directly with real data.

You likely:

• Think algorithmically and reason from underlying structure
• Are comfortable adapting or extending model architectures when needed
• Have built and debugged meaningful ML systems or research prototypes
• Enjoy operating in dynamic, early-stage environments
• Read papers, build prototypes to test ideas, and translate concepts into working systems

What matters most is your ability to reason across data, models, and the systems they represent.

Technical Background

Required

• Strong hands-on experience building and training modern ML models
• Fluency in Python and at least one major ML framework (e.g., PyTorch or equivalent)
• Experience working with real-world or experimentally generated data
• Ability to design, run, and interpret ML experiments
• Comfort working in practical development environments (e.g., cloud infrastructure, experiment tracking, reproducible workflows)

Helpful

• Experience with inverse problems, latent-variable inference, or structured generative modeling (e.g., diffusion or flow-based methods)
• Familiarity with geometric or symmetry-aware architectures
• Experience incorporating physical or structural constraints into learning systems
• Experience working with time-series or high-dimensional signal data
• Exposure to biology, chemistry, physics, or related sciences

ABOUT FLAGSHIP PIONEERING

Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  

Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.

We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

The salary range for this role is $130,000 - $230,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. FL120 currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on FL120's good faith estimate as of the date of publication and may be modified in the future.

What You Will Contribute To Altos

As a Senior or Principal Machine Learning Scientist, you will play a prominent role in developing generative AI/ML models for multi-modal, multiscale biology from virtual cells to agentic target assessment.  We are looking for a hands-on, creative, and collaborative individual to join our multidisciplinary team of scientists and engineers focused on transforming how we treat aging and disease. The successful candidate will thrive in a fast-paced environment that emphasizes teamwork, transparency, scientific excellence, originality, rigor, and integrity.

Responsibilities

• Pioneer novel machine learning methodologies and statistical frameworks (e.g., generative models, causal inference, diffusion models, and advanced transformer architectures) to address fundamental challenges in cell health and rejuvenation
• Contribute to setting the long-term technical vision and research strategy for a core domain (e.g., multi-modal data fusion, perturbation modeling) within the Institute of Computation
• Translate your deep understanding of the mathematical and theoretical underpinnings of cutting-edge AI research into high-impact applications
• Design, implement, and optimize large-scale machine learning systems using modern frameworks (e.g., PyTorch, JAX) and  agile practices
• Develop and manage efficient distributed training strategies across multiple GPUs and compute clusters to handle terabytes of multi-modal biological data
• Develop robust approaches for multi-modal data integration and cross-domain mapping to extract actionable biological insights
• Apply computational thinking to solve problems in drug target identification, compound assessment, and prediction of cellular perturbation responses
• Lead the full ML development lifecycle from theoretical conception and data strategy through model development, training, and evaluation
• Act as a key technical mentor to Machine Learning Scientists and Engineers, raising the bar for scientific rigor and model robustness across the organization.

Who You Are

• Proven track record leveraging machine learning to solve real-world problems;
• Expertise in one or more of the following: generative models, language models, computer vision, bayesian inference, causal reasoning & inference, transfer & multi-task learning, diffusion models, graph neural networks, active learning, cooperative agents
• Experience writing production-quality code with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or similar
• Experience with multi-GPU and distributed training at scale
• A team player who thrives in collaborative environments and is committed to enabling colleagues to reach their full potential through giving and requesting feedback focussed on professional growth
• Able to advise others across the wider function / company on cutting edge practices and approaches to enable the science / research. Desire to constantly expand your skillset and knowledge. Keen to learn more about biology, computational science, and medicine;
• Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities.

Minimum Qualifications

• Ph.D. in Machine Learning, Computer Science, Artificial Intelligence, Statistics, or a related quantitative field, demonstrating a deep theoretical foundation in ML/AI.
• 6+ years of of relevant post-PhD work experience in either an academic or industry setting
• Proven experience developing and applying complex machine learning models, preferably with a significant portion of that time spent in a fast-paced industry or translational research environment.
• A strong track record of leading and publishing innovative, peer-reviewed research in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR) or high-impact scientific journals.
• Excellent scientific communication skills: verbally and in writing; with computational and non-computational audiences, in informal 1-1 settings, team meetings, and formal seminars
• Expertise in several of the following: deep learning, reinforcement learning, generative models, language models, computer vision, Bayesian inference, causal reasoning & inference, transfer & multi-task learning, graph neural networks, active learning, hybrid mechanistic/ML models
• Proven experience applying sophisticated ML techniques to molecular and cell biological data sets (e.g., NGS, spatial omics, bioimaging).

