Docker Jobs in Honolulu.
All active Docker roles based in Honolulu.
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Software Engineer, Data Infrastructure & Acquisition - Honolulu, HI, USA
The mission of Speechify is to make sure that reading is never a barrier to learning.
Over 50 million people use Speechify’s text-to-speech products to turn whatever they’re reading – PDFs, books, Google Docs, news articles, websites – into audio, so they can read faster, read more, and remember more. Speechify’s text-to-speech reading products include its iOS app, Android App, Mac App, Chrome Extension, and Web App. Google recently named Speechify the Chrome Extension of the Year and Apple named Speechify its 2025 Design Award winner for Inclusivity.
Today, nearly 200 people around the globe work on Speechify in a 100% distributed setting – Speechify has no office. These include frontend and backend engineers, AI research scientists, and others from Amazon, Microsoft, and Google, leading PhD programs like Stanford, high growth startups like Stripe, Vercel, Bolt, and many founders of their own companies.
Overview
We're looking to hire for our Data side of our AI team at Speechify. This role is responsible for all aspects of data collection to support our model training operations. We are able to build high-quality datasets at petabyte-scale and low cost through a tight integration of infrastructure, engineering, and research work. We are looking for a skilled Software Engineer to join us.
What You’ll Do
- Be scrappy to find new sources of audio data and bring it into our ingestion pipeline
- Operate and extend the cloud infrastructure for our ingestion pipeline, currently running on GCP and managed with Terraform.
- Collaborate closely with our Scientists to shift the cost/throughput/quality frontier, delivering richer data at bigger scale and lower cost to power our next-generation models.
- Collaborate with others on the AI Team and Speechify Leadership to craft the AI Team’s dataset roadmap to power Speechify’s next-generation consumer and enterprise products.
An Ideal Candidate Should Have
- BS/MS/PhD in Computer Science or a related field.
- 5+ years of industry experience in software development.
- Proficiency with bash/Python scripting in Linux environments
- Proficiency in Docker and Infrastructure-as-Code concepts and professional experience with at least one major Cloud Provider (we use GCP)
- Experience with web crawlers, large-scale data processing workflows is a plus
- Ability to handle multiple tasks and adapt to changing priorities.
- Strong communication skills, both written and verbal.
What we offer
- A fast-growing environment where you can help shape the company and product.
- An entrepreneurial-minded team that supports risk, intuition, and hustle.
- A hands-off management approach so you can focus and do your best work.
- An opportunity to make a big impact in a transformative industry.
- Competitive salaries, a friendly and laid-back atmosphere, and a commitment to building a great asynchronous culture.
- Opportunity to work on a life-changing product that millions of people use.
- Build products that directly impact and support people with learning differences like dyslexia, ADD, low vision, concussions, autism, and more.
- Work in one of the fastest-growing sectors of tech, the intersection of artificial intelligence and audio.
Compensation: The United States base salary range for this full-time position is $140,000-$200,000 + bonus + equity depending on experience
Think you’re a good fit for this job?
Tell us more about yourself and why you're interested in the role when you apply.
And don’t forget to include links to your portfolio and LinkedIn.
Not looking but know someone who would make a great fit?
Refer them!
Speechify is committed to a diverse and inclusive workplace.
Speechify does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Ready to apply?
Apply to Speechify
The mission of Speechify is to make sure that reading is never a barrier to learning.
Over 50 million people use Speechify’s text-to-speech products to turn whatever they’re reading – PDFs, books, Google Docs, news articles, websites – into audio, so they can read faster, read more, and remember more. Speechify’s text-to-speech reading products include its iOS app, Android App, Mac App, Chrome Extension, and Web App. Google recently named Speechify the Chrome Extension of the Year and Apple named Speechify its 2025 Design Award winner for Inclusivity.
Today, nearly 200 people around the globe work on Speechify in a 100% distributed setting – Speechify has no office. These include frontend and backend engineers, AI research scientists, and others from Amazon, Microsoft, and Google, leading PhD programs like Stanford, high growth startups like Stripe, Vercel, Bolt, and many founders of their own companies.
Overview
The responsibilities of our Platform team include building and maintaining all backend services, including, but not limited to, payments, analytics, subscriptions, new products, text to speech, and external APIs.
