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Remote Automotive Research Engineer Jobs (NOW HIRING)

Waabi

Research Engineer, World Models

Pittsburgh, PA · On-site +1

$155K - $269K/yr

Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. ... As a Research Engineer in the World Models team, you will develop algorithms and productionize the ...

The Pennsylvania State University

Approval of remote and hybrid work is not guaranteed regardless of work location.For additional ... POSITION SPECIFICS We are seeking an experienced Research and Development Engineer to manage ...

Waabi

Research Engineer, Neural Rendering

San Francisco, CA · On-site +1

$134K - $235K/yr

Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. ... To learn more visit: www.waabi.ai As a Research Engineer in Neural Rendering, you will create the ...

Waabi

Research Engineer, Neural Rendering

San Francisco, CA · On-site +1

$134K - $235K/yr

Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. ... To learn more visit: www.waabi.ai As a Research Engineer in Neural Rendering, you will create the ...

Waabi

Research Engineer, Neural Rendering

San Francisco, CA · On-site +1

$134K - $235K/yr

Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. ... To learn more visit: www.waabi.ai As a Research Engineer in Neural Rendering, you will create the ...

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Remote Automotive Research Engineer information

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$37K

$106K

$142.5K

How much do remote automotive research engineer jobs pay per year?

As of Jun 10, 2026, the average yearly pay for remote automotive research engineer in the United States is $106,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,000.00 and $104,000.00 per year, depending on experience, location, and employer.

What is the difference between Remote Automotive Research Engineer vs Remote Automotive Design Engineer?

AspectRemote Automotive Research EngineerRemote Automotive Design Engineer
Required CredentialsBachelor's or Master's in Mechanical, Electrical, or Automotive Engineering; relevant certificationsBachelor's or Master's in Mechanical or Automotive Engineering; CAD certifications
Work EnvironmentResearch labs, R&D departments, testing facilities (remote collaboration)Design studios, CAD software environments (remote design work)
Employer & Industry UsageAutomotive manufacturers, R&D firms, tech companiesAutomotive manufacturers, design consultancies, OEMs
Common Search & ComparisonYesYes

The Remote Automotive Research Engineer focuses on developing new technologies, testing, and analyzing automotive systems remotely. In contrast, the Remote Automotive Design Engineer primarily works on creating vehicle designs and CAD models remotely. Both roles require engineering backgrounds and often overlap in industry usage, but their core responsibilities differ—research versus design.

More about Remote Automotive Research Engineer jobs
What cities are hiring for Remote Automotive Research Engineer jobs? Cities with the most Remote Automotive Research Engineer job openings:
What are the most commonly searched types of Automotive Research Engineer jobs? The most popular types of Automotive Research Engineer jobs are:
What states have the most Remote Automotive Research Engineer jobs? States with the most job openings for Remote Automotive Research Engineer jobs include:
What job categories do people searching Remote Automotive Research Engineer jobs look for? The top searched job categories for Remote Automotive Research Engineer jobs are:
Remote Automotive Research Engineer jobs near you
Infographic showing various Remote Automotive Research Engineer job openings in the United States as of June 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $106,012 per year, or $51 per hour.

Research Engineer, Frontier Capabilities

Lila Sciences

Cambridge, MA • On-site, Remote

Other

Posted yesterday

Job description

Your Impact at LILA

The AI Research team is tackling one of the most exciting, open problems in AI: training LLMs to run long-horizon scientific discovery tasks. Our approach spans the full post-training stack - from SFT to asynchronous RL on agentic harnesses - teaching models to plan, use tools, and learn from experience in domains where the ground truth isn't a preference label, but a scientific result.

We're rapidly growing our Research Engineering org and seeking talented engineers and ML practitioners across levels to design, build, and optimize systems to push this frontier: scaling post-training, sharpening reasoning, and unlocking compute-intensive agentic-harness training. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems.

We operate with high agency, and a bias toward execution. Below are several focus areas within the team. We ask that candidates select the stream that best matches their experience and excitement.

Work Streams

Stream A: GPU Optimization & Training Performance

Maximize hardware utilization across 100B+ parameter asynchronous RL training runs. Responsibilities include profiling, performance optimization, custom kernel development, communication-computation overlap, and long-context throughput improvements. You set and maintain the performance baseline.

Stream B: Stack & Infrastructure

Own the post-training infrastructure end-to-end - supervised fine-tuning, asynchronous RL with tool integration, and data pipelines. Build modular, reproducible workflows with single-command execution. Manage upstream framework upgrades and deliver composable pipelines spanning Data, SFT, and RL stages. You work tightly with Research Scientists to develop and productionize novel algorithms to run at scale.

Stream C: Model Experimentation

Bring deep, hands-on experience training large language models. Lead experimentation on reasoning model development, including mixture-of-experts stabilization, curriculum design, and synthetic reasoning trace generation. You have a bias toward experimental design and tracking, and know how to prioritize runs that yield promising outcomes.

Stream D: Evaluations & Benchmarks

Design and build best-in-class scientific agentic benchmarks and harnesses, along with the dashboards and leaderboards that inform every training decision. You have experience working with well known public benchmarks and have spent time building bespoke agentic benchmarks and harnesses.

Stream E: Agentic Capabilities & Frontier Research

Train models capable of planning, exploration, and tool use over extended horizons. Advance the state of the art in RL at scale with tool-calling, subgoal decomposition, and shared memory/skills across trials to expand the frontier of scientific agent capabilities.

What You'll Need to Succeed

  • Strong software engineering skills in Python; C++/CUDA a plus
  • Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray)
  • Understanding of large-scale model training techniques for 100B+ models
  • Experience with cloud or HPC environment
  • Ability to communicate technical results to internal and external stakeholders

Bonus Points For

  • Prior work with large scale scientific datasets or domain-specific modeling
  • Contributions to open-source ML frameworks
  • Experience with RL post-training (RLHF, GRPO, tool-augmented RL)
  • Experience training MoE architectures

Location

San Francisco, CA or Cambridge, MA (Remote, Hybrid, and On-Site available depending on team needs).

Apply