Position Summary: 

Join our mission to infuse cutting-edge AI/ML/GenAI into pharmacy benefits as a Machine Learning Engineer.  We are looking for a software engineer with machine learning expertise to join us in expanding our AI capabilities, enabling increased productivity and magical experiences in our products and services. 

In this role you will be expected to design and implement complex AI systems that leverage ML models for NLP, NLG, multimodal data analysis, chatbots, and RAG-based QnA. The ideal candidate should be passionate about applying AI/ML concepts to difficult problems and develop scalable solutions. We want people who like working in a collaborative team environment and enjoy creating practical, efficient, and high-performance software that leverages Large Language Models (LLM), Multimodal Language Models(MLM), and other ML models and techniques to build amazing capabilities for our customers, partners, and employees. Most importantly, we are a mission-oriented, high-growth startup and we are looking for folks that are excited to be part of our journey to make lasting impact towards transforming healthcare. 

Responsibilities: 

• Develop and productionize machine learning (ML) solutions in the fields of Document understanding, Search and QnA, GenAI, Virtual Agents, etc. 

• Develop and maintain backend services using Python, focusing on AI-driven applications. 

• Design and implement APIs for seamless integration with AI models and services. 

• Develop tools for large-scale data processing and ETL and contribute to extracting insights from data to help guide ML systems development 

• Participate in code reviews, testing, and quality assurance processes. 

• Troubleshoot and resolve technical issues related to AI model, integration, deployment and backend services. 

• Develop algorithms to ensure the integrity and robustness of ML solutions by developing automated testing and validation processes. 

• Document and communicate development processes and implementation details with peers and supervisors 

• Ensure the security and compliance of healthcare data, adhering to HIPAA regulations. 

Required Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related quantitative field. 

• 2+ years experience in the industry with a strong focus on ML solutions development and production deployment is a plus. 

• Good understanding of Machine Learning fundamentals such as Deep Neural Networks, Transformer-based LLM and MLMs, Boosted or standard decision trees, RAG, etc.  

• Strong grasp of OOP, Design Patterns, efficient algorithms, and quality software development. 

• Proficiency in Python and familiarity with ML libraries such as PyTorch. 

• Familiarity with GenAI services such as OpenAI GPT, Anthropic Claude, Google Gemma etc. Some experience in developing and fine-tuning prompts on any of the GenAI services is a plus. 

• Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking. 

• The ability to work collaboratively across multiple disciplines in an extremely fast-paced, startup environment. 

• Good written communication skills that enable collaboration in a remote environment. 

ABOUT THE TEAM

Anduril's Air & Missile Defense Radar team develops cutting-edge tracking algorithms and software systems that detect, track, and characterize airborne threats in real-time. We're building the next generation of tracking intelligence capabilities—automated analysis systems that understand tracking performance, identify failure modes, and continuously improve our algorithms through data-driven insights.

This role sits at the intersection of ML engineering and tracking domain expertise. You'll build end-to-end pipelines that ingest tracking algorithm telemetry, analyze correlation failures and performance anomalies, train models to automate root cause analysis, and deploy production tools that help engineers ask questions like "why didn't track X and track Y associate?" We don't just track targets; we track our tracking systems and make them smarter.

WHAT YOU'LL DO

• Own tracking intelligence infrastructure end-to-end: Build the platform for ingesting tracking algorithm telemetry (hypotheses, scores, gains, association decisions), feature engineering performance metrics, training analysis models, and deploying them into production
• Automate tracking analysis: Develop ML models that identify correlation failures, track quality degradation, and root causes for tracking anomalies—replacing manual deep-dive investigations with scalable automated insights
• Build autotuning capabilities: Create systems that recognize incoming data characteristics and automatically adjust tracking algorithm parameters, frame rates, and model configurations for optimal performance
• Design human-in-the-loop tools: Build interfaces and query services that let engineers ask natural questions about tracking behavior and get data-driven answers backed by your models
• Exploit tracking telemetry: Instrument C++ tracking algorithms with appropriate logging (working with platform engineers), then marshal that data into consistent formats for analysis and model training
• Deploy in constrained environments: Package and deploy models for air-gapped systems with no external connectivity, following security scanning requirements where ML models are treated as data artifacts
• Manage the ML lifecycle: Handle data catalogs, ground truth labeling, model registries, versioning, and validation—ensuring models improve tracking performance in measurable ways
• Bridge domains: Translate between tracking algorithm fundamentals (Kalman filters, data association, multi-hypothesis tracking) and ML/data science techniques to build solutions that actually work
• Drive make/build decisions: Evaluate when to build custom models vs. leverage existing ML capabilities, selecting appropriate algorithm architectures for tracking intelligence problems
• Work hands-on-keyboard: This is a one-person show initially—you'll architect, code, deploy, and iterate rapidly using modern Python-based ML tooling