Preferred Qualifications

• Experience in cell health and rejuvenation related research area
• Experience in the application of machine learning methods to biological data
• Experience in computational approaches to drug discovery
• Experience with software development methodologies and open-source software

The salary range for Redwood City, CA:

• Senior Scientist I, Machine Learning: $257,400 - $330,000
• Senior Scientist II, Machine Learning: $290,900 - $373,000
• Principal Scientist, Machine Learning: $330,800 - $424,100

The salary range for San Diego, CA:

• Senior Scientist I, Machine Learning: $239,500 - $307,000
• Senior Scientist II, Machine Learning: $262,100 - $336,000
• Principal Scientist, Machine Learning: $312,100 - $400,100

Exact compensation may vary based on skills, experience, and location.

Before submitting your application:

- Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice)
- This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.

#LI-NN1

What You Will Contribute To Altos

As a Senior or Principal Machine Learning Scientist, you will play a prominent role in developing generative AI/ML models for multi-modal, multiscale biology from virtual cells to agentic target assessment.  We are looking for a hands-on, creative, and collaborative individual to join our multidisciplinary team of scientists and engineers focused on transforming how we treat aging and disease. The successful candidate will thrive in a fast-paced environment that emphasises teamwork, transparency, scientific excellence, originality, rigor, and integrity.

Responsibilities

• Pioneer novel machine learning methodologies and statistical frameworks (e.g., generative models, causal inference, diffusion models, and advanced transformer architectures) to address fundamental challenges in cell health and rejuvenation
• Contribute to setting the long-term technical vision and research strategy for a core domain (e.g., multi-modal data fusion, perturbation modeling) within the Institute of Computation
• Translate your deep understanding of the mathematical and theoretical underpinnings of cutting-edge AI research into high-impact applications
• Design, implement, and optimize large-scale machine learning systems using modern frameworks (e.g., PyTorch, JAX) and  agile practices
• Develop and manage efficient distributed training strategies across multiple GPUs and compute clusters to handle terabytes of multi-modal biological data
• Develop robust approaches for multi-modal data integration and cross-domain mapping to extract actionable biological insights
• Apply computational thinking to solve problems in drug target identification, compound assessment, and prediction of cellular perturbation responses
• Lead the full ML development lifecycle from theoretical conception and data strategy through model development, training, and evaluation
• Act as a key technical mentor to Machine Learning Scientists and Engineers, raising the bar for scientific rigor and model robustness across the organization.

Who You Are

• Proven track record leveraging machine learning to solve real-world problems;
• Expertise in one or more of the following: generative models, language models, computer vision, bayesian inference, causal reasoning & inference, transfer & multi-task learning, diffusion models, graph neural networks, active learning, cooperative agents
• Experience writing production-quality code with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or similar
• Experience with multi-GPU and distributed training at scale
• A team player who thrives in collaborative environments and is committed to enabling colleagues to reach their full potential through giving and requesting feedback focussed on professional growth
• Able to advise others across the wider function / company on cutting edge practices and approaches to enable the science / research. Desire to constantly expand your skillset and knowledge. Keen to learn more about biology, computational science, and medicine;
• Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities.

Minimum Qualifications

• Ph.D. in Machine Learning, Computer Science, Artificial Intelligence, Statistics, or a related quantitative field, demonstrating a deep theoretical foundation in ML/AI.
• 6+ years of of relevant post-PhD work experience in either an academic or industry setting
• Proven experience developing and applying complex machine learning models, preferably with a significant portion of that time spent in a fast-paced industry or translational research environment.
• A strong track record of leading and publishing innovative, peer-reviewed research in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR) or high-impact scientific journals.
• Excellent scientific communication skills: verbally and in writing; with computational and non-computational audiences, in informal 1-1 settings, team meetings, and formal seminars
• Expertise in several of the following: deep learning, reinforcement learning, generative models, language models, computer vision, Bayesian inference, causal reasoning & inference, transfer & multi-task learning, graph neural networks, active learning, hybrid mechanistic/ML models
• Proven experience applying sophisticated ML techniques to molecular and cell biological data sets (e.g., NGS, spatial omics, bioimaging).

Preferred Qualifications

• Experience in cell health and rejuvenation related research area
• Experience in the application of machine learning methods to biological data
• Experience in computational approaches to drug discovery
• Experience with software development methodologies and open-source software

The salary range for Cambridge, UK:

• Senior Scientist I, Machine Learning: £115,600 - £152,100
• Senior Scientist II, Machine Learning: £131,900 - £173,500
• Principal Scientist, Machine Learning: £173,700 - £228,600

Exact compensation may vary based on skills, experience, and location.

Before submitting your application:

- Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice)
- This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.

#LI-NN1

What You Will Contribute to Altos

This is an opportunity to join the state-of-the-art Virtual Cell team that recently won the Generalist prize in the ARC Virtual Cell Challenge. Here you will help to accelerate and optimize our progress in developing multi-modal generative foundation models for multiscale biology. 

