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About the Role

We are looking for a Data Analytics Engineer who sits at the intersection of data engineering and analytics. In this role, you will transform raw, messy data from vehicles, APIs, and operational systems into clean, reliable datasets that are trusted and widely used-from engineering teams to executive leadership.

You will own data pipelines end to end and build dashboards that surface insights, track performance, and help teams quickly identify issues.

What You’ll Do

• Build and maintain ETL pipelines that ingest data from diverse internal systems into a centralized analytics warehouse
• Work with unique and high-volume datasets, including vehicle telemetry, sensor-derived signals, logistics data, and system test results
• Write efficient, well-structured SQL to model and prepare data for analysis and reporting
• Design, build, and maintain dashboards (e.g., Grafana or similar) used to monitor system performance and operational health
• Partner closely with engineering, operations, and leadership teams to understand data needs and deliver actionable datasets
• Explore internal AI- and LLM-based tools to automate analysis and uncover new insights

What You’ll Need

• Strong hands-on experience with Python and data libraries such as pandas, Polars, or similar
• Advanced SQL skills, including complex joins, window functions, and query optimization
• Proven experience building and operating ETL pipelines using modern data tooling
• Experience with BI and visualization tools (e.g., Grafana, Tableau, Looker)
• Familiarity with workflow orchestration tools such as Airflow, Dagster, or Prefect
• High-level understanding of LLMs and interest in applying them to data and analytics workflows
• Strong ownership mindset and commitment to data quality and reliability

Nice to Have

• Experience with ClickHouse or other analytical databases (e.g., Snowflake, BigQuery, Redshift)
• Background working with vehicle, sensor, or logistics data
• Prior experience in autonomous systems, robotics, or other data-intensive hardware-driven domains

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What You’ll Do

• Own and evolve the metrics platform, including schemas, storage layouts optimized for high-volume writes and fast analytical reads, and clear versioning strategies
• Build and maintain a framework for writing and running metrics, including interfaces, examples, local execution, and CI compatibility checks
• Design and implement testing systems for metrics and pipelines, including unit, contract, and regression tests using synthetic and sampled data
• Operate compute and storage systems in production, with responsibility for monitoring, debugging, stability, and cost awareness
• Partner with metric authors and stakeholders across development, analytics, and QA to plan changes and roll them out safely

What You’ll Need

• Strong experience using Python in production, including asynchronous programming (e.g., asyncio, aiohttp, FastAPI)
• Advanced SQL skills, including complex joins, window functions, CTEs, and query optimization through execution plan analysis
• Solid understanding of data structures and algorithms, with the ability to make informed performance trade-offs
• Experience with databases, especially PostgreSQL (required); experience with ClickHouse is a strong plus
• Understanding of OLTP vs. OLAP trade-offs and how schema and storage decisions affect performance
• Experience with workflow orchestration tools such as Airflow (used today), Prefect, Argo, or Dagster
• Familiarity with data libraries and validation frameworks (NumPy, pandas, Pydantic, or equivalents)
• Experience building web services (FastAPI, Flask, Django, or similar)
• Comfort working with containers and orchestration tools like Docker and Kubernetes
• Experience working with large-scale datasets and data-intensive systems

Nice to Have

• Ability to read and make small changes in C++ code
• Experience building ML-adjacent metrics or evaluation infrastructure
• Familiarity with Parquet and object storage layout/partitioning strategies
• Experience with Kafka or task queues
• Exposure to basic observability practices (logging, metrics, tracing)

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Engineering Manager, Enterprise Security

Fully Remote – USA

$200,000 – $250,000

The Opportunity

My client is expanding their Enterprise Security product line and are looking for a technical leader to build and scale the engineering team behind it. This group develops the security capabilities that enterprise IT leaders rely on to approve, deploy, and trust our platform.

You’ll lead the development of high-impact features such as data loss prevention, audit logging, SIEM integrations, encryption controls, and data governance capabilities. These products are directly tied to large enterprise deals and long-term revenue growth.

This role combines hands-on technical leadership with team building and product ownership. You’ll shape the roadmap, define success metrics, and ensure we deliver enterprise-grade security features that meet the highest standards of reliability and compliance.

What You’ll Own

Technical Direction & Delivery

• Drive architecture and technical strategy for enterprise security products.
• Contribute meaningfully to the codebase while guiding engineering standards and quality.
• Deliver key roadmap milestones, including advanced DLP capabilities and real-time SIEM integrations.
• Ensure security services meet strict uptime, compliance, and audit requirements.

