About the Role

As a Technical Deployment Lead on the Claude Agentic Solutions team, you will lead the delivery of custom AI agent solutions for enterprise customers in highly regulated industries. You'll own high-value engagements where we collaborate directly with customers to build and deploy agents into their most critical business processes.

This is a founding team: you will help us to build technical playbooks and define the processes and repeatable patterns needed for us to scale this emerging motion. You will champion our mission in the field, ensure world class delivery, and bring insights back to our product and research teams on a regular basis.

You'll own engagements end-to-end, from SOW through production deployment. You'll work alongside Forward Deployed Engineers who build the technical solution, while you own product scoping, stakeholder management, value measurement, and the organizational complexity that comes with deploying AI agents in enterprise environments. You need to be technical enough to hold architecture conversations with engineering stakeholders and polished enough to run executive briefings with C-suite sponsors.

Responsibilities

• Own the technical delivery plan for each engagement. Structure SOWs with clear scope, milestones, dependencies, success criteria, and value hypotheses. Translate customer business objectives into a sequenced roadmap that FDEs execute against.
• Lead technical discovery. Map customer workflows, identify constraints, define MVP scope, and shape the solution architecture for custom agent deployments.
• Run day-to-day engineering execution. Drive delivery across Anthropic and customer teams. Keep progress unblocked and sequenced. Make real-time trade-offs on scope and priority to protect the critical path.
• Own product scoping for field engagements. Define the MVP, author requirements documentation, prioritize the engineering backlog, and manage scope against success criteria as requirements evolve.
• Own the customer relationship throughout delivery. Lead executive briefings, manage stakeholder communications across technical leads and procurement, and represent Anthropic's technical credibility with senior business and engineering leaders.
• Own value measurement and ROI. Define impact hypotheses, set baselines and KPIs, run pre- and post-deployment measurement, and report outcomes to executive sponsors.
• Codify reusable delivery assets. Build solution patterns, evaluation frameworks, and technical playbooks. Extract what works across engagements and feed field signals back to Product and Research to improve our platform and models.
• Navigate enterprise and regulatory complexity. Security reviews, legal approvals, procurement processes, compliance requirements, and organizational dynamics.
• Manage scope and change. Handle evolving requirements, set expectations, negotiate contract modifications, identify risks early, and escalate with clear context when needed.
• Run delivery operations. Sprint ceremonies, milestone reviews, and progress reporting.
• Travel to customer sites. Build relationships, unblock delivery, and accelerate adoption. (25–50% expected).

You May Be a Good Fit If You

• Have led AI/ML engagements/deployments, whether as a founder, data scientist, engineer, researcher, or in a professional services or consulting role.
• Have delivered AI, ML, or LLM-based agentic solutions into production. You understand solution patterns, integration approaches, and what breaks in real environments.
• Have experience in a specialized vertical (financial services, life sciences, pharmaceutical, retail, mining, agriculture, etc.).
• Can lead architecture discussions with engineering stakeholders, evaluate technical trade-offs, and pressure-test technical decisions. You won't write production code, but you will own the technical direction of engagements alongside FDEs.
• Have a track record delivering complex, high-stakes technical projects for enterprise clients where outcomes depended on tight coordination and fast decision-making — ideally across multiple workstreams in regulated industries.
• Have executive presence — polished, credible, and comfortable representing Anthropic to senior leaders in high-stakes environments.
• Thrive in ambiguity and bring structure where none exists.
• Have a builder's mindset — you're here to create a function, not join one.

About Avride

Avride is a US-based developer of autonomous vehicles and delivery robots. We develop and operate both autonomous cars and delivery robots that share technologies and mutually benefit from each other's advancements—a unique approach in the industry. 

About the Internship

At Avride, ML Engineer Interns operate at the intersection of cutting-edge academic research and real-world engineering. You will use our massive datasets of real driving logs to train models and develop algorithms.

During this internship, you will be embedded in our Perception team. The Perception team serves as the eyes and ears of our autonomous vehicles, transforming raw data from cameras, LiDAR, and microphones into a precise, real-time 3D understanding of the surrounding world. 

You will be paired with a dedicated senior mentor and work on problems directly impacting real-world driving performance. This program is designed to give you a deep understanding of how to take a theoretical concept or novel system architecture, prototype it, and evaluate its performance within a complex, safety-critical stack.

What You’ll Do

We are currently offering four different internships within our Perception Team for the Summer of 2026. 