This is a key role and ideal for someone who thinks strategically, enjoys fast-paced environments, is passionate about making product decisions, and has experience building great user experiences that delight users.
We are a flat organization that allows anyone to become a leader by showing excellent technical skills and delivering results consistently and fast. Work ethic, solid communication skills, and obsession with winning are paramount.
Our interview process involves several technical interviews and we aim to complete them within 1 week.
What You’ll Do
- Design, develop, and maintain robust APIs including public TTS API, internal APIs like Payment, Subscription, Auth and Consumption Tracking, ensuring they meet business and scalability requirements
- Oversee the full backend API landscape, enhancing and optimizing for performance and maintainability
- Collaborate on B2B solutions, focusing on customization and integration needs for enterprise clients
- Work closely with cross-functional teams to align backend architecture with overall product strategy and user experience
An Ideal Candidate Should Have
- Proven experience in backend development: TS/Node (required)
- Direct experience with GCP and knowledge of AWS, Azure, or other cloud providers
- Efficiency in ideation and implementation, prioritizing tasks based on urgency and impact
- Preferred: Experience with Docker and containerized deployments
- Preferred: Proficiency in deploying high availability applications on Kubernetes
What We Offer
- A dynamic environment where your contributions shape the company and its products
- A team that values innovation, intuition, and drive
- Autonomy, fostering focus and creativity
- The opportunity to have a significant impact in a revolutionary industry
- Competitive compensation, a welcoming atmosphere, and a commitment to an exceptional asynchronous work culture
- The privilege of working on a product that changes lives, particularly for those with learning differences like dyslexia, ADD, and more
- An active role at the intersection of artificial intelligence and audio – a rapidly evolving tech domain
The United States Based Salary range for this role is: 140,000-200,000 USD/Year + Bonus + Stock depending on experience
Think you’re a good fit for this job?
Tell us more about yourself and why you're interested in the role when you apply.
And don’t forget to include links to your portfolio and LinkedIn.
Not looking but know someone who would make a great fit?
Refer them!
Speechify is committed to a diverse and inclusive workplace.
Speechify does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Ready to apply?
Apply to Speechify
Principal MLOps Engineer
This is a U.S. based position. All of the programs we support require U.S. citizenship to be eligible for employment. All work must be conducted within the continental U.S.
Who we are:
Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.
We’re looking for an experienced Principal ML Ops Engineer to support our customers and join our passionate team of high-impact problem solvers.
About the role:
Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making.
Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.
In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.
What you’ll do:
- Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
- Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
- Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
- Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
- Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
- Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
- Improve observability, reliability, security, and maintainability across ML infrastructure and services
- Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
- Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
- Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
- Get hands-on with customers at the most forward-leaning places in the Department of War
What we are looking for:
- 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
- 5+ years of experience with Docker and Kubernetes in production environments
- 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
- Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
- Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
- Practical experience deploying machine learning workloads on Kubernetes
- Experience managing clusters or workloads that use GPUs
- Strong understanding of Helm and Kubernetes deployment patterns
- Strong scripting or programming skills, preferably in Python
- Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
- Strong troubleshooting, systems thinking, and communication skills
- Ability to work independently and collaboratively in a fast-moving environment
- Ability to obtain and maintain a Top Secret clearance
- Ability to obtain Security+ certification within the first 90 days of employment
Highly preferred:
- Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
- Experience with secure and compliant deployment practices in regulated or government environments
- Experience with Kubernetes-based ML platforms such as Kubeflow
- Familiarity with service mesh technologies such as Istio
- Experience provisioning and debugging complex CI/CD systems
- Experience with infrastructure as code tools such as Terraform
- Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
- Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
- Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
- Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system
What success looks like:
- You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production
- ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use
- Model deployments become more secure, observable, and supportable across real-world mission environments
- The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership
Clearance Requirements:
- Ability to obtain and maintain a Top Secret clearance
Work Type:
- Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY
- May require up to 40% travel
Salary Range: $150,000.00 - $200,000.00
What we will offer you:
- Highly competitive salary
- Fully covered healthcare, dental, and vision coverage
- 401(k) and company match
- Take as you need PTO + 11 paid holidays
- Education & training benefits
- Annual budget for your tech/gadgets needs
- Monthly box of yummy snacks to eat while doing meaningful work
- Remote, hybrid, and flexible work options
- Team off-site in fun places!