REQUIRED QUALIFICATIONS

• 3+ years of experience with a strong mix of ML engineering and data science—you've built models AND deployed them into production systems
• Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Experience with MLOps practices: data pipelines, feature engineering, model versioning, experiment tracking, and deployment workflows
• Familiarity with ML infrastructure tooling (MLflow, Dagster/Airflow, or similar orchestration tools)
• Understanding of tracking, estimation, or filtering algorithms (Kalman filters, data association techniques)—you need to understand what tracking algorithms output and why they make the decisions they do
• Ability to work with streaming time-series data and engineer features from algorithm telemetry
• Experience building data catalogs, managing ground truth labels, and validating model performance
• Strong software engineering fundamentals—you can build maintainable, production-quality code independently
• Comfortable working in C++ environments enough to add instrumentation/logging (no deep algorithm development required)
• Ability to obtain and maintain a U.S. Top Secret SCI security clearance

PREFERRED QUALIFICATIONS

• Experience deploying ML models in edge, embedded, or air-gapped environments with security constraints
• Background in defense, aerospace, or sensor systems
• Familiarity with containerization (Docker, Kubernetes) for model serving and deployment
• Experience with anomaly detection, root cause analysis, or automated diagnostics systems
• Knowledge of AutoML, hyperparameter tuning, or online learning techniques
• Understanding of radar systems, sensor fusion, or signal processing
• Experience building conversational or query interfaces for technical systems
• Familiarity with model registries and model-as-data artifact management
• Experience with distributed data processing (Spark, Dask) for large-scale telemetry analysis
• Formal coursework or training in MLOps, data science, or estimation theory
• Active U.S. Top Secret SCI clearance

Tenstorrent is seeking an Physical Design Engineer to lead  cross-functional efforts to solve complex physical design challenges and develop end-to-end RTL-to-GDS methodologies across advanced nodes, with a strong focus on PPA and runtime improvements. The engineer will architect, integrate, and deploy AI/ML-driven solutions into production physical design flows, creating custom CAD tools and partnering with internal teams and EDA vendors to drive next-generation, ML-enabled capabilities. 

This role is hybrid, based out of Santa Clara, CA or Austin, TX or Fort Collins, CO.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who you are

• BS in Electrical or Computer Engineering (or equivalent experience) with 5+ years in Physical Design CAD methodology at advanced nodes.
• Proven track record improving PPA and/or runtime on high-performance, low-power taped-out designs.
• Hands-on with industry-standard EDA tools (e.g., Fusion Compiler) across synthesis, P&R, STA, signoff, and hierarchical flows.
• Strong Python/Tcl and data skills, with interest or experience in ML frameworks (PyTorch, TensorFlow), and the ability to drive complex projects independently.

What we need

• Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes.
• Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, P&R, STA, signoff, and assembly.
• Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and QoR.
• Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, in close collaboration with verification, extraction, timing, DFT, and EDA vendors.

What you will learn

• How to scale AI/ML-driven methodologies across diverse products and advanced technology nodes in real production flows.
• New ways to blend classical EDA algorithms with modern ML techniques to push PPA and runtime limits.
• Best practices for deploying, validating, and monitoring ML models in production CAD environments.
• How to influence next-generation ML-enabled EDA tools and collaborate deeply with cross-functional teams (PV, extraction, timing, DFT).

Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.

This position requires access to technology that requires a U.S. export license for persons whose most recent country of citizenship or permanent residence is a U.S. EAR Country Groups D:1, E1, or E2 country. 