Team Leadership & Growth

• Build and develop a high-performing engineering team focused on enterprise security.
• Establish clear ownership, execution standards, and performance expectations.
• Create clarity in ambiguous spaces and guide the team toward measurable outcomes.

Product & Business Impact

• Partner closely with Product to define and prioritize initiatives based on enterprise customer needs.
• Align engineering efforts with adoption, deal velocity, and revenue impact.
• Help shape Enterprise Security as a distinct and strategic product area within the broader platform.

What You Bring

• Proven experience building enterprise security products for IT administrators and security leaders (e.g., DLP, SIEM integrations, audit systems, access controls, encryption, compliance tooling).
• Strong understanding of enterprise buyers, compliance requirements, and the operational expectations of security products.
• 5+ years of hands-on software engineering experience, ideally in modern web stacks (Node.js/TypeScript, React, or similar).
• 2+ years of experience managing and mentoring engineers, with a history of delivering complex, high-impact systems.
• Comfort operating in high-visibility environments where reliability and trust are non-negotiable.
• Experience leveraging AI tools to accelerate development workflows or a strong interest in adopting them.
• Excellent remote collaboration skills, with experience leading distributed teams through clear documentation and async communication.

Nice to Have

• Experience working with enterprise compliance frameworks such as SOC 2, FedRAMP, or GDPR.
• Familiarity with integrations across enterprise security ecosystems (e.g., SIEM platforms and monitoring tools).
• Background in companies evolving from SMB-focused offerings to enterprise-scale solutions.

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Machine Learning Engineer – GenAI (Remote)

Location: Remote (U.S.)
Compensation: Up to $250K

The Role

We’re looking for a product-minded Machine Learning Engineer to help take GenAI ideas from zero to one. This role is for someone who enjoys ambiguity, thinks in terms of user impact, and knows how to turn early concepts into real, shipped AI products.

You’ll sit at the intersection of ML engineering, product strategy, and execution, owning problems end-to-end – from defining what to build to deploying it in production.

What You’ll Do

• Build, fine-tune, and deploy GenAI, LLM, and RAG-powered features used by real customers.

• Own 0→1 product development, translating fuzzy ideas into shipped ML systems.

• Partner closely with product, design, and engineering to define requirements, success metrics, and tradeoffs.

• Prototype quickly, run experiments, and iterate based on real user feedback.

• Take models from notebook to production, owning pipelines, deployment, and monitoring.

• Help shape product direction by connecting ML capabilities to business and user outcomes.

What You’ll Bring

• 4+ years of experience building and deploying machine learning systems.

• Hands-on experience with GenAI / LLMs (RAG, embeddings, prompt design, fine-tuning).

• Strong product intuition – experience working closely with PMs or owning product decisions yourself.

• Proven experience delivering early-stage or 0→1 products.

• Strong Python skills and experience with modern ML frameworks.

• Excellent communication skills and comfort explaining technical tradeoffs to non-technical partners.

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ML Infrastructure Engineer

Location: Remote (US or UK)

About the Role

We’re looking for an ML Infrastructure Engineer to build and scale production systems for cutting-edge generative AI models. You’ll architect scalable inference pipelines, optimize model deployment, and ensure our 3D and multimodal generation systems run reliably at scale.

What You’ll Do

• Design and deploy high-performance backend systems for serving generative models in production
• Build and optimize GPU-based inference services with focus on latency, throughput, and cost efficiency
• Implement model optimization techniques including quantization, pruning, and distillation
• Develop robust APIs and microservices for model serving using FastAPI, Flask, or gRPC
• Manage cloud infrastructure and CI/CD pipelines for continuous model deployment
• Scale distributed inference systems to handle high-concurrency workloads with request batching
• Collaborate with ML researchers to productionize diffusion models, transformers, and multimodal pipelines

Required Experience

Generative AI Models

• Hands-on experience with diffusion models and transformer-based architectures
• Background in multimodal pipelines combining image and 3D generation
• Familiarity with 3D generation or computer graphics pipelines (meshes, textures, multi-view data)

Production Infrastructure

• Strong track record building backend and infrastructure systems in production environments
• Expert-level Python programming with production-grade API design
• Deep experience deploying and operating ML models at scale, including GPU-based inference services, concurrency handling, request batching, and latency/throughput optimization

ML Deployment Stack

• Proficiency with cloud platforms: AWS (SageMaker, EC2, EKS), GCP, or equivalent
• Experience with containerization (Docker), orchestration, and CI/CD pipelines
• Hands-on work with model optimization frameworks: ONNX Runtime, TensorRT, FSDP, DeepSpeed
• Knowledge of distributed systems and scalable inference frameworks (Ray, Triton, TorchServe)

Nice to Have

• Experience with real-time inference systems or streaming pipelines
• Background in graphics rendering or game engine technologies
• Contributions to open-source ML infrastructure projects
• Understanding of cost optimization strategies for GPU compute

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Machine Learning Engineer – GenAI / Azure

Location: remote (United States)

Compensation: Up to 250K

Unfortunately, this role does not offer sponsorship or can facilitate transfers at this time.