- Generous Referral Bonuses
- And More!
Our Vision Statement:
We bridge the gap between humans and data through radical transparency and our obsession with the mission.
Our Customer Obsession:
We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.
How do we get there?
Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm.
Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration.
We’re an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Ready to apply?
Apply to Raft Company WebsitePrincipal MLOps Engineer
This is a U.S. based position. All of the programs we support require U.S. citizenship to be eligible for employment. All work must be conducted within the continental U.S.
Who we are:
Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.
We’re looking for an experienced Principal ML Ops Engineer to support our customers and join our passionate team of high-impact problem solvers.
About the role:
Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making.
Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.
In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.
What you’ll do:
- Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
- Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
- Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
- Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
- Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
- Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
- Improve observability, reliability, security, and maintainability across ML infrastructure and services
- Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
- Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
- Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
- Get hands-on with customers at the most forward-leaning places in the Department of War
What we are looking for:
- 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
- 5+ years of experience with Docker and Kubernetes in production environments
- 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
- Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
- Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
- Practical experience deploying machine learning workloads on Kubernetes
- Experience managing clusters or workloads that use GPUs
- Strong understanding of Helm and Kubernetes deployment patterns
- Strong scripting or programming skills, preferably in Python
- Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
- Strong troubleshooting, systems thinking, and communication skills
- Ability to work independently and collaboratively in a fast-moving environment
- Ability to obtain and maintain a Top Secret clearance
- Ability to obtain Security+ certification within the first 90 days of employment
Highly preferred:
- Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
- Experience with secure and compliant deployment practices in regulated or government environments
- Experience with Kubernetes-based ML platforms such as Kubeflow
- Familiarity with service mesh technologies such as Istio
- Experience provisioning and debugging complex CI/CD systems
- Experience with infrastructure as code tools such as Terraform
- Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
- Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
- Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
- Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system
What success looks like:
- You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production
- ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use
- Model deployments become more secure, observable, and supportable across real-world mission environments
- The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership
Clearance Requirements:
- Ability to obtain and maintain a Top Secret clearance
Work Type:
- Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY
- May require up to 40% travel
Salary Range: $150,000.00 - $200,000.00
What we will offer you:
- Highly competitive salary
- Fully covered healthcare, dental, and vision coverage
- 401(k) and company match
- Take as you need PTO + 11 paid holidays
- Education & training benefits
- Annual budget for your tech/gadgets needs
- Monthly box of yummy snacks to eat while doing meaningful work
- Remote, hybrid, and flexible work options
- Team off-site in fun places!
- Generous Referral Bonuses
- And More!
Our Vision Statement:
We bridge the gap between humans and data through radical transparency and our obsession with the mission.
Our Customer Obsession:
We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.
How do we get there?
Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm.
Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration.
We’re an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Ready to apply?
Apply to Raft Company WebsitePrincipal MLOps Engineer
This is a U.S. based position. All of the programs we support require U.S. citizenship to be eligible for employment. All work must be conducted within the continental U.S.
Who we are:
Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.
We’re looking for an experienced Principal ML Ops Engineer to support our customers and join our passionate team of high-impact problem solvers.
About the role:
Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making.
Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.
In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.
What you’ll do:
- Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
- Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
- Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
- Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
- Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
- Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
- Improve observability, reliability, security, and maintainability across ML infrastructure and services
- Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
- Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
- Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
- Get hands-on with customers at the most forward-leaning places in the Department of War
What we are looking for:
- 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
- 5+ years of experience with Docker and Kubernetes in production environments
- 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
- Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
- Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
- Practical experience deploying machine learning workloads on Kubernetes
- Experience managing clusters or workloads that use GPUs
- Strong understanding of Helm and Kubernetes deployment patterns
- Strong scripting or programming skills, preferably in Python
- Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
- Strong troubleshooting, systems thinking, and communication skills
- Ability to work independently and collaboratively in a fast-moving environment
- Ability to obtain and maintain a Top Secret clearance
- Ability to obtain Security+ certification within the first 90 days of employment
Highly preferred:
- Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
- Experience with secure and compliant deployment practices in regulated or government environments
- Experience with Kubernetes-based ML platforms such as Kubeflow
- Familiarity with service mesh technologies such as Istio
- Experience provisioning and debugging complex CI/CD systems
- Experience with infrastructure as code tools such as Terraform
- Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
- Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
- Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
- Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system
What success looks like:
- You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production
- ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use
- Model deployments become more secure, observable, and supportable across real-world mission environments
- The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership
Clearance Requirements:
- Ability to obtain and maintain a Top Secret clearance
Work Type:
- Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY
- May require up to 40% travel
Salary Range: $150,000.00 - $200,000.00
What we will offer you:
- Highly competitive salary
- Fully covered healthcare, dental, and vision coverage
- 401(k) and company match
- Take as you need PTO + 11 paid holidays
- Education & training benefits
- Annual budget for your tech/gadgets needs
- Monthly box of yummy snacks to eat while doing meaningful work
- Remote, hybrid, and flexible work options
- Team off-site in fun places!