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

What you will do:

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

We are looking for people who have:

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

Nice to have:

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

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

Bonus Points:

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

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

As a Hunter Solutions Architect on the Digital Natives team, you will shape the future of the big data landscape by working with the most sophisticated data engineering and data science teams in the world.

Reporting to the Field Engineering Manager, you will collaborate with customers, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide customers through the competitive landscape, best practices, and implementation; and develop technical champions along the way.

The impact you will have:

• You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
• Work directly with prospective, greenfield, customers on their 0 to 1 journey with Databricks 
• Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
• Collaborate with your fellow Solutions Architects, using your skills to support each other and our users
• Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.

What we look for:

• 5+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
• Experience building distributed data systems at scale 
• Comfortable programming in, and debugging, Python and SQL
• Experience using agentic coding harnesses and demonstrable experience scaling your work leveraging them 
• Have built solutions with public cloud providers such as AWS, Azure, or GCP
• Expertise in one of the following:
• Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
• Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
• Available to travel to customers in your region
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Nice to have: Databricks Certification

NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply.  We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.

While candidates in the listed location(s) are encouraged for this role, candidates in other locations will be considered. #LI-REMOTE

As a Solutions Architect on the Digital Natives team, you will shape the future of the big data landscape by working with the most sophisticated data engineering and data science teams in the world.

Reporting to the Field Engineering Manager, you will collaborate with customer stakeholders, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide one of our largest AI native customers through the competitive landscape, best practices, and implementation; and develop technical champions along the way.

The impact you will have:

• You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
• Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
• Collaborate with your fellow Solutions Architects, using your skills to support each other and our users
• Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.

What we look for:

• 7+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
• Experience building distributed data systems
• Comfortable programming in, and debugging, Python and SQL
• Have built solutions with public cloud providers such as AWS, Azure, or GCP
• Expertise in one of the following:
  • Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
  • Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
• Available to travel to customers in your region
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Nice to have: Databricks Certification

NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply.  We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.

About the Opportunity

We are seeking a Senior Software Engineer to design, build, deploy, monitor, and optimize production-ready ML services in regulated healthcare. You will work hands-on to package, test, orchestrate, deploy, and maintain ML models, improve workflows, and implement CI/CD and automated testing to ensure reliability, performance, and faster delivery of business value.

Responsibilities

• Design, build, and deploy scalable AI/ML services with clear service boundaries, making pragmatic trade-offs across performance, maintainability, security, and long-term extensibility.

• Own full lifecycle delivery of complex features from architectural planning and API design to implementation and post-release observability.

• Define and enforce robust verification and validation for AI services including unit, integration, and end-to-end testing to ensure reliability of code in production and compliance with our Quality Management System and regulatory standards. 

• Integrate CI/CD and MLOps practices for automated model builds, testing, and deployment.

• Translate product requirements into well-defined technical designs proactively shaping implementation details.

• Contribute to the necessary technical documentation required for regulatory submissions.

• Collaborate cross functionally to reduce knowledge silos and ensure continuity in critical systems through cross-training and documentation. 

Requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field

• 7+ years of software engineering experience, with expertise in AI production systems (Python, PyTorch) and data services (SQL, Postgres, NoSQL, Redis or similar).

• Experience with unit, integration testing for ML models, including data validation, correctness checks, and reproducibility.

• Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms(SageMaker, Databricks, Unity Catalog, Snowflake/Snowpark) to build scalable ML data pipelines and model workflows.

• Strong collaboration skills to work effectively with ML scientists, engineers, and regulatory teams.

• Comfortable operating in ambiguity and driving clarity across product, engineering, and business stakeholders.

This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges:

• Base Salary: $153,000 - $179,000
• TTC: $176,000 - $206,000

*Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.

About Sovrn

Every interesting company solves important problems for other people.  Sovrn is a Software and Data business that helps Open Web businesses be and remain independent.  We help them understand their business better, operate more efficiently, and make & keep more money.

• We believe in the freedom and free-flow of information.
• We believe the Open Web is the largest source of this information.
• We believe in helping Open Web businesses be and remain Independent.