Long-Tail 3D Entity Recognition via Pre-Trained 2D Models

• Targeted ML Investigation: Take charge of solving a classic autonomous driving challenge: long-tail entity recognition. You will research how to leverage the broad visual knowledge of pre-trained, open-source 2D models for 3D applications.
• Simulation-Driven Evaluation: Design and run rigorous experiments in our simulation environment to prove your models can detect rare, infrequent objects without sacrificing precision.
• Feature Integration: Work closely with your mentor to prototype and iterate on techniques that adapt these 2D features into our current perception stack.
• Knowledge Sharing: Conclude your internship by sharing your experimental findings, recall/precision trade-offs, and simulation methodology with the research and engineering groups.

RGB-Only 3D Perception & RGB-LiDAR Fusion

• Applied Research Ownership: Lead a scoped research initiative to advance our 3D perception capabilities. You will dive into state-of-the-art literature on RGB-only methods and formulate hypotheses to improve sensor fusion.
• Model Training & Experimentation: Utilize Avride’s extensive real-world LiDAR and camera datasets to train, test, and evaluate ML models using PyTorch, aiming to extract stronger, more reliable signals from RGB data.
• Iterative Prototyping: Partner with your mentor to design and refine algorithms that directly enhance our existing perception baselines.
• Knowledge Sharing: Present your methodology, fusion results, and future recommendations to the broader engineering and research teams at the end of your term.

Data Engineering - Visual Scene Search via Vector Embeddings

• System Architecture & Design: Own the development of a new vector-based search capability to upgrade how we query our scene database. You will research and integrate embedding models (like CLIP) alongside our existing natural language systems.
• Data Tooling Implementation: Build out the backend infrastructure using Python to map and search Avride's massive library of real-world camera data.
• Pipeline Integration: Collaborate with your mentor to deploy these embedding models effectively, unlocking faster and smarter data mining for our labeling and perception teams.
• Knowledge Sharing: Present your system architecture, search performance metrics, and the practical impact of your new tool to the wider engineering organization.

Audio Signal Processing & Siren Recognition Pipeline

• End-to-End Pipeline Creation: Lead an applied engineering project centered on our vehicle microphone arrays. You will design and build a robust data mining pipeline to extract relevant audio signals from raw vehicle logs.
• Auto-Labeling & Fine-Tuning: Leverage large open-source models to automatically label your mined data, then use that dataset to train and fine-tune a compact, efficient onboard ML model for siren recognition.
• Edge Optimization: Partner with your mentor to iterate on the model's performance, ensuring it is highly accurate and lightweight enough for real-time onboard processing.
• Knowledge Sharing: Wrap up your internship by demoing your automated labeling pipeline and the performance of your onboard siren detector to the engineering teams.

What You’ll Need

• Education: Currently pursuing a Bachelor's, Master’s, or PhD (highly preferred) in Computer Science, Robotics, Machine Learning, Applied Mathematics, or a related field with an expected graduation date between Winter 2026 and Spring 2027. 
• Machine Learning / Math Foundation: Strong understanding of deep learning, reinforcement learning, computer vision, optimization, or probabilistic modeling.
• Programming Skills: Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow). Basic familiarity or willingness to learn C++.
• Research Acumen: Ability to read, understand, and implement algorithms from academic research papers. A strong analytical mindset for designing experiments and interpreting data.
• Eagerness to Learn: Highly collaborative, open to feedback, and excited to tackle unsolved problems in the autonomous driving space.

What You’ll Get

• 1:1 Mentorship: Direct guidance from leading researchers and engineers in the autonomous vehicle industry to help you navigate technical roadblocks and grow your career.
• Massive Compute & Data: Access to state-of-the-art driving data to fuel your experiments.
• Networking & Culture: Invitations to tech talks, paper reading groups, intern social events, and cross-team collaborations.

Please note that this is an in-person internship based at our office in Austin, Texas.  We are prioritizing candidates who currently reside within commuting distance of Austin.  We do not provide relocation assistance, travel reimbursement, or housing stipends for this position.

About team

Avride is at the forefront of autonomous mobility, developing and deploying state-of-the-art self-driving cars and delivery robots. We’re shaping the future of transportation and logistics—and our Labeling Team plays a vital role in bringing that vision to life.

The Labeling Backend Team builds the data infrastructure that powers groundbreaking research and development across our labeling pipelines, data preparation workflows, and model training processes. The high-quality labeled data we deliver is critical to advancing our core technologies and supports the diverse range of models that drive our entire business.

