Deep Learning Engineer job description template
A deep learning engineer working in vision designs, trains and improves neural networks — architecture selection, training strategy, loss design and large-scale experimentation. The role leans further toward modelling than a general computer vision engineer and further toward engineering than a research scientist.
Before you post
Be honest about the split between research and application, and about your compute. Overselling research freedom is the most common cause of early attrition in this role. If your strongest asset is a distinctive dataset rather than a GPU budget, lead with that — it is a better filter and a better pitch.
The template
About the role
We are hiring a deep learning engineer to develop the models behind [product]. You will own model quality — architecture, training strategy and evaluation — for [specific capability].
What you will do
- Design, train and improve deep learning models for [task]
- Run controlled experiments and ablations to guide modelling decisions
- Build and maintain evaluation that reflects real product requirements
- Adapt current research to our data and constraints
- Work with engineering to bring models into production
What we are looking for
- Deep PyTorch experience including custom training loops and losses
- Strong grasp of modern vision architectures
- Disciplined experimental methodology
- Ability to read and implement current literature
Keep this list to three to five items. Long mandatory lists disproportionately deter the candidates you most want.
Nice to have
- Experience with [your specific problem area]
- Distributed or large-scale training experience
- Self-supervised or transfer learning for limited-label domains
- Publication record in a relevant venue
Plain text version
Select all and paste into your ATS, then replace everything in brackets.
DEEP LEARNING ENGINEER ABOUT THE ROLE We are hiring a deep learning engineer to develop the models behind [product]. You will own model quality — architecture, training strategy and evaluation — for [specific capability]. WHAT YOU WILL DO - Design, train and improve deep learning models for [task] - Run controlled experiments and ablations to guide modelling decisions - Build and maintain evaluation that reflects real product requirements - Adapt current research to our data and constraints - Work with engineering to bring models into production WHAT WE ARE LOOKING FOR - Deep PyTorch experience including custom training loops and losses - Strong grasp of modern vision architectures - Disciplined experimental methodology - Ability to read and implement current literature NICE TO HAVE - Experience with [your specific problem area] - Distributed or large-scale training experience - Self-supervised or transfer learning for limited-label domains - Publication record in a relevant venue ABOUT US [Two or three sentences on the company, the product, and why the problem matters.] DETAILS - Location: [city / hybrid / remote] - Salary: [range] - Apply: [link]
Calibrating the level
Adjust the requirements to match the level you are actually hiring for. Asking for senior capability at a mid-level budget is the most common cause of a stalled search.
Junior
Runs experiments and implements published methods against a defined objective.
Mid
Owns a model family and its improvement, with disciplined ablation practice.
Senior
Sets modelling strategy and knows which literature is worth pursuing.
Staff / Principal
Directs modelling across the organisation and makes build-versus-adopt calls on foundation models.
Post it where the right people are
JobsInVision is a computer vision job board — your listing reaches engineers who work in this field specifically, rather than a general software audience.