- Generous Referral Bonuses
- And More!
Our Vision Statement:
We bridge the gap between humans and data through radical transparency and our obsession with the mission.
Our Customer Obsession:
We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.
How do we get there?
Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm.
Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration.
We’re an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Ready to apply?
Apply to Raft Company Website
Machine Learning Engineer (Active Secret Clearance)
Build, Deploy, and Maintain AI for an Unpredictable World
Striveworks helps organizations harness the power of artificial intelligence to solve real-world national security and business challenges by serving as the command center between data, models, and business outcomes. Founded by data scientists and engineers, Striveworks set out to make the journey from deployment to ongoing optimization simple and effective.
With Striveworks, organizations aren’t just deploying AI—they’re building systems that remain reliable, adaptable, and ready to scale in an unpredictable world. Mission-critical operations require models that perform where they’re deployed, scale as workloads grow, and adapt rapidly as AI capabilities advance. Striveworks meets these demands, increasing reliability and performance while lowering costs—and enabling confident, data-driven decision-making in dynamic environments.
The Role
As a Machine Learning Engineer at Striveworks, you’ll be challenged—and trusted—on day one to be a core contributor to both the customer-driven projects and the enduring products of the company. You will represent Striveworks as a technology builder on projects and solutions that leverage Chariot, our proprietary AI operations (AIOps) platform, and you will inform and contribute to future capabilities of that platform. You will inform, envision, and help extend Striveworks’ core software products. You will work alongside data scientists, software engineers, and DevOps engineers to transform machine learning models into operational capabilities.
You’re right for this opportunity if you value and possess technical expertise and enjoy pushing the boundaries of your own capabilities. You’re outcome driven and are passionate about applying both software engineering and data science to solve real-world problems. You know that building customer-centric solutions, communicating clearly, and capturing repeatable value into productized capabilities are all important.
Your day-to-day will include:
- Developing machine learning pipelines and custom analytics that are applied to image, video, text, geospatial, time series, and structured data
- Orchestrating and automating complex data engineering and analytic pipelines
- Envisioning, specifying, and, at times, designing and implementing core product functionality
- Conducting mission-critical fieldwork in support of customers and other stakeholders
This position offers a hybrid/on-site work environment at customer sites at Schofield Barracks in Oߵahu, Hawaiߵi. You will be expected to travel up to 30% of the time, including some international travel.
The Right Fit
In addition to the specific skills and expertise detailed below, we are looking for individuals who share our values. Sharing a set of values allows us to move at the speed of trust.
Collectively, we value a high-trust work environment where people respect each other and use candor kindly and constructively. We value work that intersects passion and perseverance, we geek out about the potential of our contributions, and we find joy in working hard on things that matter. Finally, we value taking ownership, having agency, and feeling individual responsibility for collective results.
Here’s what we’re looking for:
- BS degree in computer science, machine learning, or a related discipline and 2+ years of relevant experience
- Experience contributing to data-centric systems (e.g., data engineering, data cleaning, ETL pipelines, machine learning, and other production analytics)
- Proficiency in software engineering fundamentals to include algorithms, data structures, design patterns, and at least one systems programming language (e.g., Go, Rust, C++, Java, Scala, etc.)
- Proficiency in Python and exposure to libraries like TensorFlow, PyTorch, and/or scikit-learn
- Exposure to modern software engineering tools and processes (Agile, version control, issue tracking, CI/CD, debugging, etc.)