We are seeking a seasoned ML Engineer working on GenAI products with a core focus on end-to-end deployment.

What You’ll Do

• Build, fine-tune, and deploy LLM-powered and Retrieval-Augmented Generation (RAG) systems for production use cases.
• Design and maintain end-to-end ML pipelines covering data ingestion, training, evaluation, deployment, and monitoring.
• Productionize models using Azure-based services (e.g., Azure OpenAI, Azure AI Search, Azure ML, or equivalent tooling).
• Collaborate with product and engineering partners to define requirements, success metrics, and model behaviour in real applications.
• Optimize inference performance, reliability, and cost for large-scale, user-facing AI systems.
• Improve evaluation frameworks, feedback loops, and continuous learning workflows to raise model quality over time.
• Stay current with emerging GenAI techniques and help apply them pragmatically in production environments.

What you’ll Bring

• 5+ years of experience in Machine Learning with a core focus on RAG, LLM, and GenAI solutions
• Experience designing models and deploying them into production
• Extensive experience in Python
• Experience in a variety of ML tools like Huggingface, Pytorch, Tensorflow, OpenAI
• Strong skills with Azure
• Mentorship or leadership experience

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Lead Full Stack Software Engineer C#/.NET & React

4 Days per week onsite in Dallas, TX or Chicago, IL

We are seeking an experienced Lead Full Stack Software Engineer to join industrial real estate client. This is a hands-on technical leadership role where you’ll guide a team of 9 engineers while maintaining significant individual contribution through code review, architecture decisions, and strategic technology initiatives.

About the Role

As Lead Full Stack Software Engineer, you’ll be the technical anchor for our engineering team, ensuring code quality, architectural consistency, and technical excellence across our platform. You’ll balance hands-on technical work with team leadership, owning the full lifecycle of 10+ critical APIs while mentoring engineers and driving adoption of modern development practices.

Key Responsibilities

• Conduct comprehensive code reviews for a team of 9 engineers, ensuring adherence to best practices, design patterns, and architectural standards
• Own and maintain 10+ production APIs that support core business operations
• Lead proof-of-concept initiatives and evaluate new tools, frameworks, and technologies to continuously improve our tech stack
• Design and implement full stack solutions using C#, .NET, React, and Azure services
• Architect scalable solutions within distributed computing environments
• Drive CI/CD pipeline improvements and champion DevOps best practices across the team
• Collaborate closely with product managers, business stakeholders, and cross-functional teams to translate requirements into technical solutions
• Mentor and develop junior and mid-level engineers through pair programming, technical guidance, and career development
• Establish and enforce coding standards, documentation practices, and software development lifecycle (SDLC) processes
• Participate in architecture reviews and provide technical direction for complex engineering challenges

Required Qualifications

• 7+ years of professional software engineering experience with full stack development
• Strong proficiency in C# and .NET framework/core for backend development
• Expert-level experience with React for modern front-end development
• Hands-on experience with Azure cloud services (App Service, Functions, Storage, SQL Database, etc.)
• Deep understanding of distributed computing environments and microservices architecture
• Proven experience with CI/CD processes and tools (Azure DevOps, GitHub Actions, or similar)
• Strong knowledge of RESTful API design, development, and maintenance
• Experience owning and operating APIs in production environments
• Solid understanding of the full Software Development Lifecycle (SDLC)
• Demonstrated ability to conduct thorough code reviews and provide constructive technical feedback
• Excellent written and verbal communication skills with ability to explain complex technical concepts to non-technical stakeholders
• Experience leading technical initiatives and proof-of-concepts

Nice to Have

• Experience in real estate technology, property management systems, or similar domains

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SENIOR BUSINESS ANALYST (RISK ANALYTICS)

HYBRID – GREATER PHOENIX AREA

$115,000 – $130,000 BASE + FULL BENEFITS

The Opportunity

We’re partnering with a large, data-driven residential real estate platform operating at national scale, where analytics directly influence who gets approved, how homes are filled, and how risk is managed.

