Founding Ai Engineer

Founding Ai Engineer Jobs

4 jobs found

Today

Founding Machine Learning Engineer, Computer Vision (Object Detection)

Clera New York, NY

New York, NY

Today

$200k - $260k/yr Full time Artificial Intelligence Data Science Founding Ai Engineer

Apply Now

Job Description

Founding Machine Learning Engineer, Computer Vision to set the technical direction for fine-grained object detection, calibrated uncertainty, and production serving.

Responsibilities

Design and own the computer vision architecture for fine-grained identification, including adapting foundation vision models to a specific catalog
Define accuracy standards by category and implement calibrated confidence scores so the product expresses uncertainty rather than guessing
Partner with labeling teams to convert model errors into prioritized improvements for future data collection
Manage tradeoffs between expanding to new categories and improving accuracy for existing ones
Own the end-to-end path from model research to production serving, including focus on latency, cost, and reliability
Communicate model capabilities and limitations to both technical and non-technical stakeholders

Requirements

5+ years of applied computer vision experience, including production systems shipped at meaningful scale
Industry experience in image or item identification, object recognition, or detection, including fine-grained or instance-level classification where closely related items must be distinguished accurately
Hands-on experience fine-tuning and deploying vision transformers or CNNs using PyTorch or TensorFlow
Experience adapting foundation vision models, building evaluation frameworks beyond loss metrics, and developing calibrated confidence scoring
Experience owning production model serving decisions, including latency, cost, and reliability (familiarity with low-latency APIs is useful)
Strong communication skills and the ability to lead technical decisions in an open-ended problem space
Plus: experience with active learning, human-in-the-loop labeling, visual search, marketplaces, or an early-stage startup

Technologies

PyTorch
TensorFlow
Vision transformers
CNNs

Compensation & Benefits

Base salary: USD 200,000 - 260,000 per year
Visa sponsorship: not available

Location

Hybrid (New York, NY, United States)

View full job page →

1d ago

Founding Applied AI Engineer

Clera San Francisco, CA

San Francisco, CA

1d ago

$180k - $230k/yr Full time Agentic Ai Engineering Applied Ai Engineer Artificial Intelligence

Apply Now

Job Description

Clera is hiring a Founding Applied AI Engineer in San Francisco to lead quality, evaluation, and learning for multi-channel marketing agents.

Responsibilities

Raise the bar on agent output and content quality for social platforms and article writing by building and using evaluation systems to measure outcomes.
Improve SEO and generative engine optimization performance for agent-generated content.
Design feedback loops that let agents learn from real-world results and performance signals.
Own and evolve production AI systems, including agent architecture, model strategy, and supporting data pipelines.
Support AI team growth by helping hire and leading the team as it scales.

Requirements

3+ years building production machine learning or AI systems, with recent hands-on experience shipping LLM products or agents.
Experience designing evaluations and using outcome signals at scale in search, ranking, recommendations, or related domains.
Strong TypeScript skills, including Next.js, plus Python for data analysis, evaluations, and model work; demonstrated end-to-end shipping experience.
Production experience with applied AI, including areas such as LLM reasoning or post-training, generative AI, agentic systems, search, recommendation systems, algorithms, or machine learning research.
Proven ability to build and improve feedback loops and production AI infrastructure, agent architecture, or model strategy.
Experience owning and shipping work in a startup or small team, along with strong research or applied AI achievements.
Search, ranking, or AI answer engine experience is valuable.
Marketing or go-to-market experience is a plus; recommendation systems, algorithms, and reinforcement learning are relevant.

Technologies

TypeScript
Next.js
Python

Compensation & Benefits

Annual base compensation: $180,000 - $230,000 USD.
Visa sponsorship available.

Location

On-site in San Francisco, California.
Candidates must be based in or willing to relocate to San Francisco.

View full job page →

1mo ago

Founding Developer Relations Engineer

Goaly Palo Alto, CA

Palo Alto, CA

1mo ago Closed

Full time Ai Developer Tooling Deep Learning Developer Relations

This position is no longer accepting applications (closed Nov 3, 2026).

Job Description

Goaly is coming out of stealth, and the company needs a technical voice who can make post-training performance tangible for the people running it day to day. As the first Developer Relations hire, you will build the benchmarks, tooling, and documentation that practitioners rely on, then distribute those artifacts across the channels where ML engineers actually learn and share.

What you will do

Build the signature technical artifacts, including a public post-training efficiency benchmark, reproducible before/after case studies (including doomed-run kills, silent throughput regressions, and dollars per eval point), and the open-source tools released around them.
Write conversion-focused docs such as quickstarts, reference documentation, and example integrations for the stacks users run, including PyTorch distributed, Megatron, FSDP, TorchTitan, Ray, Slurm, Kubernetes on GPUs, Weights & Biases (W&B), and MLflow. The goal is to help an engineer generate a read-only Retroactive Kill Report on their own cluster without needing Goaly’s involvement.
Create practitioner-ready technical content, including deep-dive engineering posts, benchmark write-ups, failure-mode teardowns (for example, “seven ways an RL run dies silently”), short demo videos, and live walkthroughs.
Own distribution by running Goaly’s technical presence across X, LinkedIn, GitHub, Hacker News, and the ML-infra Discord and Slack communities, plus deliver talks at venues users attend such as PyTorch Conference, MLSys, Ray Summit, GTC, and NeurIPS and ICML workshops, along with Bay Area AI infra meetups.
Act as a feedback loop: sit with design partners, reproduce their problems on Goaly’s stack, and deliver precise, prioritized engineering feedback. Convert repeated questions into docs, tools, or product changes.
Build community around the problem, not the product, through practitioner conversations, office hours, and a newsletter or series focused on learning about post-training efficiency.
Amplify the founders by turning the founding team’s Llama post-training infrastructure experience into content and talks, coordinating with the GTM team to strengthen both the design-partner pipeline and candidate pipeline.
Measure and improve by tracking reach, repo adoption, the docs funnel, inbound design-partner requests, and hires sourced. Double down on what works and stop what does not.