In this role, you will be an integral part of our multidisciplinary teams enabling Altos to achieve its mission. You will partner and collaborate with other Machine Learning Scientists and Engineers, as well as other computational scientists and biologists, across the Institute of Computation to contribute to the Altos research and translation ecosystem. This role is focused on improving our state-of-the-art “virtual cell” models, encompassing gene and protein modeling, imaging, and other modalities to aid in the discovery of novel interventions for aging and disease. 

The successful candidate will thrive in a fast-paced environment that emphasises/emphasizes *please spell differently for UK/US teamwork, transparency, scientific excellence, originality, and integrity.

Responsibilities

• Use your experience to focus on designing, developing and evaluating state of the art foundation and focused models, at scale, to advance the Altos mission
• Pre-train and fine-tune large-scale machine learning systems using multimodal biological data and prior knowledge inputs.
• Pioneer novel machine learning methodologies and statistical frameworks (e.g., generative models,  diffusion/flow matching models, and advanced transformer architectures) to address fundamental challenges in cell health and rejuvenation
• Design, implement, and optimize large-scale machine learning systems using modern frameworks (e.g., PyTorch, JAX), AI-assisted coding, and agile practices
• Develop and manage efficient distributed training strategies across multiple GPUs and compute clusters to handle terabytes of multi-modal biological data
• Develop robust approaches for multi-modal data integration and cross-domain mapping to extract actionable biological insights
• Participate in the full ML development lifecycle from theoretical conception and data strategy through model development, training, and evaluation

Who You Are

• Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities
• Highly collaborative in mindset and ways of working
• Self-motivated to drive and deliver on projects and goals
• Focused on professional growth and expanding you skillset and knowledge
• Able to communicate and explain the design, results, conclusions and the impact of their work to both scientific and nonscientific staff.
• Able to stay up-to-date on the latest developments in deep learning and apply knowledge to their work.
• Keen to take the opportunity to contribute to seminars and other scientific initiatives within Altos and the broader scientific community.

Minimum Qualifications

• Ph.D. in Machine Learning, Computer Science, Artificial Intelligence, Statistics, or a related quantitative field, demonstrating a deep theoretical foundation in ML/AI.
• Relevant work experience in either an academic or industry setting.
• Prior experience in developing and implementing novel generative AI models in a subset of the following: transformers, multi-modality, diffusion/flow matching models.
• Can demonstrate a deep understanding and expertise of Machine Learning Principles and how they apply to different models  
• Proven experience developing and applying complex machine learning models, preferably with a significant portion of that time spent in a fast-paced industry or translational research environment.
• Very strong programming skills, including experience with Python and deep learning libraries (PyTorch, Hugging Face Transformers, H-F Datasets, H-F Accelerate)
• Experience writing production-quality code with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or similar;
• Experience with multi-GPU and distributed training at scale;

Preferred Qualifications

• Strong track record of published peer reviewed innovative AI/ML research 
• Experience in cell health and rejuvenation related research area
• Experience in the application of machine learning methods to biological data
• Experience in computational approaches to drug discovery
• Experience with software development methodologies and open-source software

The salary range for Cambridge, UK:

• Scientist I, Machine Learning: £74,900 - £98,500
• Scientist II, Machine Learning: £107,900 - £142,000
• Senior Scientist I, Machine Learning: £115,600 - £152,100

Exact compensation may vary based on skills, experience, and location.

Before submitting your application:

- Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice)
- This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.

#LI-NN1

What You Will Contribute To Altos

This is an opportunity to join the state-of-the-art Virtual Cell team that recently won the Generalist prize in the ARC Virtual Cell Challenge. Here you will help to accelerate and optimize our progress in developing multi-modal generative foundation models for multiscale biology. 

In this role, you will be an integral part of our multidisciplinary teams enabling Altos to achieve its mission. You will partner and collaborate with other Machine Learning Scientists and Engineers, as well as other computational scientists and biologists, across the Institute of Computation to contribute to the Altos research and translation ecosystem. This role is focused on improving our state-of-the-art “virtual cell” models, encompassing gene and protein modeling, imaging, and other modalities to aid in the discovery of novel interventions for aging and disease. 

The successful candidate will thrive in a fast-paced environment that emphasises/emphasizes *please spell differently for UK/US teamwork, transparency, scientific excellence, originality, and integrity.