This is a high-impact senior analyst role, not a reporting-only position. You’ll own analyses that shape resident screening strategy, occupancy outcomes, and credit performance, with regular visibility to executive leadership.

What You’ll Be Working On

• Analyze resident screening, approval, and performance data to balance occupancy growth vs. credit risk
• Identify trends, tradeoffs, and root causes behind key business metrics
• Develop SQL-based analyses pulling from large production data warehouses
• Translate complex data into clear recommendations for senior and executive stakeholders
• Monitor KPIs tied to screening outcomes, resident performance, and operational efficiency
• Partner cross-functionally with Risk, Operations, Revenue, and Data teams
• Improve data quality and integrity by influencing upstream processes and systems

What We’re Looking For

• 5+ years in business analytics, data analytics, or consulting
• Strong SQL skills with experience working in large, messy datasets
• Experience using data to influence policy, pricing, or risk decisions
• Comfort presenting insights and recommendations to senior leadership
• Ability to manage multiple projects in a fast-paced, high-ownership environment
• Curious, problem-solving mindset with strong business intuition

Strong Pluses

• Credit, risk, pricing, or underwriting analysis experience
• Consulting background
• Python for data analysis (pandas, dataframes)
• Experience with statistical analysis or model monitoring
• Prior exposure to executive-level storytelling with data

Tech Stack (Core)

• SQL (complex queries, joins, aggregations)
• Excel (pivot tables, lookups)
• Python for data analysis
• BI tools and cloud-based data warehouses

Why This Role

This role sits at the intersection of data, strategy, and real-world outcomes. Your work directly impacts revenue, risk exposure, and customer experience at national scale. You’ll have autonomy, executive exposure, and the chance to expand beyond one function as the business grows.

If you’re motivated by using data to drive decisions-not just dashboards, APPLY.

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SENIOR SOFTWARE ENGINEER, AI PRODUCT

REMOTE – UNITED STATES

$180,000 – $220,000 BASE + EQUITY + BENEFITS

The Opportunity

We’re working with a collaborative SaaS company embedding AI directly into its core product experience. The focus isn’t demos or prototypes-it’s shipping real AI-powered features that help teams create, collaborate, and make decisions faster.

This role sits at the intersection of product engineering and applied AI, ideal for engineers who enjoy building polished user-facing experiences backed by LLMs and modern cloud infrastructure.

What You’ll Be Working On

• Build and ship AI-powered product features (e.g. chat assistants, summaries, contextual recommendations)
• Integrate LLMs into production systems using modern SDKs and APIs
• Design end-to-end workflows from frontend → backend → AI services
• Collaborate closely with product and design to shape AI-driven UX
• Improve reliability, performance, and iteration speed of AI features
• Contribute to patterns and best practices for AI product development

What We’re Looking For

• 5+ years of experience as a software engineer
• Strong TypeScript / JavaScript background with modern frontend frameworks
• Experience building backend services and APIs (Node.js, serverless, etc.)
• Hands-on experience shipping AI features using LLMs (RAG, agents, copilots, etc.)
• Product-oriented mindset with high ownership
• Comfort working in ambiguity and iterating quickly

Tech Stack (Core)

• TypeScript, React / Next.js
• Node.js, serverless backends
• Cloud infrastructure (AWS or similar)
• LLM platforms (e.g. Bedrock, OpenAI, Anthropic)
• RAG pipelines and AI SDKs

Why This Role

• You’ll help define what great AI product experiences look like at a company investing deeply in AI. High visibility, strong product partnership, and the chance to shape how AI shows up for thousands of users.

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AI / Machine Learning Engineer

Fully Remote

$150-200K plus equity

We’re hiring an AI/ML Engineer to build the intelligence behind a new financial platform. This is a foundational role where you’ll design and deploy models that power real underwriting, cash-flow forecasting, and automated financial decisions used in production.

You’ll work end-to-end from messy transaction data to real-time ML systems, and your work will directly influence how businesses manage and move money.

What You’ll Do

• Build and ship ML models that analyze and predict small-business cash flow.
• Develop underwriting and decisioning systems that drive real financial outcomes.
• Create real-time signals and insights that balance risk, growth, and trust.
• Own the full ML lifecycle: data → modeling → deployment → monitoring.
• Partner closely with product and leadership to shape the AI roadmap.

What We’re Looking For

• Experience shipping ML or data-driven systems into production.
• 2+ years of experience.
• Experience within a start-up environment.
• Comfort working with noisy, real-world financial or transactional data.
• Strong Python and applied ML skills.
• Product-minded, impact-driven, and excited by high-ownership environments.