How the work is split

About half of the week is building: running real post-training workloads on Goaly’s stack, creating benchmark harnesses, building example integrations, and writing docs that take someone from zero to a first result in about thirty minutes.
About half of the week is distribution and relationship building: engineering posts, talks, open-source releases, and one-on-one conversations that lead to design partners and hires.

Who you are

3+ years as a software or ML engineer, with the expectation that you still write code every week.
A strong engineering background: you can stand up a distributed training job, read a profiler trace, and debug a CUDA OOM without needing help.
Hands-on experience with LLM post-training and/or the infrastructure underneath it, including SFT, RL, LoRA, DPO, or the stack beneath them such as PyTorch distributed, Megatron, DeepSpeed, FSDP, Ray, Slurm, and GPU Kubernetes.
A track record of explaining difficult topics clearly through blog posts, docs, conference talks, OSS READMEs, tutorials, or videos (links are expected).
Distribution you can point to, such as an audience on X, LinkedIn, YouTube, a newsletter, or GitHub, plus talks given, communities where people know your name, or evidence of building reach from zero in ML infrastructure and post-training.
Two-way communication: write content practitioners trust, and also listen to a frustrated infra engineer for an hour and return with the three engineering fixes that matter most.
A taste for substance: you would rather publish one reproducible benchmark than multiple hot takes.
Comfort being the public face of something early, even without a playbook, with messaging that shifts as the product evolves.

Technical stack you will work with

PyTorch distributed, Megatron, FSDP, TorchTitan, Ray, Slurm, Kubernetes on GPUs, Weights & Biases (W&B), MLflow
SFT, RL, LoRA, DPO, DeepSpeed
CUDA OOM, W&B, Nsight, PyTorch Profiler

Location and logistics

Palo Alto, CA (hybrid): 4+ days/week in office, with roughly 20% travel for conferences and customer visits.
Visa sponsorship: H-1B and OPT/CPT support available, with immigration counsel.
Perks: complimentary lunch, dinner, snacks, and drinks.

About the role, in practice

Goaly’s users are ML infra engineers and researchers who operate SFT, RL, and fine-tuning jobs on GPU clusters. They do not read marketing; they read benchmarks, repos, and documentation, and they rely on the engineer behind the artifacts. This role reports to the founders, works daily with engineering and the go-to-market team, and is explicitly founding-stage: you help shape the playbook while keeping output credible to people who train models.

How Goaly works

Mission first: choose work for impact and take responsibility for outcomes.
High agency: identify what is missing, form a plan, and move without waiting for perfect clarity.
Speed with rigor: ship, measure, and iterate while protecting correctness, safety, and reliability.
Flexible scope: cross team boundaries when it is the fastest path to the real problem.
Low ego, high standards: give direct feedback and adjust when evidence changes.
Continuous learning: the stack changes quickly, and you are willing to learn unfamiliar systems and domains.

View full job page →

1mo ago

Founding AI Engineer

Futureproofing Boston, MA

Boston, MA

1mo ago Closed

Full time Backend Developer Agentic Ai Artificial Intelligence

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

Futureproofing is building an end-to-end AI platform for investment banking, and the near-complete MVP is ready for an expert to help it become a product real customers can rely on. This role is centered on production-grade AI engineering, where core product capabilities are built with LLMs, agents, RAG, and evaluation rather than added on after the fact.

As the founding AI engineer in Boston, MA (onsite), you will work closely with the founding team to define what gets built and why, then take ownership of the system areas that determine whether the platform performs reliably in the real world.

Responsibilities

Take the product from MVP to something real customers rely on
Build with LLMs, agents, RAG, and evaluation as core product surface areas rather than bolt-ons
Partner directly with the founding team on scope and priorities, including rationale for engineering decisions
Own high-impact parts of the system, including the data pipeline, infrastructure, and model integration
Move quickly alongside engineers who already know how to ship

Requirements

7+ years shipping production software, including meaningful experience at an early-stage company
Built at least one AI-powered product from nothing to production users, with the ability to explain key tradeoffs made during delivery
Comfort across modern stacks, typically TypeScript/React/Node and/or Python, with experience spanning full-stack work into infrastructure
Uses AI tooling daily as part of how you actually build
Comfort with ambiguity and with being the person who decides
Can communicate technical tradeoffs clearly to non-engineers without losing context for either side
Founding engineer experience is a plus, noting the role does not include the traditional founding-engineer responsibility of building and owning the whole system solo

Technologies

LLMs, agents, RAG, evaluation
TypeScript, React, Node, Python

What Makes This Opportunity Different

The product is category-defining
Early stage, but not too early: the MVP is nearly built, so you join with something real in hand
Build next to people who know the domain cold
High-autonomy team

View full job page →

Founding Ai Engineer Jobs

Founding Machine Learning Engineer, Computer Vision (Object Detection)

Founding Applied AI Engineer

Founding Developer Relations Engineer

Founding AI Engineer

Get Founding Ai Engineer Jobs alerts