Through Software products and Data solutions we help our customers:

• Understand their business better, so they can make better decisions
• Operate their business more efficiently, so they can invest in what matters most
• Make (and Keep) more money, so they control their own destiny

About the Role

The Senior AI/ML Data Scientist at Sovrn is a strategic technical leader who drives high-impact data initiatives across the organization. This role requires a unique blend of strategic vision ("the art of the possible") and tactical execution ("think and execute tactically"). You will solve ambiguous problems to deliver measurable business value and influence the future of data science in AdTech. You will guide architecture, experimentation, and product design while ensuring scientific and technical rigor. This position requires technical excellence, business acumen, and leadership.

What you’ll be doing:

• Operate at the frontier of applied AI as a hands-on practitioner — personally exploring, prototyping, evaluating, and deploying cutting-edge Agentic AI technologies to bring new, grounded insight into product, modeling, and optimization decisions. 
• Design, implement, and deploy advanced predictive models, algorithms, and experiments that drive core business outcomes, specifically optimizing ad performance, user engagement, and monetization strategies.
• Analyze large-scale datasets to identify patterns, trends, and optimization opportunities, while maintaining and improving data pipelines and experimentation frameworks to support ongoing research.
• Define and evangelize best practices in modeling, testing, measurement, and data governance, ensuring scientific rigor while balancing practical business constraints and timelines.
• Collaborate cross-functionally to identify high-leverage AI/ML opportunities, translate them into actionable roadmaps, and deliver measurable outcomes.
• Mentor other data scientists through code review, experiment design, and technical feedback and model what it looks like to stay current with fast-moving AI research in a production-focused environment. 
• Translate complex data problems into strategic decisions through compelling storytelling and clear communication of findings and recommendations to both technical and non-technical stakeholders.

A successful candidate will have:

• Proven ability to deploy autonomous agents to production including optimizing inference performance, managing latency/cost tradeoffs, and ensuring robust model evaluation at each stage.
• Demonstrates prompt engineering discipline by systematically evaluating prompts across model families using quantitative evaluation.
• Active use of AI tools in daily technical work — integrates coding assistants, orchestration frameworks, and search-augmented workflows in ways that accelerate prototyping and reduce iteration cycles.
• Experience in data science and applied machine learning, with a strong record of delivering business impact.
• Deep technical proficiency across classical ML (supervised/unsupervised), reinforcement learning, and deep learning, with a strong foundation in statistical modeling, A/B testing, and causal inference.
• Proficiency with Python, SQL, cloud-based data ecosystems, modern AI/ML frameworks (e.g., Pandas, Scikit-Learn, PyTorch/TensorFlow, LangChain, Hugging Face, PydanticAI), experience with large-scale datasets and distributed computing frameworks (e.g., Spark, PySpark), and Databricks running on AWS Cloud.
• Strong understanding of ad tech ecosystems (e.g., RTB, DSP/SSP workflows, auctions, and yield optimization) is a big plus.
• Demonstrated ability to influence across technical and non-technical audiences, lead cross-functional initiatives, and work in a collaborative, agile environment (Scrum and Kanban).
• A strategic mindset with strong product intuition and experience working at scale.

Location: This position is based in our Boulder, Colorado office. For exceptional candidates we would consider remote locations. Applicants need to be able to commute/travel to our Boulder location.

We understand that no candidate is perfectly qualified for any job. Experience comes in different forms; many skills are transferable; and passion goes a long way. Even more important than your resume is a clear demonstration of accountability and the ability to thrive in a fluid and collaborative environment. We expect you to learn new things in this role and encourage you to apply if your experience is close to what we're looking for.

Sovrn Core Values: Candid, Customer Empathy, Learning, Scrappy, Second Order Thinking

Compensation and Benefits

The base salary for this position in Boulder, Colorado is $165,000 to $195,000 annually. In addition to salary, the total compensation package includes bonus, equity, and benefits. Actual earnings may vary depending on the candidate's direct experience, skills, knowledge, and location.

Sovrn offers a full slate of benefits from competitive salaries, stock options, medical, dental, and vision coverage, short and long-term disability, life insurance, 11 paid holidays, flexible vacation, commuter benefits, a 401(k) plan and match, and a paid parental leave program.

Job Posting

Applications will be accepted until May 30, 2026.