About the Role

We are looking for a Research Engineer to improve the quality and representativeness of datasets powering our self-driving systems. You will design algorithms and tools for auto-labeling, data mining and dataset monitoring, combining strong Python engineering with applied ML concepts. Your work will directly enhance data efficiency, reduce labeling costs, and improve model performance.

What You'll Do

• Design and implement algorithms that optimize annotation, including auto-labeling systems that reduce manual effort and increase throughput
• Build data-mining and active-learning pipelines to surface the highest-value samples for training
• Create dataset-quality monitoring systems identifying noise, redundancy, and low-value data
• Develop analytics platforms (databases, dashboards, reporting) to track dataset quality and coverage over time
• Collaborate with ML and Perception teams to integrate research results into production workflows
• Explore emerging approaches (vision-language models, weak supervision, uncertainty estimation) to expand dataset quality and automation

What You’ll Need

• Bachelor’s or Master’s degree in Computer Science or related field
• Strong Python skills for algorithm development and prototyping
• Solid understanding of ML concepts (metrics, evaluation, dataset sampling, etc.)
• Experience with data processing and analysis at scale
• Ability to move between research prototyping and production engineering
• Strong analytical mindset and curiosity to dig deep into data quality problems

Nice to Have

• Experience with auto-labeling, weak supervision, or human-in-the-loop ML
• Exposure to 3D data (point clouds, sensor fusion, 3D annotation pipelines)
• Background in AV, robotics, or large-scale ML dataset development
• Experience with foundation models / VLMs
• Workflow orchestration systems (Argo, Airflow, etc.)
• Backend engineering experience (APIs, ORMs, databases)
• Experience building dashboards or analytics systems (Grafana, Superset, etc.)

Allen Control Systems (ACS) is a cutting-edge defense startup founded by two ex-Navy electrical engineers with a proven track record in robotics and software. We are developing a small, autonomous gun turret that employs advanced computer vision and control systems to precisely target and neutralize small drones and loitering munitions. Our innovative approach requires overcoming significant technical challenges, making this an exciting and dynamic environment for experienced engineers. 

With an engineering-first culture, ACS values technical excellence and continuous learning. Backed by our founders' successful exits from two previous ventures acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop have a real-world impact. 

Position Overview:  

We are seeking a Data Platform Engineer who combines expert-level data infrastructure skills with a strong knowledge of AI & Machine Learning principles. In this role, you will go beyond simple data validation scripts; you will apply your understanding of model training dynamics to design and implement existing and novel approaches to optimize our datasets. 

You will build and maintain large-scale image and video pipelines, but with a focus on data curation strategies—such as coreset selection, embedding-based filtering, and automated complexity scoring. You’ll partner closely with our ML engineers to orchestrate ingestion, synthetic data generation, and versioned releases, ensuring that every dataset is not only high-integrity and available but strictly optimized to maximize model performance. 

What You’ll Do: 

• Design and develop a scalable data infastructure, focusing on organization and curation to support continuing increases in data volume and complexity
• Design and implement existing and novel approaches to optimize datasets for model training (e.g., hard example mining, class balancing, de-duplication, embedded-based filtering).
• Support the data infrastructure required for optimal ingestion, transformation, and storing of datasets
• Develop and use synthetic data generation workflows to create realistic synthetic training data for computer vision models.
• Design and own end-to-end image and video pipelines for computer vision model training: multi-source ingestion, QA and visualization, standardization, and organization.
• Coordinate collection of real-world data; coordinate label creation and QA with labelers.
• Develop and use data quality tooling: metrics for balance, drift, and annotation error; active-learning sampling to target gaps; feedback loops from production back to curation. 

• Implement and own dataset versioning, release management, and lineage and metadata cataloging.

What You’ll Need: 

• 3+ years of experience in data engineering or equivalent fields. 
• Solid understanding of data structures and systems design for orchestrating data-related workflows in a rapidly growing environment.
• Proficient in using AWS for data management and processing.
• Proficient in Python for scripting and data processing; proficient with SQL and Linux.
• Educational Background: Bachelor’s or Master’s degree in Computer Science or a related field.
• Proven ability to communicate well across engineering teams, and write and maintain effective documentation. 

You’ll Stand Out: 

• 5+ years of industry experience.
• Experience in image/video data engineering for computer vision projects.
• Experience with PyTorch DeepCore.
• Experience with Unreal Engine. 

What We Offer: 

• Competitive salary
• ACS Equity Package
• Health, Dental, Vision Insurance
• Paid Time Off 

Allen Control Systems is an Equal Opportunity Employer, providing equal employment opportunities to all employees and applicants for employment. Allen Control Systems prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.