Responsibilities

• Use your experience to focus on designing, developing and evaluating state of the art foundation and focused models, at scale, to advance the Altos mission
• Pre-train and fine-tune large-scale machine learning systems using multimodal biological data and prior knowledge inputs.
• Pioneer novel machine learning methodologies and statistical frameworks (e.g., generative models,  diffusion/flow matching models, and advanced transformer architectures) to address fundamental challenges in cell health and rejuvenation
• Design, implement, and optimize large-scale machine learning systems using modern frameworks (e.g., PyTorch, JAX), AI-assisted coding, and agile practices
• Develop and manage efficient distributed training strategies across multiple GPUs and compute clusters to handle terabytes of multi-modal biological data
• Develop robust approaches for multi-modal data integration and cross-domain mapping to extract actionable biological insights
• Participate in the full ML development lifecycle from theoretical conception and data strategy through model development, training, and evaluation

Who You Are

• Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities
• Highly collaborative in mindset and ways of working
• Self-motivated to drive and deliver on projects and goals
• Focused on professional growth and expanding you skillset and knowledge
• Able to communicate and explain the design, results, conclusions and the impact of their work to both scientific and nonscientific staff.
• Able to stay up-to-date on the latest developments in deep learning and apply knowledge to their work.
• Keen to take the opportunity to contribute to seminars and other scientific initiatives within Altos and the broader scientific community.

Minimum Qualifications

• Ph.D. in Machine Learning, Computer Science, Artificial Intelligence, Statistics, or a related quantitative field, demonstrating a deep theoretical foundation in ML/AI.
• Relevant work experience in either an academic or industry setting.
• Prior experience in developing and implementing novel generative AI models in a subset of the following: transformers, multi-modality, diffusion/flow matching models.
• Can demonstrate a deep understanding and expertise of Machine Learning Principles and how they apply to different models  
• Proven experience developing and applying complex machine learning models, preferably with a significant portion of that time spent in a fast-paced industry or translational research environment.
• Very strong programming skills, including experience with Python and deep learning libraries (PyTorch, Hugging Face Transformers, H-F Datasets, H-F Accelerate)
• Experience writing production-quality code with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or similar;
• Experience with multi-GPU and distributed training at scale;

Preferred Qualifications

• Strong track record of published peer reviewed innovative AI/ML research 
• Experience in cell health and rejuvenation related research area
• Experience in the application of machine learning methods to biological data
• Experience in computational approaches to drug discovery
• Experience with software development methodologies and open-source softwar

The salary range for Redwood City, CA:

• Scientist I, Machine Learning: $200,900 - $257,500
• Scientist II, Machine Learning: $226,200 - $290,000
• Senior Scientist I, Machine Learning: $257,400 - $330,000

The salary range for San Diego, CA:

• Scientist I, Machine Learning: $179,400 - $230,000
• Scientist II, Machine Learning: $212,900 - $273,000
• Senior Scientist I, Machine Learning: $239,500 - $307,000

Exact compensation may vary based on skills, experience, and location.

Before submitting your application:

- Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice)
- This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.

#LI-NN1

We are hiring a Principal Machine Learning Engineer to work on cutting-edge R&D and translating research into customer-centric solutions.   

As innovators in speech technology, our mission is to Understand Every Voice—a vision that has propelled us to be world leaders of Voice AI. Fuelled by innovation, inclusivity, and a passion for making a global impact through world-leading Speech AI, we're looking for an experienced Principal Machine Learning Engineer to accelerate our efforts towards exceptional speech solutions. 

Our Modelling Team trains diverse models, including large self-supervised ones, supporting Speechmatics towards being the most accurate speech recognition system globally. It also ensures their deployment into production, working with the latest developments in ML, but also with the best engineering practices for software engineering and model serving. 

This is a hands-on, technical leadership role with high ownership.  You will develop and deploy advanced speech systems that power our products as well as co-define the technical vision for ML, drive innovation, and mentor engineering teams. Your work will span the entire codebase.    

What You’ll Do:

• Develop and deploy ML models, translating research into scalable, maintainable code and services 
• Optimise ML models for speed, accuracy, and cost efficiency 
• Evaluate and integrate cutting-edge approaches into our ML stack
• Identify and solve complex ML problems across the organisation
• Define and enforce best practices for code quality, model lifecycle management, etc.
• Mentor engineers and foster a culture of technical excellence and innovation
• Co-define the long-term technical vision 

What We’re Looking For:

• Deep understanding of the modern Machine Learning stack, for example:
  • Knowledge of contemporary transformer architectures (e.g., GQA, KV-caching) and best practices
  • Expertise in distributed training techniques
  • Familiarity with optimisation strategies for model inference (e.g., dynamic batching, flash attention, speculative decoding)
• Proven track record of developing and deploying models at scale
• Expertise in Python, ML frameworks (TensorFlow, PyTorch), and experience with cloud platforms
• Strong familiarity with MLOps principles, CI/CD pipelines, and containerization (Docker, Kubernetes)

  With preferred backgrounds covering some of the following:

• Experience with speech-to-text or text-to-speech 
• Contributions to open-source projects or published research in top-tier conferences