Equal Opportunity Employer 

Sovrn is proud to be an Equal Opportunity Employer and provides equal employment opportunities to all employees and applicants regardless of race, color, religion, gender, gender identity, age, national origin, disability, parental or pregnancy status, marriage and civil partnership, sexual orientation, veteran status, or any other characteristic protected by law. Reasonable accommodations will be made to meet the requirements of the Americans with Disabilities Act and will be provided as requested by candidates taking part in all aspects of the selection process. 

Recruitment Agencies

Sovrn does not accept agency resumes. Please do not forward resumes to our jobs alias or Sovrn employees. Sovrn is not responsible for any fees related to unsolicited resumes.

While candidates in the listed location(s) are encouraged for this role, candidates in other US locations will be considered. #LI-REMOTE

As a Solutions Architect on the Digital Natives team, you will work with the leading data engineering, data science & ML teams in the market to push the boundaries of what big data architectures are capable of. 

Reporting to the Field Engineering Manager, you will collaborate with strategic customers, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide customers through the competitive landscape, best practices, and implementation; and develop technical champions along the way.

We are looking for high technical aptitude individuals with a deep sense of ownership and a desire to help customers ship solutions at production scale. Ideal candidates are deeply curious, capable of operating with confidence in ambiguous situations, and are extremely adaptable. 

The impact you will have:

• You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
• Drive technical discovery and solution design, focusing on winning competitive deals and accelerating time-to-value in strategic accounts.
• Continuously research & learn new technologies and their implementations on Databricks
• Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
• Collaborate with your fellow Solutions Architects, using your skills to support each other and our customers
• Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.
• Work closely with account executives to create and execute account penetration strategies, focusing on winning technical decision-makers and building new customer champions.
• Build trusted advisor relationships with senior and executive stakeholders by articulating the business value of Databricks in clear, outcomes-driven terms.
  
  

What we look for:

• 5+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
• Experience building distributed data systems
• Comfortable programming in, and debugging, Python and SQL
• Have built solutions with public cloud providers such as AWS, Azure, or GCP
• Expertise in one of the following:
• Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
• Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
• Strong executive presence with the ability to influence C/VP-level stakeholders and align technical solutions to strategic business priorities.
• Available to travel to customers in your region
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Nice to have: Databricks Certification
  
  

NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply. We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.

About your role

Rocket Lawyer is seeking an experienced, passionate AI/ML software engineer for Rocket Copilot, our new AI assistant launched in December 2023. In this role, you will help our team deeply embed AI features across our platform. You will work in an agile, cross-functional team, working with multiple product teams as we build out AI features across Rocket Lawyer. You will design, implement, test, release and maintain new AI features, working closely with product managers, UX designers, data engineers, QA engineers, and others.

We value a fun, collaborative, team-oriented work environment, where we celebrate our accomplishments. 

How you will make a difference day to day

• Build, train, fine-tune, deploy and maintain ML models for AI features
• Create, monitor and troubleshoot backend API services to allow Rocket Lawyer product frontends to interact with ML models
• Work closely with multiple product teams and cross-functional teammates to quickly integrate AI into existing and new Rocket Lawyer products
• Keep up-to-date with industry trends around generative AI and machine learning
• Rapidly prototype product features to explore new AI capabilities, while writing clean, understandable code

What you’ll need

• 5+ years of Experience writing ML code for launched products
• Understanding of the state of the art in generative AI, including RAG, LoRA, RLHF and others
• Experience creating data sets from proprietary anonymized data, and using them to train and fine-tune ML models
• Experience with machine learning frameworks like TensorFlow, PyTorch and Scikit-Learn
• Strong programming skills in Python and SQL
• BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields, or related work experience

Benefits & Perks

• Comprehensive health plans (including Medical, Dental and Vision insurance for full-time employees)
• Unlimited PTO
• Competitive salary packages
• Life insurance
• Disability benefits
• Supplemental Optional Life Insurance Benefits
• FSA Options Optional
• HSA with Company Match
• 401k program with Company Match
• Fertility Assistance and Planning options
• Wellhub & ClassPass fitness platforms
• Comprehensive Pet Insurance options
• Financial Wellbeing & Student Loan Program access
• Access to additional Mental Health & Wellbeing resources
• Pre-tax Commuter/Transit Benefits
• Free Rocket Lawyer account with online access to an extensive legal documents library and brilliant licensed attorneys at discounted rates 

