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Embedded Ai Engineer Jobs in Rhode Island (NOW HIRING)

C++ Tutor

Providence, RI · Remote

$18 - $40/hr

Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ... programming to operating systems, embedded systems, and high-performance computing applications.

Electronics Tutor

Providence, RI · Remote

$18 - $40/hr

Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ... engineering coursework and electronics industry careers. * Conceptual Teaching & Problem-Solving:

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Embedded Ai Engineer information

See Rhode Island salary details

$68.6K

$150.2K

$170.4K

How much do embedded ai engineer jobs pay per year?

As of Aug 20, 2026, the average yearly pay for embedded ai engineer in Rhode Island is $150,210.00, according to ZipRecruiter salary data. Most workers in this role earn between $128,800.00 and $169,400.00 per year, depending on experience, location, and employer.

What is an embedded AI engineer?

An Embedded AI Engineer is a professional who designs, develops, and implements artificial intelligence (AI) algorithms and models directly onto embedded systems, such as microcontrollers or edge devices. Their work involves optimizing AI solutions to run efficiently on hardware with limited computing resources, power, and memory. They collaborate with hardware engineers and software developers to integrate machine learning, computer vision, or other AI functionalities into products like smart appliances, autonomous vehicles, or IoT devices. Their expertise helps bring intelligent features directly to devices, enabling real-time decision-making without needing constant cloud connectivity.

What are the key skills and qualifications needed to thrive as an embedded AI engineer?

To thrive as an Embedded AI Engineer, you need expertise in embedded systems, AI/ML algorithms, programming languages like C/C++ and Python, and typically a degree in computer engineering or a related field. Familiarity with development tools such as TensorFlow Lite, ONNX, embedded Linux, and microcontroller platforms is essential, along with experience deploying AI models on resource-constrained devices. Strong problem-solving, collaboration, and communication skills help you work effectively in multidisciplinary teams and address real-world challenges. These skills ensure efficient integration of AI into embedded systems, enabling innovative, high-performance solutions for edge computing.

How does an embedded AI engineer typically collaborate with hardware and software teams during a project?

Embedded AI Engineers work closely with both hardware and software teams to ensure AI models are efficiently integrated into resource-constrained devices. They often collaborate with hardware engineers to optimize model performance based on device limitations like memory and processing power. At the same time, they coordinate with software developers to design efficient firmware and manage data pipelines. Regular cross-functional meetings and code reviews are common to address integration challenges and maintain alignment throughout the project lifecycle.

What is the difference between Embedded Ai Engineer vs Machine Learning Engineer?

CriteriaEmbedded Ai EngineerMachine Learning Engineer
Required CredentialsBachelor's in Electrical Engineering, Computer Science, or related; knowledge of embedded systemsBachelor's or Master's in Computer Science, Data Science, or related; strong programming skills
Work EnvironmentEmbedded systems, IoT devices, hardware integrationData centers, cloud platforms, software development environments
Employer & Industry UsageConsumer electronics, automotive, IoT companiesTech firms, startups, research institutions
Common Search & ComparisonYesNo

Embedded Ai Engineers focus on integrating AI algorithms into embedded hardware and IoT devices, requiring knowledge of hardware constraints and embedded programming. Machine Learning Engineers develop models primarily for software applications and data analysis. While both roles involve AI, Embedded Ai Engineers specialize in hardware-software integration within embedded systems, whereas Machine Learning Engineers work on developing and deploying AI models in software environments.

What are popular job titles related to Embedded Ai Engineer jobs in Rhode Island?

For Embedded Ai Engineer jobs in Rhode Island, the most frequently searched job titles are:

What job categories do people searching Embedded Ai Engineer jobs in Rhode Island look for?

The top searched job categories for Embedded Ai Engineer jobs in Rhode Island are:

What cities in Rhode Island are hiring for Embedded Ai Engineer jobs?

Cities in Rhode Island with the most Embedded Ai Engineer job openings:

Database Internals Engineer

Teradata Corporation (SE)

Providence, RI • On-site

$101.60 - $152.50/hr

Other

Medical, Life, Retirement

Posted 2 days ago

New


Job description

Database Internals Engineer — Next‑Gen Parallel Compute Engine

At Teradata, we believe that people thrive when empowered with better information. Teradata's Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge, and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

About the Role

Join the team that built the original Teradata engine, and work on what comes next. Inside the Office of the CTO, our Advanced Research Team is exploring a new generation of massively parallel, decentralized compute — engine architecture where parallelism is a first principle rather than a later refinement.

That choice shapes everything that follows: the structure of plans, the expression of operators, the organization of storage, and the movement of work and data across nodes. It is the kind of systems problem that can define a career, and we are approaching it with both ambition and rigor.

This is a rare opportunity to work at the frontier of database internals, learn from the people who helped define the field, and leave your mark on a new engine as it takes shape. If you want greenfield systems work backed by the people, resources, and a problem set only a company like Teradata can offer, this is the place.