Why This Role

• Your models will directly control real financial decisions.
• You’ll help define the AI foundation of an early-stage fintech.
• High ownership, fast iteration, and visible impact from day one.

To Apply for this Job Click Here

To Apply for this Job Click Here

Engineering Manager, Enterprise Security

Fully Remote- USA

The Opportunity

My client is expanding their Enterprise Security product line and are looking for a technical leader to build and scale the engineering team behind it. This group develops the security capabilities that enterprise IT leaders rely on to approve, deploy, and trust our platform.

You’ll lead the development of high-impact features such as data loss prevention, audit logging, SIEM integrations, encryption controls, and data governance capabilities. These products are directly tied to large enterprise deals and long-term revenue growth.

This role combines hands-on technical leadership with team building and product ownership. You’ll shape the roadmap, define success metrics, and ensure we deliver enterprise-grade security features that meet the highest standards of reliability and compliance.

What You’ll Own

Technical Direction & Delivery

• Drive architecture and technical strategy for enterprise security products.

• Contribute meaningfully to the codebase while guiding engineering standards and quality.

• Deliver key roadmap milestones, including advanced DLP capabilities and real-time SIEM integrations.

• Ensure security services meet strict uptime, compliance, and audit requirements.

Team Leadership & Growth

• Build and develop a high-performing engineering team focused on enterprise security.

• Establish clear ownership, execution standards, and performance expectations.

• Create clarity in ambiguous spaces and guide the team toward measurable outcomes.

Product & Business Impact

• Partner closely with Product to define and prioritize initiatives based on enterprise customer needs.

• Align engineering efforts with adoption, deal velocity, and revenue impact.

• Help shape Enterprise Security as a distinct and strategic product area within the broader platform.

What You Bring

• Proven experience building enterprise security products for IT administrators and security leaders (e.g., DLP, SIEM integrations, audit systems, access controls, encryption, compliance tooling).

• Strong understanding of enterprise buyers, compliance requirements, and the operational expectations of security products.

• 5+ years of hands-on software engineering experience, ideally in modern web stacks (Node.js/TypeScript, React, or similar).

• 2+ years of experience managing and mentoring engineers, with a history of delivering complex, high-impact systems.

• Comfort operating in high-visibility environments where reliability and trust are non-negotiable.

• Experience leveraging AI tools to accelerate development workflows or a strong interest in adopting them.

• Excellent remote collaboration skills, with experience leading distributed teams through clear documentation and async communication.

Nice to Have

• Experience working with enterprise compliance frameworks such as SOC 2, FedRAMP, or GDPR.

• Familiarity with integrations across enterprise security ecosystems (e.g., SIEM platforms and monitoring tools).

• Background in companies evolving from SMB-focused offerings to enterprise-scale solutions.

To Apply for this Job Click Here

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Senior Business Risk Analyst

Phoenix, AZ Onsite / Hybrid

$90,000 – $120,000 Salary

Overview

We’re partnering with a growing, data-driven organization to hire a Senior Business Risk Analyst who will play a key role in supporting risk-focused analytics and broader business initiatives. This role will initially focus on risk and performance analysis, then expand to support operational and revenue-related projects across the business.

You’ll work closely with cross-functional stakeholders and leadership, using data to influence strategy, optimize outcomes, and improve decision-making.

Key Responsibilities

• Support risk-related analytics, including performance evaluation and policy effectiveness

• Use SQL to extract and analyze data from data warehouses and BI tools

• Perform ad-hoc and deep-dive analyses to identify trends, tradeoffs, and optimization opportunities

• Conduct root cause analysis tied to key performance metrics

• Develop executive-level presentations that clearly communicate insights and recommendations

• Partner with data quality and operations teams to improve data integrity and processes

• Contribute to analytics initiatives across multiple business functions as needed

Required Qualifications

• Bachelor’s degree in a quantitative field (e.g., Economics, Statistics, Engineering, Computer Science) Required

• 5+ years of experience in an analytics or business analysis role

• Strong SQL skills (complex querying required)

• Advanced Excel skills (pivot tables, lookups)

• Python experience for data manipulation and analysis

• Experience translating data insights into business recommendations

• Strong communication skills with experience presenting to senior or executive audiences

• Ability to manage multiple priorities in a fast-paced environment

• Experience with risk, credit, pricing, or policy analysis

• Experience with statistical modeling, regression, or model monitoring

• Exposure to machine learning or hypothesis testing

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