Interview Process:

• Recruiter Phone Screen
• Role Assessment(s)
• Hiring Manager Interview
• Panel Interviews
• Final Interview

Dark Wolf is looking for summer interns to join us at our offices in Herndon, VA, Colorado Springs, CO, Omaha, NE or Tampa, FL. Outside of the office, you’ll join a community of interns with access to local networking events, professional development workshops, and the vibrant culture of Dark Wolf, spanning multiple areas. Software Development, Google Cloud Delivery, Data&AI, Cybersecurity and Mission Enablement.

Duration: June 2026 – August 2026

Type: Full-Time Internship (Paid)

Software Delivery and Cloud Internship: 

As an intern in our NoVA technology hub, you will work at the intersection of modern software delivery and emerging intelligence. This role is designed for students who are passionate about building scalable applications, leveraging cloud infrastructure, and implementing data-driven AI solutions. You won't just be shadowing; you will be an active member of an Agile squad contributing to production-level code.

Key Responsibilities

• Software Development: Design, develop, and test features using languages such as Python, Java, or Node.js

• Cloud & Delivery: Assist in deploying applications to cloud environments (AWS, Azure, or GCP) and managing CI/CD pipelines to automate software delivery.

• Data Engineering: Build and optimize data pipelines to ensure high-quality data is available for analysis and model training.

• AI & Machine Learning: Support the integration of Generative AI and LLMs into existing workflows, performing prompt engineering and model fine-tuning.

• Agile Collaboration: Participate in daily stand-ups, sprint planning, and retrospectives to experience the full Software Development Life Cycle (SDLC).

Required Skills & Qualifications

• Current Enrollment: Pursuing a Bachelor’s or Master’s degree in Computer Science, Data Science, Computer Engineering, or a related technical field at the University of Florida.

• Technical Proficiency: Foundational knowledge in at least one major programming language (Python preferred for AI/Data tracks).

• Problem Solving: Strong analytical skills and a "Go Gator" attitude toward tackling complex technical challenges.

• Communication: Ability to clearly communicate technical concepts to both technical and non-technical team members.

Preferred Experience

• Familiarity with containerization tools like Docker or Kubernetes.

• Knowledge of SQL and NoSQL databases.

• Exposure to AI frameworks such as PyTorch, TensorFlow, or LangChain.

• Previous project work involving cloud-native architecture.

Cybersecurity Academy Internship: 

Are you ready to go beyond the textbook ?

The Dark Wolf Cyber Academy (DWCA) internship is an immersive program designed to turn aspiring students into cybersecurity professionals. Unlike standard internships, we challenge you to navigate the complexities of cybersecurity through three critical lenses: Risk Management, Penetration Testing, and Cyber Engineering. This internship is built for people who want to grow fast.

The DWCA Internship is designed to be cyber intensive where you won’t just talk about security—you’ll build it. This program is structured into dedicated 2-3 week blocks where you work alongside professionals and are exposed to real-world tools and frameworks. From breaking into systems to building resilient cloud architectures, you will gain hands-on experience that bridges the gap between theory and implementation. This internship is designed to help you figure out your path—and give you experience to back it up.

Professional Sprints:

• Pentesting & Offensive Security: Adopt an adversarial perspective to identify and exploit critical vulnerabilities.
• Cyber Engineering: Move beyond policy to practical application by learning to architect and secure cloud environments.
• Risk Management & Compliance: Familiarize yourself with the components of authorization packages through hands-on development activities.
• AI Education & Emerging Tech: Explore the cutting edge of security by analyzing the impact of emerging technologies on modern cyber engineering.

Why DWCA?