- Active Secret (or above) US security clearance
- Due to the nature of this role, candidates must have US citizenship
The Wish List
We’re very interested in candidates who possess the above qualifications, and we appreciate and consider the addition of:
- Advanced degree (e.g., MS, MEng, PhD) in computer science, machine learning, data science, or a related discipline
- Excellence in Python and deep knowledge of libraries like TensorFlow, PyTorch, and/or scikit-learn
- Knowledge of relevant architectures and design patterns for client-server systems (e.g., asynchronous programming, REST, GraphQL, React, Vue, Angular)
- Experience implementing and deploying software into containerized or cloud environments (e.g., Docker, Kubernetes [K8s], cloud architectures)
- Experience with machine learning applied to imagery and/or video data
- Experience building agentic systems, agentic workflows, or AI agents
- Experience defining, scoping, planning, and delivering complex technical solutions
- Experience delivering technology solutions in secure government environments
The anticipated base pay range for this position is $175,000–$205,000/year. Striveworks’ total compensation package includes a competitive base salary, equity grants, and cash bonuses.
The Benefits
- Medical/dental/vision insurance
- Voluntary life, long-term disability, accident, and hospital indemnity insurance
- HSA and FSA (including dependent care FSA) plans
- 401(k) plan
- Unlimited PTO
- Paid parental leave
Check us out on Built In!
Striveworks is an Equal Opportunity Employer and does not discriminate in employment on the basis of race, color, religion, belief, sex (including pregnancy and gender identity or expression), national origin, social or ethnic origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factors. Striveworks will not tolerate discrimination or harassment of any kind.
If you require assistance or a reasonable accommodation in the application process, please contact Operations at hr@striveworks.us.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete an employment eligibility verification form upon hire.
Striveworks is a participating employer in the E-Verify program.
Ready to apply?
Apply to Striveworks
Data Scientist (Active Secret Clearance)
Build, Deploy, and Maintain AI for an Unpredictable World
Striveworks helps organizations harness the power of artificial intelligence to solve real-world national security and business challenges by serving as the command center between data, models, and business outcomes. Founded by data scientists and engineers, Striveworks set out to make the journey from deployment to ongoing optimization simple and effective.
With Striveworks, organizations aren’t just deploying AI—they’re building systems that remain reliable, adaptable, and ready to scale in an unpredictable world. Mission-critical operations require models that perform where they’re deployed, scale as workloads grow, and adapt rapidly as AI capabilities advance. Striveworks meets these demands, increasing reliability and performance while lowering costs—and enabling confident, data-driven decision-making in dynamic environments.
The Role
As a Data Scientist at Striveworks, you’ll be challenged—and trusted—on day one to be a core contributor to the projects, products, and direction of the company. You will represent Striveworks as a technology builder on projects and solutions that leverage Chariot, our proprietary AI operations (AIOps) platform, and you will inform and contribute to future capabilities of that platform. You will work as part of a team of data scientists, machine learning engineers, software engineers, and DevOps engineers to transform machine learning models into functional products.
You’re right for this opportunity if you value and possess technical expertise and enjoy pushing the boundaries of your own capabilities. You’re outcome driven and are passionate about applying both software and data science to solve real-world problems. You know that being customer focused, rigorous in approach, clear in communication, and able to identify repeatable value opportunities are all critical to success. You are able to sense the needs of the customer, identify evolving demands, and then synthesize that feedback into actionable suggestions for Striveworks’ product teams.
Your day-to-day will include:
- Developing and validating machine learning models and custom analytic algorithms that are applied to image, video, text, geospatial, time series, and structured data
- Implementing AI-based software solutions for cloud and edge environments
- Conducting mission-critical fieldwork in support of customers and other stakeholders
This position offers a hybrid/on-site work environment at customer sites at Schofield Barracks in Oߵahu, Hawaiߵi. You will be expected to travel up to 30% of the time, including some international travel.
The Right Fit
In addition to the specific skills and expertise detailed below, we are looking for individuals who share our values. Sharing a set of values allows us to move at the speed of trust.