What You'll Do
  • Build components across the engine spine: SQL front end (tokenizer, parser, binder), logical/physical plan layer, rule‑and cost‑based optimizer, and operators (joins, aggregates, sort, scan).
  • Build the storage substrate: Arrow in‑memory format, slotted‑page on‑disk format with checksums, buffer pool, and a B‑tree or LSM access method.
  • Implement transactions and recovery: lock/latch management, MVCC/snapshot isolation, WAL/ARIES, checkpoints, and crash recovery.
  • Add parallelism and distribution along the correct axis — exchange‑based parallel execution for query work, consensus/replication/atomic‑commit for data correctness — without conflating the two.
  • Write design specifications before coding (schemas; null/empty/duplicate semantics; memory budget and spill behavior; cost characteristics), write tests first, implement behind the established operator interface, verify safety then speed, and report the benchmark delta.
Who You Will Work With

You will work within an intentionally small Advanced Research Team inside Teradata's Office of the CTO — meaning your contributions directly shape architecture and direction rather than passing through layers of process. You will collaborate with:

  • The architects and engineers behind one of the most successful massively parallel databases ever shipped, who have faced the hardest problems in this space and are now focused on what comes next.
  • Senior experts who are available to challenge your thinking, share lessons learned from building the first generation, and provide mentorship on deep systems problems.
  • Peers who share a commitment to rigor, quality, and first‑principles engineering — a small team where everyone's work matters and is visible.
What Makes You a Qualified Candidate

We are open to two complementary profiles for this role:

Profile A — Distributed Query Optimizer
  • Deep expertise in distributed query optimization: cascading optimizers, Abstract Syntax Tree (AST) binding, logical and physical plan distribution.
  • Experience designing parallel execution pipelines where distribution is a first‑class concern, not an afterthought.
  • Familiarity with systems like Apache DataFusion or similar distributed execution frameworks.
Profile B — Execution Engine & Database Internals
  • Strong hands‑on background with analytical/embedded engines (e.g., DuckDB, DataFusion) and their internals.
  • Deep knowledge of pipeline execution, vectorized processing, file system I/O, and open table formats (Iceberg, Delta).
  • Experience with transaction management, lock managers, cache management, or OS‑level scheduling.
What You'll Bring Technical Requirements
  • Systems programming in Rust or modern C/C++ with memory‑safety discipline.
  • Relational foundations end to end: relational algebra and the algebraic laws behind query rewrites, logical→physical lowering, and cost‑based optimization. You can walk the full lifecycle — text → tokens → AST → bound logical plan → optimized plan → physical plan → batches — and reason about cost at every step.
  • Demonstrated depth in at least one area of the engine spine (front end, plan/optimizer, operators, storage engine, transactions/recovery, or scale‑out) with working literacy across its neighbors.
  • Vectorized execution over a columnar (Arrow) representation: batch‑at‑a‑time operators, the pull/iterator model, validity‑bitmap‑correct null handling, zero‑copy buffer sharing.
  • Cost reasoning at the metal: cache behavior, alignment, SIMD, allocation patterns, false sharing in parallel accumulators — you can articulate why one physical implementation beats another on a given CPU.
  • Test‑and benchmark‑gated engineering: golden/sqllogictest, property and fuzz testing, deterministic simulation testing for concurrent/distributed paths, and microbenchmarks (Criterion/Google Benchmark) plus a TPC‑H subset. Correctness gates before performance, always.
  • Sophisticated, hands‑on use of AI coding agents directed against a reference‑grade spec — managed as a virtual engineering team, held to the same bar as the rest of the work.
Soft Skills
  • Communicates clearly — can describe a system or problem at the right level of abstraction for any audience, whether a senior architect, a peer, or an AI coding agent.
  • Doer over theorist — moves from concept to working prototype quickly; validates by building.
  • Intellectually curious and self‑directed — digs deep independently before asking for help; brings answers and proposals.
  • Collaborative with peers — receptive to input and direction while owning your area end‑to‑end.
  • Comfortable with evolving requirements — writes design documents to create clarity.
  • High personal bar for quality.
Why We Think You Will Love Teradata

Teradata's architecture has run the world's largest data workloads for over 45 years — because it was engineered right. We are applying that same discipline to the next generation of compute. This is a rare chance to work on something foundational, collaborating directly with the people who did it the first time.

We prioritize a people‑first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well‑being because we care about our people and their ability to thrive both personally and professionally. We are an anti‑racist company because our dedication to Diversity, Equity, and Inclusion is more than a statement — it is a deep commitment to doing the work to foster an equitable environment that celebrates people for all of who they are.

Equal Opportunity Statement

Teradata is proud to be an equal opportunity employer. We do not discriminate based upon race, color, ancestry, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related conditions), national origin, sexual orientation, age, citizenship, marital status, disability, medical condition, genetic information, gender identity or expression, military and veteran status, or any other legally protected status. We welcome and encourage individuals from all backgrounds to apply and join our team, bringing their unique perspectives and experiences to help us innovate and grow. If you require accommodations during the interview process, please let your recruiter know and we will work with you to meet your needs.

Compensation and Benefits

Pay Rate: 101,600.00 - 127,000.00 - 152,500.00 Annually. Starting pay for the successful applicant will depend on geographic location, internal equity, job‑related knowledge, skills, and candidate experience. All non‑sales roles are eligible for one of our annual incentive plans, which are based on company financial attainment and individual performance. Employees in this position are eligible to participate in the Company’s benefits programs, which generally include health care, life and disability insurance, retirement savings, and time‑off programs. Benefit details vary by country and plan, and will be shared during the hiring process. Additional information is available here: https://www.teradata.com/About-Us/Careers/Benefits

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