• Real-World Impact: Work on a simulation that mirrors industry challenges
• Mentorship: Learn directly from professionals who serve as your dedicated day-to-day coaches
• Career Launchpad: Gain the practical, employable skills that prepare you to stand out in a competitive market

Desired Majors

• Cybersecurity
• Information Security
• Computer Science
• Information Technology
• Information Systems

Desired Skillsets

• Willingness to learn and apply knowledge
• Comfortable communicating and collaborating with teammates and leadership
• Strong problem-solving and analytical skills with attention to detail
• Basic understanding of how cybersecurity protects systems and data (threats, risks, and why security matters)

Google Cloud Delivery Internship: 

Help us build and extend our Secure Cloud Landing Zone (SCLZ) codebase which automates the entire process of fielding an authorized cloud solution from cloud org -> app. Our SCLZ allows DoD & National Security customers to scale their missions securely and efficiently using our IL5 and FedRAMP High P-ATO’d landing zone architecture which provides IAM Best Practices, Tenant Isolation, Shared Services, and monitoring through Infrastructure-as-Code (IAC). In this internship you will work across the core SCLZ codebase and build cloud-native solutions running on top of the SCLZ within Google Cloud in support of Defense and National Security mission use cases.

Skillsets: 

Computer Science, Computer Engineering Majors

• Application Development
• Cloud Infrastructure Engineering
• Developing Zero Trust Cloud Native Apps/Solutions on Google Cloud

Cybersecurity

• Cloud GRC
• Cloud Native Policy as Code
• SecOps

We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.

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 the required employment eligibility verification form upon hire.

About the Opportunity

We're seeking a Senior Software Engineer to drive key technical initiatives across our computational imaging pipeline. In this high-impact role, you'll own the complex systems responsible for deploying, scaling, and monitoring our regulated AI algorithms end-to-end. You will influence the architecture of our system, ensuring our solutions meet medical standards for quality and performance, while mentoring other engineers and helping shape our technical culture.

About the Team

You will join our expert Computational Imaging team, where we develop sophisticated algorithms to enhance and improve medical imaging. We are focused on improving the quality and clinical value of medical images, creating powerful, extra software components that correct artifacts, standardize data, and ensure the utmost precision before the final analysis. Our work is essential for elevating the reliability and accuracy of Cleerly’s regulated products.

Responsibilities

• Design, build, and deploy scalable AI services and computational imaging pipelines to production, ensuring robust, high-availability infrastructure for our machine learning algorithms.
• Design extensible system architectures and make pragmatic trade-offs that balance performance, security, and maintainability.
• Own full lifecycle delivery of complex features, from architectural planning and API design to implementation and post-release observability.
• Ensure high availability and observability of our services; improve alerting, logging, and incident response across the stack.
• Set and uphold strong engineering standards via code reviews, technical mentorship, and design documentation.
• Lead technical design reviews and system decomposition efforts across teams.
• Proactively identify risks and gaps in operational or architectural resilience, and drive durable improvements.
• Collaborate closely across AI and engineering to reduce knowledge silos and ensure continuity in critical systems through cross-training and documentation.
• Design and implement robust testing mechanism and validation strategies to ensure compliance with our Quality Management System and regulatory standards 
• Contribute to the necessary technical documentation required for regulatory submissions.

Requirements 

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field (or equivalent experience).
• 8–12 years of software engineering experience, with expertise in AI production systems (Python, PyTorch) and data services (SQL, Postgres, NoSQL, Redis or similar).
• Demonstrable capability in designing, implementing, and securing RESTful web services
• Experience in MLOps orchestration and cloud deployment (Docker, Kubernetes).
• Experience with AWS, GitHub, and continuous integration pipelines
• Proven track record of architecting and delivering scalable systems in production environments.
• Experience mentoring engineers and raising the technical bar through reviews and design feedback.
• Strong systems thinking with the ability to evaluate tradeoffs in scalability, reliability, and maintainability.
• Experience designing for observability and operational excellence (e.g., logs, alerts, dashboards, runbooks).
• Comfortable operating in ambiguity and driving clarity across product, engineering, and business stakeholders.

Preferred:

• Experience designing and optimizing end-to-end medical imaging pipelines in a production environment and familiarity with HIPAA/HITRUST security requirements.
• Hands-on experience with Computer Vision and Deep Learning techniques used for medical image processing.

The base salary range for this role varies by location and is aligned to market benchmarks.

• Candidates located in higher-cost labor markets, including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range.
• Final compensation is determined based on location, experience, skills, and internal equity.

This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges:

• Base Salary: $153,000 - $179,000
• TTC: $176,000 - $206,000

*Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.