Collectively, we value a high-trust work environment where people respect each other and use candor kindly and constructively. We value work that intersects passion and perseverance, we geek out about the potential of our contributions, and we find joy in working hard on things that matter. Finally, we value taking ownership, having agency, and feeling individual responsibility for collective results.
Here’s what we’re looking for:
- BS degree in computer science, machine learning, mathematics, or a related discipline and 2+ years of relevant experience
- Proficiency in machine learning and data science, and applying both to image and video data
- Proficiency in implementing and analyzing data structures and algorithms
- Proficiency in programming languages and libraries common to machine learning; excellence in Python is essential, as is knowledge of libraries like TensorFlow, PyTorch, and/or scikit-learn
- Exposure to software development life cycle and tools (e.g., Git, Agile)
- Active Secret (or above) US security clearance
- Due to the nature of this role, candidates must have US citizenship
The Wish List
We’re very interested in candidates who possess the above qualifications, and we appreciate and consider the addition of:
- Advanced degree (e.g., MS, MEng, PhD) in computer science, machine learning, mathematics, or a related discipline
- Experience deploying machine learning and data science, and applying both to production environments
- Exposure to DevOps and Cloud infrastructure (e.g., Docker, Kubernetes, CI/CD, major cloud providers)
- Experience processing a variety of unstructured data types (e.g., imagery, full-motion video, text, acoustic, sonar, RF, geospatial, graphs, or telemetry signals)
- Experience building AI agents and agentic workflows
- Experience implementing ETL pipelines, data pipelines, and/or workflow automation
- Experience developing software in a compiled programming language (e.g., Go, Rust, C++, Java, etc.)
- Experience building full-stack applications (i.e., back end, front end, REST)
- Experience delivering technology solutions in secure government environments
- Experience working with federal, state, and/or local government customers
The anticipated base pay range for this position is $175,000–$205,000/year. Striveworks’ total compensation package includes a competitive base salary, equity grants, and cash bonuses.
The Benefits
- Medical/dental/vision insurance
- Voluntary life, long-term disability, accident, and hospital indemnity insurance
- HSA and FSA (including dependent care FSA) plans
- 401(k) plan
- Unlimited PTO
- Paid parental leave
Check us out on Built In!
Striveworks is an Equal Opportunity Employer and does not discriminate in employment on the basis of race, color, religion, belief, sex (including pregnancy and gender identity or expression), national origin, social or ethnic origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factors. Striveworks will not tolerate discrimination or harassment of any kind.
If you require assistance or a reasonable accommodation in the application process, please contact Operations at hr@striveworks.us.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete an employment eligibility verification form upon hire.
Striveworks is a participating employer in the E-Verify program.
Ready to apply?
Apply to Striveworks
Mission Software Engineer, Public Sector
Scale AI is seeking a highly skilled and motivated Mission Software Engineer to join our dynamic Federal Engineering team. As a part of this team, you will play a critical role in supporting Scale’s government customers by scoping and developing onsite solutions. Our scalable, high-performance platform is the foundation for these customer solutions, and your expertise will be instrumental in designing and implementing systems that can handle interactions with existing customer systems to help our products integrate into existing customer workflows.
You will:
- Work directly with customers to understand their problems and translate those into features in Scale’s platform.
- Be open to >50% travel or relocation to a key customer geographic location.
- Collaborate with cross-functional teams to define and execute the vision for backend solutions, ensuring they meet the unique needs of government agencies operating in secure environments.
- Implement end-to-end data integrations, syncing customer’s data to Scale’s platform and back
- Deploy and maintain Scale software at customer sites
- Develop customer requested features and work closely with them to ensure that they win customer love.
- Build robust and reliable backend systems that can serve as standalone products, empowering customers to accelerate their own AI ambitions.
- Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions.
- This role will require an active TS/SCI security clearance or the ability to obtain a security clearance.
Ideally you have:
- Track record of success as a hybrid customer facing engineer, forward deployed software engineer, and ability to quickly adapt to different roles.
- Prior experience developing with Python and JavaScript, or other modern software languages. Familiarity with Node and React is a plus.
- Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
- Linux experience: Understanding of shell scripting, operating systems, etc
- Networking experience: Understanding of networking technologies, configuration (ports, protocols, etc)
- Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
- Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About Us:
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.
Ready to apply?
Apply to Scale AI